U.S. patent application number 16/706249 was filed with the patent office on 2020-04-09 for systems and methods for data collection and frequency evaluation for a vehicle steering system.
The applicant listed for this patent is Strong Force IoT Portfolio 2016, LLC. Invention is credited to Charles Howard Cella, Mehul Desai, Gerald William Duffy, JR., Jeffrey P. McGuckin.
Application Number | 20200110401 16/706249 |
Document ID | / |
Family ID | 63669288 |
Filed Date | 2020-04-09 |
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United States Patent
Application |
20200110401 |
Kind Code |
A1 |
Cella; Charles Howard ; et
al. |
April 9, 2020 |
SYSTEMS AND METHODS FOR DATA COLLECTION AND FREQUENCY EVALUATION
FOR A VEHICLE STEERING SYSTEM
Abstract
Systems and methods for data collection and frequency evaluation
for a vehicle steering system are disclosed. An example monitoring
system for data collection in a vehicle steering system may include
a vehicle steering system comprising a rack, a pinion, and a
steering column; a data acquisition circuit to interpret a
plurality of detection values corresponding to a plurality of input
sensors, each input sensors operationally coupled to the vehicle;
and a data storage circuit to store one or more operating
frequencies of the vehicle. The example system may further include
a frequency evaluation circuit to detect an operating signal,
wherein the operating signal comprises a frequency higher than the
one or more operating frequencies; and a response circuit
structured to perform at least one operation in response to the
detected operating signal.
Inventors: |
Cella; Charles Howard;
(Pembroke, MA) ; Duffy, JR.; Gerald William;
(Philadelphia, PA) ; McGuckin; Jeffrey P.;
(Philadelphia, PA) ; Desai; Mehul; (Oak Brook,
IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Strong Force IoT Portfolio 2016, LLC |
Santa Monica |
CA |
US |
|
|
Family ID: |
63669288 |
Appl. No.: |
16/706249 |
Filed: |
December 6, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16143347 |
Sep 26, 2018 |
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16706249 |
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15973406 |
May 7, 2018 |
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16143347 |
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PCT/US17/31721 |
May 9, 2017 |
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15973406 |
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PCT/US18/45036 |
Aug 2, 2018 |
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16143347 |
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15973406 |
May 7, 2018 |
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PCT/US18/45036 |
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62333589 |
May 9, 2016 |
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62350672 |
Jun 15, 2016 |
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62412843 |
Oct 26, 2016 |
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62427141 |
Nov 28, 2016 |
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62540557 |
Aug 2, 2017 |
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62562487 |
Sep 24, 2017 |
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62583487 |
Nov 8, 2017 |
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62540557 |
Aug 2, 2017 |
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62540513 |
Aug 2, 2017 |
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62562487 |
Sep 24, 2017 |
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62583487 |
Nov 8, 2017 |
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62583487 |
Nov 8, 2017 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G05B 23/0291 20130101;
H04L 67/12 20130101; G06N 3/0454 20130101; G05B 19/4183 20130101;
G05B 23/0221 20130101; H04B 17/40 20150115; H04B 17/23 20150115;
H04B 17/26 20150115; G05B 19/4185 20130101; G05B 23/0264 20130101;
G06N 7/005 20130101; H04L 1/1874 20130101; H04W 4/70 20180201; H04L
1/0041 20130101; H04L 5/0064 20130101; G05B 23/0229 20130101; G06N
20/00 20190101; G05B 2219/37434 20130101; G06N 3/0472 20130101;
G06N 5/046 20130101; H04L 67/1097 20130101; G05B 19/042 20130101;
G05B 23/0289 20130101; G06N 3/088 20130101; Y02P 90/02 20151101;
G06N 3/02 20130101; G05B 19/4184 20130101; G05B 23/024 20130101;
G06Q 30/06 20130101; G05B 2219/35001 20130101; H04L 1/18 20130101;
H04L 1/0009 20130101; G06N 3/126 20130101; G05B 13/028 20130101;
Y02P 90/80 20151101; G05B 23/0294 20130101; G05B 19/41865 20130101;
H04B 17/29 20150115; G06K 9/6263 20130101; G05B 23/0297 20130101;
H04W 4/38 20180201; G05B 23/0286 20130101; H04B 17/318 20150115;
G05B 2219/37351 20130101; G05B 2219/32287 20130101; G06N 3/006
20130101; H04B 17/309 20150115; H04L 67/306 20130101; G05B 19/41845
20130101; G05B 2219/45129 20130101; Y02P 80/10 20151101; G05B
19/41875 20130101; G05B 2219/37337 20130101; G05B 2219/40115
20130101; G06N 3/084 20130101; G06Q 30/02 20130101; H04B 17/345
20150115; H04L 1/0002 20130101; G05B 23/0283 20130101; G06N 3/0445
20130101; G05B 2219/45004 20130101 |
International
Class: |
G05B 23/02 20060101
G05B023/02; G06N 3/08 20060101 G06N003/08; G06N 3/04 20060101
G06N003/04; G06N 3/00 20060101 G06N003/00; G05B 19/418 20060101
G05B019/418; H04L 29/08 20060101 H04L029/08; G06N 20/00 20060101
G06N020/00; H04L 1/00 20060101 H04L001/00; G05B 13/02 20060101
G05B013/02; H04B 17/318 20060101 H04B017/318; G06N 3/02 20060101
G06N003/02; G06N 7/00 20060101 G06N007/00; G06K 9/62 20060101
G06K009/62; G06N 5/04 20060101 G06N005/04; H04B 17/309 20060101
H04B017/309; H04L 1/18 20060101 H04L001/18 |
Claims
1. A monitoring system for data collection in a vehicle steering
system, the monitoring system comprising: a vehicle steering system
comprising a rack, a pinion, and a steering column; a data
acquisition circuit structured to interpret a plurality of
detection values, each of the plurality of detection values
corresponding to at least one of a plurality of input sensors, each
of the plurality of input sensors operationally coupled to the
rack, the pinion, or the steering column, and communicatively
coupled to the data acquisition circuit; a data storage circuit
structured to store one or more operating frequencies of the rack,
the pinion, or the steering column; and a frequency evaluation
circuit structured to detect an operating signal in response to the
plurality of detection values, wherein the operating signal
comprises a frequency higher than the one or more operating
frequencies; and a response circuit structured to perform at least
one operation in response to the detected operating signal.
2. The monitoring system of claim 1, wherein the at least one
operation comprises performing at least one of: adjusting a
sampling rate of at least one of the plurality of detection values;
requesting a maintenance event; changing a utilized input sensor
corresponding to at least one of the plurality of input sensors;
storing event data related to the vehicle steering system and the
operating signal; and providing one of an alert or a notification
in response to the operating signal.
3. The monitoring system of claim 1, wherein the frequency
evaluation circuit is further structured to detect a misalignment
in response to the operating signal indicating a change in energy
at frequencies at least twice a frequency of at least one of the
one or more operating frequencies.
4. The monitoring system of claim 1, wherein the operating signal
indicates an anomalous condition.
5. The monitoring system of claim 4, wherein the anomalous
condition comprises a pre-failure mode condition for the vehicle
steering system.
6. The monitoring system of claim 1, further comprising a data
analysis circuit structured to analyze at least two of the
plurality of detection values, to determine a relative phase value
between the at least two of the plurality of detection values, and
to detect an anomalous condition in response to the relative phase
value.
7. The monitoring system of claim 6, wherein the vehicle steering
system comprises one or more rotating components, and wherein the
data analysis circuit is further structured to perform band-pass
tracking associated with the one or more rotating components to
detect the anomalous condition.
8. The monitoring system of claim 1, wherein at least one of the
plurality of input sensors comprises a vibration sensor, and
wherein the frequency evaluation circuit is further structured to
detect a noise pattern from the vehicle steering system in response
to detection values from the vibration sensor.
9. The monitoring system of claim 8, wherein the frequency
evaluation circuit is further structured to detect the noise
pattern at frequencies higher than a frequency at which one or more
rotating components of the vehicle steering system rotates.
10. The monitoring system of claim 7, wherein detecting the
anomalous condition comprises performing a frequency analysis at a
selected multiple of a rotational speed of at least one of the one
or more rotating components.
11. The monitoring system of claim 1, wherein the frequency
evaluation circuit is further structured to perform a frequency
analysis at a selected multiple of at least one of the one or more
operating frequencies.
12. A method for collecting data in a vehicle steering system, the
method comprising: collecting data from a plurality of input
channels, wherein a subset of the plurality of input channels is
communicatively coupled to sensors measuring operational parameters
of a rack, a pinion, and a steering column of the vehicle steering
system; storing one or more operating frequencies related to an
operation of at least one of the rack, the pinion, or the steering
column; interpreting a plurality of detection values from the
collected data, each of the plurality of detection values
corresponding to at least one of the plurality of input channels;
and detecting an operating signal on one of the plurality of input
channels at a frequency higher than at least one of the one or more
operating frequencies.
13. The method of claim 12, further comprising analyzing at least
two of the plurality of input channels, and wherein detecting the
operating signal comprises detecting an anomalous condition in
response to a relative phase difference between the at least two of
the plurality of input channels.
14. The method of claim 13, further comprising initiating an action
on the vehicle steering system based on the detection of the
operating signal.
15. The method of claim 14, wherein initiating the action comprises
performing at least one of: adjusting a sampling rate of at least
one of the plurality of input channels; requesting a maintenance
event; changing a utilized sensor corresponding to at least one of
the plurality of input channels; storing event data related to the
vehicle steering system and the anomalous condition; and providing
one of an alert or a notification in response to the anomalous
condition.
16. The method of claim 14, wherein initiating the action comprises
performing at least one of: adjusting a sampling rate of at least
one of the plurality of input channels; requesting a maintenance
event; changing a utilized sensor corresponding to at least one of
the plurality of input channels; storing event data related to the
vehicle steering system and the operating signal; and providing one
of an alert or a notification in response to the operating
signal.
17. The method of claim 12, further comprising detecting a
misalignment in response to the operating signal indicating a
change in energy at frequencies at least twice a frequency of at
least one of the one or more operating frequencies.
18. The method of claim 12, wherein the operating signal indicates
an anomalous condition.
19. The method of claim 18, wherein the anomalous condition is a
pre-failure mode condition for the vehicle steering system.
20. The method of claim 18, further comprising performing band-pass
tracking associated with one or more rotating components of the
vehicle steering system to detect the anomalous condition.
21. The method of claim 12, wherein at least one of the plurality
of input channels is communicatively coupled to a vibration sensor,
and wherein the method further comprises detecting a noise pattern
from the vehicle steering system in response to detection values
from the vibration sensor.
22. The method of claim 18, wherein detecting the anomalous
condition comprises performing a frequency analysis at a selected
multiple of a rotational speed of one or more rotating components
of the vehicle steering system.
23. The method of claim 18, wherein detecting the anomalous
condition comprises performing a frequency analysis at a selected
multiple of at least one of the one or more operating frequencies.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application claims the benefit of, and is a
continuation of, U.S. Non-Provisional patent application Ser. No.
16/143,347, filed Sep. 26, 2018, entitled METHODS AND SYSTEMS FOR
INTELLIGENT COLLECTION AND ANALYSIS OF VEHICLE DATA
(STRF-0017-U01).
[0002] U.S. Ser. No. 16/143,347 (STRF-0017-U01) is a continuation
of U.S. Non-Provisional patent application Ser. No. 15/973,406,
filed May 7, 2018, entitled METHODS AND SYSTEMS FOR DETECTION IN AN
INDUSTRIAL INIERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH
LARGE DATA SETS (STRF-0001-U22).
[0003] U.S. Ser. No. 15/973,406 (STRF-0001-U22) is a bypass
continuation-in-part of International Application Number
PCT/US17/31721, filed May 9, 2017, entitled METHODS AND SYSTEM FOR
THE INDUSTRIAL INTERNET OF THINGS, published on Nov. 16, 2017, as
WO 2017/196821 (STRF-0001-WO), which claims priority to: U.S.
Provisional Patent Application Ser. No. 62/333,589, filed May 9,
2016, entitled STRONG FORCE INDUSTRIAL IOT MATRIX (STRF-0001-P01);
U.S. Provisional Patent Application Ser. No. 62/350,672, filed Jun.
15, 2016, entitled STRATEGY FOR HIGH SAMPLING RATE DIGITAL
RECORDING OF MEASUREMENT WAVEFORM DATA AS PART OF AN AUTOMATED
SEQUENTIAL LIST THAT STREAMS LONG-DURATION AND GAP-FREE WAVEFORM
DATA TO STORAGE FOR MORE FLEXIBLE POST-PROCESSING (STRF-0001-P02);
U.S. Provisional Patent Application Ser. No. 62/412,843, filed Oct.
26, 2016, entitled METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET
OF THINGS (STRF-0001-P03); and U.S. Provisional Patent Application
Ser. No. 62/427,141, filed Nov. 28, 2016, entitled METHODS AND
SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS (STRF-0001-P04).
[0004] U.S. Ser. No. 15/973,406 (STRF-0001-U22) also claims
priority to: U.S. Provisional Patent Application Ser. No.
62/540,557, filed Aug. 2, 2017, entitled SMART HEATING SYSTEMS IN
AN INDUSTRIAL INTERNET OF THINGS (STRF-0001-P05); U.S. Provisional
Patent Application Ser. No. 62/562,487, filed Sep. 24, 2017,
entitled METHODS AND SYSTEMS FOR THE INDUSTRIAL INIERNET OF THINGS
(STRF-0001-P06); and U.S. Provisional Patent Application Ser. No.
62/583,487, filed Nov. 8, 2017, entitled METHODS AND SYSTEMS FOR
THE INDUSTRIAL INTERNET OF THINGS (STRF-0001-P07).
[0005] U.S. Ser. No. 16/143,347 (STRF-0017-U01) claims the benefit
of, and is a bypass continuation of, International Application
Number PCT/US18/45036, filed Aug. 2, 2018, entitled METHODS AND
SYSTEMS FOR DETECTION IN AN INDUSTRIAL INTERNET OF THINGS DATA
COLLECTION ENVIRONMENT WITH LARGE DATA SETS (STRF-0011-WO).
[0006] International Application Number PCT/US18/45036
(STRF-0011-WO) claims the benefit of, and is a continuation of,
U.S. Non-Provisional Patent Application Ser. No. 15/973,406, filed
May 7, 2018, entitled METHODS AND SYSTEMS FOR DETECTION IN AN
INDUSTRIAL INTERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH
LARGE DATA SETS (STRF-0001-U22).
[0007] International Application Number PCT/US18/45036
(STRF-0011-WO) claims priority to: U.S. Provisional Patent
Application Ser. No. 62/540,557, filed Aug. 2, 2017, entitled SMART
HEATING SYSTEMS IN AN INDUSTRIAL INTERNET OF THINGS
(STRF-0001-P05); U.S. Provisional Patent Application Ser. No.
62/540,513, filed Aug. 2, 2017, entitled SYSTEMS AND METHODS FOR
SMART HEATING SYSTEM THAT PRODUCES AND USES HYDROGEN FUEL
(STRF-0001-P08); U.S. Provisional Patent Application Ser. No.
62/562,487, filed Sep. 24, 2017, entitled METHODS AND SYSTEMS FOR
THE INDUSTRIAL INTERNET OF THINGS (STRF-0001-P06); and U.S.
Provisional Patent Application Ser. No. 62/583,487, filed Nov. 8,
2017, entitled METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF
THINGS (STRF-0001-P07).
[0008] U.S. Ser. No. 16/143,347 (STRF-0017-U01) claims priority to
U.S. Provisional Patent Application Ser. No. 62/583,487, filed Nov.
8, 2017, entitled METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET
OF THINGS (STRF-0001-P07).
[0009] All of the foregoing applications are hereby incorporated by
reference as if fully set forth herein in their entirety.
BACKGROUND
1. Field
[0010] The present disclosure relates to methods and systems for
data collection in industrial environments, as well as methods and
systems for leveraging collected data for monitoring, remote
control, autonomous action, and other activities in industrial
environments.
2. Description of the Related Art
[0011] Heavy industrial environments, such as environments for
large scale manufacturing (such as manufacturing of aircraft,
ships, trucks, automobiles, and large industrial machines), energy
production environments (such as oil and gas plants, renewable
energy environments, and others), energy extraction environments
(such as mining, drilling, and the like), construction environments
(such as for construction of large buildings), and others, involve
highly complex machines, devices and systems and highly complex
workflows, in which operators must account for a host of
parameters, metrics, and the like in order to optimize design,
development, deployment, and operation of different technologies in
order to improve overall results. Historically, data has been
collected in heavy industrial environments by human beings using
dedicated data collectors, often recording batches of specific
sensor data on media, such as tape or a hard drive, for later
analysis. Batches of data have historically been returned to a
central office for analysis, such as undertaking signal processing
or other analysis on the data collected by various sensors, after
which analysis can be used as a basis for diagnosing problems in an
environment and/or suggesting ways to improve operations. This work
has historically taken place on a time scale of weeks or months,
and has been directed to limited data sets.
[0012] The emergence of the Internet of Things (IoT) has made it
possible to connect continuously to, and among, a much wider range
of devices. Most such devices are consumer devices, such as lights,
thermostats, and the like. More complex industrial environments
remain more difficult, as the range of available data is often
limited, and the complexity of dealing with data from multiple
sensors makes it much more difficult to produce "smart" solutions
that are effective for the industrial sector. A need exists for
improved methods and systems for data collection in industrial
environments, as well as for improved methods and systems for using
collected data to provide improved monitoring, control, intelligent
diagnosis of problems and intelligent optimization of operations in
various heavy industrial environments.
[0013] Industrial system in various environments have a number of
challenges to utilizing data from a multiplicity of sensors. Many
industrial systems have a wide range of computing resources and
network capabilities at a location at a given time, for example as
parts of the system are upgraded or replaced on varying time
scales, as mobile equipment enters or leaves a location, and due to
the capital costs and risks of upgrading equipment. Additionally,
many industrial systems are positioned in challenging environments,
where network connectivity can be variable, where a number of noise
sources such as vibrational noise and electro-magnetic (EM) noise
sources can be significant an in varied locations, and with
portions of the system having high pressure, high noise, high
temperature, and corrosive materials. Many industrial processes are
subject to high variability in process operating parameters and
non-linear responses to off-nominal operations. Accordingly,
sensing requirements for industrial processes can vary with time,
operating stages of a process, age and degradation of equipment,
and operating conditions. Previously known industrial processes
suffer from sensing configurations that are conservative, detecting
many parameters that are not needed during most operations of the
industrial system, or that accept risk in the process, and do not
detect parameters that are only occasionally utilized in
characterizing the system. Further, previously known industrial
systems are not flexible to configuring sensed parameters rapidly
and in real-time, and in managing system variance such as
intermittent network availability. Industrial systems often use
similar components across systems such as pumps, mixers, tanks, and
fans. However, previously known industrial systems do not have a
mechanism to leverage data from similar components that may be used
in a different type of process, and/or that may be unavailable due
to competitive concerns. Additionally, previously known industrial
systems do not integrate data from offset systems into the sensor
plan and execution in real time.
SUMMARY
[0014] Systems for data collection and frequency evaluation for a
vehicle steering system are disclosed. In embodiments, an example
monitoring system for data collection in a vehicle steering system
may include a vehicle steering system comprising a rack, a pinion,
and a steering column; a data acquisition circuit structured to
interpret a plurality of detection values, each of the plurality of
detection values corresponding to at least one of a plurality of
input sensors, each of the plurality of input sensors operationally
coupled to the rack, the pinion, or the steering column, and
communicatively coupled to the data acquisition circuit; a data
storage circuit structured to store one or more operating
frequencies of the rack, the pinion, or the steering column; and a
frequency evaluation circuit structured to detect an operating
signal in response to the plurality of detection values, wherein
the operating signal comprises a frequency higher than the one or
more operating frequencies; and a response circuit structured to
perform at least one operation in response to the detected
operating signal.
[0015] Certain further aspects of an example system are described
following, any one or more of which may be present in certain
embodiments. An example system may include wherein the at least one
operation comprises performing at least one of: adjusting a
sampling rate of at least one of the plurality of detection values;
requesting a maintenance event; changing a utilized input sensor
corresponding to at least one of the plurality of input sensors;
storing event data related to the vehicle steering system and the
operating signal; and providing one of an alert or a notification
in response to the operating signal.
[0016] An example system may include wherein the frequency
evaluation circuit is further structured to detect a misalignment
in response to the operating signal indicating a change in energy
at frequencies at least twice a frequency of at least one of the
one or more operating frequencies.
[0017] An example system may include wherein the operating signal
indicates an anomalous condition.
[0018] An example system may include wherein the anomalous
condition comprises a pre-failure mode condition for the vehicle
steering system.
[0019] An example system may further include a data analysis
circuit structured to analyze at least two of the plurality of
detection values, to determine a relative phase value between the
at least two of the plurality of detection values, and to detect an
anomalous condition in response to the relative phase value.
[0020] An example system may include wherein the vehicle steering
system comprises one or more rotating components, and wherein the
data analysis circuit is further structured to perform band-pass
tracking associated with the one or more rotating components to
detect the anomalous condition.
[0021] An example system may include wherein at least one of the
plurality of input sensors comprises a vibration sensor, and
wherein the frequency evaluation circuit is further structured to
detect a noise pattern from the vehicle steering system in response
to detection values from the vibration sensor.
[0022] An example system may include wherein the frequency
evaluation circuit is further structured to detect the noise
pattern at frequencies higher than a frequency at which one or more
rotating components of the vehicle steering system rotates.
[0023] An example system may include wherein detecting the
anomalous condition comprises performing a frequency analysis at a
selected multiple of a rotational speed of at least one of the one
or more rotating components.
[0024] An example system may include wherein the frequency
evaluation circuit is further structured to perform a frequency
analysis at a selected multiple of at least one of the one or more
operating frequencies.
[0025] Methods for data collection and frequency evaluation for a
vehicle steering system are disclosed. In embodiments, an example
method for collecting data in a vehicle steering system may include
collecting data from a plurality of input channels, wherein a
subset of the plurality of input channels is communicatively
coupled to sensors measuring operational parameters of a rack, a
pinion, and a steering column of the vehicle steering system;
storing one or more operating frequencies related to an operation
of at least one of the rack, the pinion, or the steering column;
interpreting a plurality of detection values from the collected
data, each of the plurality of detection values corresponding to at
least one of the plurality of input channels; and detecting an
operating signal on one of the plurality of input channels at a
frequency higher than at least one of the one or more operating
frequencies.
[0026] Certain further aspects of an example method are described
following, any one or more of which may be present in certain
embodiments. An example method may further include analyzing at
least two of the plurality of input channels, and wherein detecting
the operating signal comprises detecting an anomalous condition in
response to a relative phase difference between the at least two of
the plurality of input channels.
[0027] An example method may further include initiating an action
on the vehicle steering system based on the detection of the
operating signal.
[0028] An example method may include wherein initiating the action
comprises performing at least one of: adjusting a sampling rate of
at least one of the plurality of input channels; requesting a
maintenance event; changing a utilized sensor corresponding to at
least one of the plurality of input channels; storing event data
related to the vehicle steering system and the anomalous condition;
and providing one of an alert or a notification in response to the
anomalous condition.
[0029] An example method may include wherein initiating the action
comprises performing at least one of: adjusting a sampling rate of
at least one of the plurality of input channels; requesting a
maintenance event; changing a utilized sensor corresponding to at
least one of the plurality of input channels; storing event data
related to the vehicle steering system and the operating signal;
and providing one of an alert or a notification in response to the
operating signal.
[0030] An example method may further include detecting a
misalignment in response to the operating signal indicating a
change in energy at frequencies at least twice a frequency of at
least one of the one or more operating frequencies.
[0031] An example method may include wherein the operating signal
indicates an anomalous condition.
[0032] An example method may include wherein the anomalous
condition is a pre-failure mode condition for the vehicle steering
system.
[0033] An example method may further include performing band-pass
tracking associated with one or more rotating components of the
vehicle steering system to detect the anomalous condition.
[0034] An example method may include wherein at least one of the
plurality of input channels is communicatively coupled to a
vibration sensor, and wherein the method further comprises
detecting a noise pattern from the vehicle steering system in
response to detection values from the vibration sensor.
[0035] An example method may include wherein detecting the
anomalous condition comprises performing a frequency analysis at a
selected multiple of a rotational speed of one or more rotating
components of the vehicle steering system.
[0036] An example method may include wherein detecting the
anomalous condition comprises performing a frequency analysis at a
selected multiple of at least one of the one or more operating
frequencies.
[0037] The present disclosure describes a data monitoring system,
the system according to one disclosed non-limiting embodiment of
the present disclosure can include a data acquisition circuit
structured to interpret a plurality of detection values, each of
the plurality of detection values corresponding to at least one of
a plurality of input sensors communicatively coupled to the data
acquisition circuit, a data storage circuit structured to store
specifications and anticipated state information for a plurality of
vehicle types, an analysis circuit structured to analyze the
plurality of detection values relative to specifications and
anticipated state information to determine a vehicle performance
parameter, and a response circuit structured to initiate an action
in response to the vehicle performance parameter.
[0038] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the action in
response to the vehicle performance parameter is selected from a
list of actions consisting of: adjusting a sensor scaling value,
selecting an alternate sensor from a plurality available, acquiring
data from a plurality of sensors of different ranges, recommending
an alternate sensor, increasing an acquisition range for a sensor,
and issuing an alarm or an alert.
[0039] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the action in
response to the vehicle performance parameter includes at least one
of: enabling or disabling a processing of the plurality of
detection values corresponding to certain sensors based on a
component status.
[0040] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the action in
response to the vehicle performance parameter includes at least one
of: enabling or disabling a processing of detection values by
accessing new sensors or types of sensors.
[0041] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the action in
response to the vehicle performance parameter includes at least one
of: enabling or disabling a processing of detection values by
accessing data from multiple sensors.
[0042] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the plurality
of input sensors includes at least one of: a temperature sensor, a
load sensor, an optical vibration sensor, an acoustic wave sensor,
a heat flux sensor, an infrared sensor, an accelerometer, a
tri-axial vibration sensor, a flow sensor, a fluid particulate
sensor, or a tachometer.
[0043] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the action in
response to the vehicle performance parameter includes at least one
of: enabling or disabling a processing of detection values by
switching to sensors having different response rates, different
sensitivity, or different ranges.
[0044] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the switching
is controlled by at least one of a model, a set of rules, or a
machine learning system.
[0045] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the switching
includes at least one of: switching from one input port to another,
altering a multiplexing of data, activating a system to obtain
additional data, or directing changes to a multiplexer (MUX)
control circuit.
[0046] The present disclosure describes a method, the method
according to one disclosed non-limiting embodiment of the present
disclosure can include interpreting a plurality of detection values
each of the plurality of detection values corresponding to at least
one of a plurality of input sensors, storing specifications and
anticipated state information for a plurality of vehicle types,
analyzing the plurality of detection values relative to
specifications and anticipated state information to determine a
vehicle performance parameter, and initiating an action in response
to the vehicle performance parameter.
[0047] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the action in
response to the vehicle performance parameter includes at least one
action selected from a list of actions consisting of: adjusting a
sensor scaling value, selecting an alternate sensor from a
plurality available, acquiring data from a plurality of sensors of
different ranges, recommending an alternate sensor, increasing an
acquisition range for a sensor, and issuing an alarm or an
alert.
[0048] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the action in
response to the vehicle performance parameter includes at least one
of: enabling or disabling a processing of the plurality of
detection values corresponding to certain sensors based on a
component status.
[0049] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the action in
response to the vehicle performance parameter includes at least one
of: enabling or disabling a processing of detection values by
accessing new sensors or types of sensors.
[0050] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the action in
response to the vehicle performance parameter includes at least one
of: enabling or disabling a processing of detection values by
accessing data from multiple sensors.
[0051] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the action in
response to the vehicle performance parameter includes at least one
of: enabling or disabling a processing of detection values by
switching to sensors having different response rates, different
sensitivity, or different ranges.
[0052] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the switching
is controlled by at least one of a model, a set of rules, or a
machine learning system.
[0053] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the switching
involves at least one of: switching from one input port to another,
altering a multiplexing of data, activating a system to obtain
additional data, or directing changes to a multiplexer (MUX)
control circuit.
[0054] The present disclosure describes an apparatus, the apparatus
according to one disclosed non-limiting embodiment of the present
disclosure can include a data acquisition circuit structured to
interpret a plurality of detection values, each of the plurality of
detection values corresponding to at least one of a plurality of
input sensors communicatively coupled to the data acquisition
circuit, a data storage circuit structured to store specifications
and anticipated state information for a plurality of vehicle types,
a bearing analysis circuit structured to analyze the plurality of
detection values relative to specifications and anticipated state
information to determine a vehicle performance parameter, and a
response circuit structured to initiate an action in response to
the vehicle performance parameter.
[0055] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the action in
response to the vehicle performance parameter includes at least one
action selected from a list of actions consisting of: adjusting a
sensor scaling value, selecting an alternate sensor from a
plurality available, acquiring data from a plurality of sensors of
different ranges, recommending an alternate sensor, increasing an
acquisition range for a sensor, and issuing an alarm or an
alert.
[0056] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the plurality
of input sensors includes at least one of: a temperature sensor, a
load sensor, an optical vibration sensor, an acoustic wave sensor,
a heat flux sensor, an infrared sensor, an accelerometer, a
tri-axial vibration sensor, a flow sensor, a fluid particulate
sensor, or a tachometer.
[0057] The present disclosure describes a system for data
collection in a vehicle, the system, according to one disclosed
non-limiting embodiment of the present disclosure, can include a
data acquisition circuit structured to interpret a plurality of
detection values, each of the plurality of detection values
corresponding to input received from at least one of a plurality of
input sensors, each of the plurality of input sensors operatively
coupled to at least one of a plurality of components of the
vehicle, a data analysis circuit structured to determine a state
value, wherein the data analysis circuit includes a pattern
recognition circuit structured to determine the state value by
analyzing a subset of the plurality of detection values and at
least one external detection value using at least one of a neural
net or an expert system, and an analysis response circuit
structured to adjust a parameter of the vehicle in response to the
state value.
[0058] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the analysis
response circuit is further structured to adjust a detection
package in response to the state value, wherein the detection
package includes a selection of available sensors utilized as the
plurality of input sensors.
[0059] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the detection
package further includes a sensor parameter for at least one of the
plurality of input sensors.
[0060] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the state
value includes at least one of: an off-nominal operation, a
component failure, a component fault, and a component maintenance
requirement and wherein the adjusting the detection package
includes enhancing a resolution of the detection values in response
to the state value.
[0061] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein enhancing the
resolution of the detection values includes at least one of
enhancing a sensor resolution, changing from a first input sensor
to a second input sensor having a higher resolution capability than
the first input sensor, and changing a data storage profile to
enhance a resolution of stored data of the detection values.
[0062] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the at least
one of the neural net or the expert system performs a pattern
recognition operation to determine the state value.
[0063] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the pattern
recognition operation is performed on vibration data of the
plurality of detection values.
[0064] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the at least
one of the neural net or the expert system further compares the
vibration data of the plurality of detection values to a library of
noise patterns, wherein the library of noise patterns includes the
at least one external data value.
[0065] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the at least
one of the neural net or the expert system is configured to at
least intermittently access a self-organizing marketplace, and
wherein the self-organizing marketplace provides the library of
noise patterns.
[0066] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the at least
one of the neural net or the expert system is configured to provide
at least a portion of the vibration data to the self-organizing
marketplace.
[0067] The present disclosure describes a method, according to one
disclosed non-limiting embodiment of the present disclosure, the
method can include interpreting a plurality of detection values of
a vehicle, each of the plurality of detection values corresponding
to input received from at least one of a plurality of input
sensors, each of the plurality of input sensors operatively coupled
to at least one component of the vehicle, operating at least one of
a neural net or an expert system on the plurality of detection
values to determine a state value for at least one of a component
or the vehicle and adjusting at least one of a sensing parameter or
a data storage profile in response to the state value.
[0068] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the state
value includes at least one of: an off-nominal operation, a
component failure, a component fault, and a component maintenance
requirement, and wherein the adjusting the at least one of the
sensing parameter or the data storage profile includes enhancing a
resolution of the detection values in response to the state
value.
[0069] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the at least
one of the neural net or the expert system performs a pattern
recognition operation to determine the state value.
[0070] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the at least
one of the neural net or the expert system accesses external data
value from a self-organizing marketplace, and further determines
the state value in response to the external data.
[0071] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the external
data value includes a library of noise patterns.
[0072] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the library
of noise patterns includes a vibration fingerprint for a component
of the vehicle.
[0073] The present disclosure describes an apparatus, according to
one disclosed non-limiting embodiment of the present disclosure,
the apparatus can include a data acquisition circuit structured to
interpret a plurality of detection values, each of the plurality of
detection values corresponding to input received from at least one
of a plurality of input sensors, each of the plurality of input
sensors operatively coupled to at least one of a plurality of
components of a vehicle, a data analysis circuit structured to
determine a state value, wherein the data analysis circuit includes
a pattern recognition circuit structured to determine the state
value by performing a pattern recognition operation on a subset of
the plurality of detection values and at least one external
detection value using at least one of a neural net or an expert
system and an analysis response circuit structured to adjust a
parameter of the vehicle in response to the state value.
[0074] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the adjusting
the parameter of the vehicle includes adjusting operations of the
vehicle to reduce a work load on a component of the vehicle.
[0075] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the state
value includes a normal operating state for a component of the
vehicle, and wherein the adjusting the parameter of the vehicle
includes reducing an amount of data of the plurality of detection
values that is stored relating to the component of the vehicle.
[0076] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the state
value includes, for a component of the vehicle, at least one of: an
off-nominal operation, a failure, a fault, or a maintenance
requirement, and wherein the adjusting the parameter of the vehicle
includes increasing an amount of data of the plurality of detection
values that is stored relating to the component of the vehicle.
[0077] Methods and systems are provided herein for data collection
in industrial environments, as well as for improved methods and
systems for using collected data to provide improved monitoring,
control, and intelligent diagnosis of problems and intelligent
optimization of operations in various heavy industrial
environments. These methods and systems include methods, systems,
components, devices, workflows, services, processes, and the like
that are deployed in various configurations and locations, such as:
(a) at the "edge" of the Internet of Things, such as in the local
environment of a heavy industrial machine; (b) in data transport
networks that move data between local environments of heavy
industrial machines and other environments, such as of other
machines or of remote controllers, such as enterprises that own or
operate the machines or the facilities in which the machines are
operated; and (c) in locations where facilities are deployed to
control machines or their environments, such as cloud-computing
environments and on-premises computing environments of enterprises
that own or control heavy industrial environments or the machines,
devices or systems deployed in them. These methods and systems
include a range of ways for providing improved data include a range
of methods and systems for providing improved data collection, as
well as methods and systems for deploying increased intelligence at
the edge, in the network, and in the cloud or premises of the
controller of an industrial environment.
[0078] Methods and systems are disclosed herein for continuous
ultrasonic monitoring, including providing continuous ultrasonic
monitoring of rotating elements and bearings of an energy
production facility; for cloud-based systems including machine
pattern recognition based on the fusion of remote, analog
industrial sensors or machine pattern analysis of state information
from multiple analog industrial sensors to provide anticipated
state information for an industrial system; for on-device sensor
fusion and data storage for industrial IoT devices, including
on-device sensor fusion and data storage for an Industrial IoT
device, where data from multiple sensors are multiplexed at the
device for storage of a fused data stream; and for self-organizing
systems including a self-organizing data marketplace for industrial
IoT data, including a self-organizing data marketplace for
industrial IoT data, where available data elements are organized in
the marketplace for consumption by consumers based on training a
self-organizing facility with a training set and feedback from
measures of marketplace success, for self-organizing data pools,
including self-organization of data pools based on utilization
and/or yield metrics, including utilization and/or yield metrics
that are tracked for a plurality of data pools, a self-organized
swarm of industrial data collectors, including a self-organizing
swarm of industrial data collectors that organize among themselves
to optimize data collection based on the capabilities and
conditions of the members of the swarm, a self-organizing
collector, including a self-organizing, multi-sensor data collector
that can optimize data collection, power and/or yield based on
conditions in its environment, a self-organizing storage for a
multi-sensor data collector, including self-organizing storage for
a multi-sensor data collector for industrial sensor data, a
self-organizing network coding for a multi-sensor data network,
including self-organizing network coding for a data network that
transports data from multiple sensors in an industrial data
collection environment.
[0079] Methods and systems are disclosed herein for training
artificial intelligence ("AI") models based on industry-specific
feedback, including training an AI model based on industry-specific
feedback that reflects a measure of utilization, yield, or impact,
where the AI model operates on sensor data from an industrial
environment; for an industrial IoT distributed ledger, including a
distributed ledger supporting the tracking of transactions executed
in an automated data marketplace for industrial IoT data; for a
network-sensitive collector, including a network
condition-sensitive, self-organizing, multi-sensor data collector
that can optimize based on bandwidth, quality of service, pricing,
and/or other network conditions; for a remotely organized universal
data collector that can power up and down sensor interfaces based
on need and/or conditions identified in an industrial data
collection environment; and for a haptic or multi-sensory user
interface, including a wearable haptic or multi-sensory user
interface for an industrial sensor data collector, with vibration,
heat, electrical, and/or sound outputs.
[0080] Methods and systems are disclosed herein for a presentation
layer for augmented reality and virtual reality (AR/VR) industrial
glasses, where heat map elements are presented based on patterns
and/or parameters in collected data; and for condition-sensitive,
self-organized tuning of AR/VR interfaces based on feedback metrics
and/or training in industrial environments.
[0081] In embodiments, a system for data collection, processing,
and utilization of signals from at least a first element in a first
machine in an industrial environment includes a platform including
a computing environment connected to a local data collection system
having at least a first sensor signal and a second sensor signal
obtained from at least the first machine in the industrial
environment. The system includes a first sensor in the local data
collection system configured to be connected to the first machine
and a second sensor in the local data collection system. The system
further includes a crosspoint switch in the local data collection
system having multiple inputs and multiple outputs including a
first input connected to the first sensor and a second input
connected to the second sensor. Throughout the present disclosure,
wherever a crosspoint switch, multiplexer (MUX) device, or other
multiple-input multiple-output data collection or communication
device is described, any multi-sensor acquisition device is also
contemplated herein. In certain embodiments, a multi-sensor
acquisition device includes one or more channels configured for, or
compatible with, an analog sensor input. The multiple outputs
include a first output and second output configured to be
switchable between a condition in which the first output is
configured to switch between delivery of the first sensor signal
and the second sensor signal and a condition in which there is
simultaneous delivery of the first sensor signal from the first
output and the second sensor signal from the second output. Each of
multiple inputs is configured to be individually assigned to any of
the multiple outputs, or combined in any subsets of the inputs to
the outputs. Unassigned outputs are configured to be switched off,
for example by producing a high-impedance state.
[0082] In embodiments, the first sensor signal and the second
sensor signal are continuous vibration data about the industrial
environment. In embodiments, the second sensor in the local data
collection system is configured to be connected to the first
machine. In embodiments, the second sensor in the local data
collection system is configured to be connected to a second machine
in the industrial environment. In embodiments, the computing
environment of the platform is configured to compare relative
phases of the first and second sensor signals. In embodiments, the
first sensor is a single-axis sensor and the second sensor is a
three-axis sensor. In embodiments, at least one of the multiple
inputs of the crosspoint switch includes internet protocol,
front-end signal conditioning, for improved signal-to-noise ratio.
In embodiments, the crosspoint switch includes a third input that
is configured with a continuously monitored alarm having a
pre-determined trigger condition when the third input is unassigned
to or undetected at any of the multiple outputs.
[0083] In embodiments, the local data collection system includes
multiple multiplexing units and multiple data acquisition units
receiving multiple data streams from multiple machines in the
industrial environment. In embodiments, the local data collection
system includes distributed complex programmable hardware device
("CPLD") chips each dedicated to a data bus for logic control of
the multiple multiplexing units and the multiple data acquisition
units that receive the multiple data streams from the multiple
machines in the industrial environment. In embodiments, the local
data collection system is configured to provide high-amperage input
capability using solid state relays. In embodiments, the local data
collection system is configured to power-down at least one of an
analog sensor channel and a component board.
[0084] In embodiments, the local data collection system includes a
phase-lock loop band-pass tracking filter configured to obtain
slow-speed revolutions per minute ("RPMs") and phase information.
In embodiments, the local data collection system is configured to
digitally derive phase using on-board timers relative to at least
one trigger channel and at least one of the multiple inputs. In
embodiments, the local data collection system includes a
peak-detector configured to autoscale using a separate
analog-to-digital converter for peak detection. In embodiments, the
local data collection system is configured to route at least one
trigger channel that is raw and buffered into at least one of the
multiple inputs. In embodiments, the local data collection system
includes at least one delta-sigma analog-to-digital converter that
is configured to increase input oversampling rates to reduce
sampling rate outputs and to minimize anti-aliasing filter
requirements. In embodiments, the distributed CPLD chips each
dedicated to the data bus for logic control of the multiple
multiplexing units and the multiple data acquisition units includes
as high-frequency crystal clock reference configured to be divided
by at least one of the distributed CPLD chips for at least one
delta-sigma analog-to-digital converter to achieve lower sampling
rates without digital resampling.
[0085] In embodiments, the local data collection system is
configured to obtain long blocks of data at a single relatively
high-sampling rate as opposed to multiple sets of data taken at
different sampling rates. In embodiments, the single relatively
high-sampling rate corresponds to a maximum frequency of about
forty kilohertz. In embodiments, the long blocks of data are for a
duration that is in excess of one minute. In embodiments, the local
data collection system includes multiple data acquisition units
each having an onboard card set configured to store calibration
information and maintenance history of a data acquisition unit in
which the onboard card set is located. In embodiments, the local
data collection system is configured to plan data acquisition
routes based on hierarchical templates.
[0086] In embodiments, the local data collection system is
configured to manage data collection bands. In embodiments, the
data collection bands define a specific frequency band and at least
one of a group of spectral peaks, a true-peak level, a crest factor
derived from a time waveform, and an overall waveform derived from
a vibration envelope. In embodiments, the local data collection
system includes a neural net expert system using intelligent
management of the data collection bands. In embodiments, the local
data collection system is configured to create data acquisition
routes based on hierarchical templates that each include the data
collection bands related to machines associated with the data
acquisition routes. In embodiments, at least one of the
hierarchical templates is associated with multiple interconnected
elements of the first machine. In embodiments, at least one of the
hierarchical templates is associated with similar elements
associated with at least the first machine and a second machine. In
embodiments, at least one of the hierarchical templates is
associated with at least the first machine being proximate in
location to a second machine.
[0087] In embodiments, the local data collection system includes a
graphical user interface ("GUI") system configured to manage the
data collection bands. In embodiments, the GUI system includes an
expert system diagnostic tool. In embodiments, the platform
includes cloud-based, machine pattern analysis of state information
from multiple sensors to provide anticipated state information for
the industrial environment. In embodiments, the platform is
configured to provide self-organization of data pools based on at
least one of the utilization metrics and yield metrics. In
embodiments, the platform includes a self-organized swarm of
industrial data collectors. In embodiments, the local data
collection system includes a wearable haptic user interface for an
industrial sensor data collector with at least one of vibration,
heat, electrical, and sound outputs.
[0088] In embodiments, multiple inputs of the crosspoint switch
include a third input connected to the second sensor and a fourth
input connected to the second sensor. The first sensor signal is
from a single-axis sensor at an unchanging location associated with
the first machine. In embodiments, the second sensor is a
three-axis sensor. In embodiments, the local data collection system
is configured to record gap-free digital waveform data
simultaneously from at least the first input, the second input, the
third input, and the fourth input. In embodiments, the platform is
configured to determine a change in relative phase based on the
simultaneously recorded gap-free digital waveform data. In
embodiments, the second sensor is configured to be movable to a
plurality of positions associated with the first machine while
obtaining the simultaneously recorded gap-free digital waveform
data. In embodiments, multiple outputs of the crosspoint switch
include a third output and fourth output. The second, third, and
fourth outputs are assigned together to a sequence of tri-axial
sensors each located at different positions associated with the
machine. In embodiments, the platform is configured to determine an
operating deflection shape based on the change in relative phase
and the simultaneously recorded gap-free digital waveform data.
[0089] In embodiments, the unchanging location is a position
associated with the rotating shaft of the first machine. In
embodiments, tri-axial sensors in the sequence of the tri-axial
sensors are each located at different positions on the first
machine but are each associated with different bearings in the
machine. In embodiments, tri-axial sensors in the sequence of the
tri-axial sensors are each located at similar positions associated
with similar bearings but are each associated with different
machines. In embodiments, the local data collection system is
configured to obtain the simultaneously recorded gap-free digital
waveform data from the first machine while the first machine and a
second machine are both in operation. In embodiments, the local
data collection system is configured to characterize a contribution
from the first machine and the second machine in the simultaneously
recorded gap-free digital waveform data from the first machine. In
embodiments, the simultaneously recorded gap-free digital waveform
data has a duration that is in excess of one minute.
[0090] In embodiments, a method of monitoring a machine having at
least one shaft supported by a set of bearings includes monitoring
a first data channel assigned to a single-axis sensor at an
unchanging location associated with the machine. The method
includes monitoring second, third, and fourth data channels each
assigned to an axis of a three-axis sensor. The method includes
recording gap-free digital waveform data simultaneously from all of
the data channels while the machine is in operation and determining
a change in relative phase based on the digital waveform data.
[0091] In embodiments, the tri-axial sensor is located at a
plurality of positions associated with the machine while obtaining
the digital waveform. In embodiments, the second, third, and fourth
channels are assigned together to a sequence of tri-axial sensors
each located at different positions associated with the machine. In
embodiments, the data is received from all of the sensors
simultaneously. In embodiments, the method includes determining an
operating deflection shape based on the change in relative phase
information and the waveform data. In embodiments, the unchanging
location is a position associated with the shaft of the machine. In
embodiments, the tri-axial sensors in the sequence of the tri-axial
sensors are each located at different positions and are each
associated with different bearings in the machine. In embodiments,
the unchanging location is a position associated with the shaft of
the machine. The tri-axial sensors in the sequence of the tri-axial
sensors are each located at different positions and are each
associated with different bearings that support the shaft in the
machine.
[0092] In embodiments, the method includes monitoring the first
data channel assigned to the single-axis sensor at an unchanging
location located on a second machine. The method includes
monitoring the second, the third, and the fourth data channels,
each assigned to the axis of a three-axis sensor that is located at
the position associated with the second machine. The method also
includes recording gap-free digital waveform data simultaneously
from all of the data channels from the second machine while both of
the machines are in operation. In embodiments, the method includes
characterizing the contribution from each of the machines in the
gap-free digital waveform data simultaneously from the second
machine.
[0093] In embodiments, a method for data collection, processing,
and utilization of signals with a platform monitoring at least a
first element in a first machine in an industrial environment
includes obtaining, automatically with a computing environment, at
least a first sensor signal and a second sensor signal with a local
data collection system that monitors at least the first machine.
The method includes connecting a first input of a crosspoint switch
of the local data collection system to a first sensor and a second
input of the crosspoint switch to a second sensor in the local data
collection system. The method includes switching between a
condition in which a first output of the crosspoint switch
alternates between delivery of at least the first sensor signal and
the second sensor signal and a condition in which there is
simultaneous delivery of the first sensor signal from the first
output and the second sensor signal from a second output of the
crosspoint switch. The method also includes switching off
unassigned outputs of the crosspoint switch into a high-impedance
state.
[0094] In embodiments, the first sensor signal and the second
sensor signal are continuous vibration data from the industrial
environment. In embodiments, the second sensor in the local data
collection system is connected to the first machine. In
embodiments, the second sensor in the local data collection system
is connected to a second machine in the industrial environment. In
embodiments, the method includes comparing, automatically with the
computing environment, relative phases of the first and second
sensor signals. In embodiments, the first sensor is a single-axis
sensor and the second sensor is a three-axis sensor. In
embodiments, at least the first input of the crosspoint switch
includes internet protocol front-end signal conditioning for
improved signal-to-noise ratio.
[0095] In embodiments, the method includes continuously monitoring
at least a third input of the crosspoint switch with an alarm
having a pre-determined trigger condition when the third input is
unassigned to any of multiple outputs on the crosspoint switch. In
embodiments, the local data collection system includes multiple
multiplexing units and multiple data acquisition units receiving
multiple data streams from multiple machines in the industrial
environment. In embodiments, the local data collection system
includes distributed CPLD chips each dedicated to a data bus for
logic control of the multiple multiplexing units and the multiple
data acquisition units that receive the multiple data streams from
the multiple machines in the industrial environment. In
embodiments, the local data collection system provides
high-amperage input capability using solid state relays.
[0096] In embodiments, the method includes powering down at least
one of an analog sensor channel and a component board of the local
data collection system. In embodiments, the local data collection
system includes an external voltage reference for an A/D zero
reference that is independent of the voltage of the first sensor
and the second sensor. In embodiments, the local data collection
system includes a phase-lock loop band-pass tracking filter that
obtains slow-speed RPMs and phase information. In embodiments, the
method includes digitally deriving phase using on-board timers
relative to at least one trigger channel and at least one of
multiple inputs on the crosspoint switch.
[0097] In embodiments, the method includes auto-scaling with a
peak-detector using a separate analog-to-digital converter for peak
detection. In embodiments, the method includes routing at least one
trigger channel that is raw and buffered into at least one of
multiple inputs on the crosspoint switch. In embodiments, the
method includes increasing input oversampling rates with at least
one delta-sigma analog-to-digital converter to reduce sampling rate
outputs and to minimize anti-aliasing filter requirements. In
embodiments, the distributed CPLD chips are each dedicated to the
data bus for logic control of the multiple multiplexing units and
the multiple data acquisition units and each include a
high-frequency crystal clock reference divided by at least one of
the distributed CPLD chips for at least one delta-sigma
analog-to-digital converter to achieve lower sampling rates without
digital resampling. In embodiments, the method includes obtaining
long blocks of data at a single relatively high-sampling rate with
the local data collection system as opposed to multiple sets of
data taken at different sampling rates. In embodiments, the single
relatively high-sampling rate corresponds to a maximum frequency of
about forty kilohertz. In embodiments, the long blocks of data are
for a duration that is in excess of one minute. In embodiments, the
local data collection system includes multiple data acquisition
units and each data acquisition unit has an onboard card set that
stores calibration information and maintenance history of a data
acquisition unit in which the onboard card set is located.
[0098] In embodiments, the method includes planning data
acquisition routes based on hierarchical templates associated with
at least the first element in the first machine in the industrial
environment. In embodiments, the local data collection system
manages data collection bands that define a specific frequency band
and at least one of a group of spectral peaks, a true-peak level, a
crest factor derived from a time waveform, and an overall waveform
derived from a vibration envelope. In embodiments, the local data
collection system includes a neural net expert system using
intelligent management of the data collection bands. In
embodiments, the local data collection system creates data
acquisition routes based on hierarchical templates that each
include the data collection bands related to machines associated
with the data acquisition routes. In embodiments, at least one of
the hierarchical templates is associated with multiple
interconnected elements of the first machine. In embodiments, at
least one of the hierarchical templates is associated with similar
elements associated with at least the first machine and a second
machine. In embodiments, at least one of the hierarchical templates
is associated with at least the first machine being proximate in
location to a second machine.
[0099] In embodiments, the method includes controlling a GUI system
of the local data collection system to manage the data collection
bands. The GUI system includes an expert system diagnostic tool. In
embodiments, the computing environment of the platform includes
cloud-based, machine pattern analysis of state information from
multiple sensors to provide anticipated state information for the
industrial environment. In embodiments, the computing environment
of the platform provides self-organization of data pools based on
at least one of the utilization metrics and yield metrics. In
embodiments, the computing environment of the platform includes a
self-organized swarm of industrial data collectors. In embodiments,
each of multiple inputs of the crosspoint switch is individually
assignable to any of multiple outputs of the crosspoint switch
[0100] Methods and systems described herein for industrial machine
sensor data streaming, collection, processing, and storage may be
configured to operate and integrate with existing data collection,
processing and storage systems and may include a method for
capturing a plurality of streams of sensed data from sensors
deployed to monitor aspects of an industrial machine associated
with at least one moving part of the machine; at least one of the
streams contains a plurality of frequencies of data. The method may
include identifying a subset of data in at least one of the
plurality of streams that corresponds to data representing at least
one predefined frequency. The at least one predefined frequency is
represented by a set of data collected from alternate sensors
deployed to monitor aspects of the industrial machine associated
with the at least one moving part of the machine. The method may
further include processing the identified data with a data
processing facility that processes the identified data with an
algorithm configured to be applied to the set of data collected
from alternate sensors. Lastly, the method may include storing the
at least one of the streams of data, the identified subset of data,
and a result of processing the identified data in an electronic
data set.
[0101] Methods and systems described herein for industrial machine
sensor data streaming, collection, processing, and storage may be
configured to operate and integrate with existing data collection,
processing, and storage systems and may include a method for
applying data captured from sensors deployed to monitor aspects of
an industrial machine associated with at least one moving part of
the machine. The data is captured with predefined lines of
resolution covering a predefined frequency range and is sent to a
frequency matching facility that identifies a subset of data
streamed from other sensors deployed to monitor aspects of the
industrial machine associated with at least one moving part of the
machine. The streamed data includes a plurality of lines of
resolution and frequency ranges. The subset of data identified
corresponds to the lines of resolution and predefined frequency
range. This method may include storing the subset of data in an
electronic data record in a format that corresponds to a format of
the data captured with predefined lines of resolution and signaling
to a data processing facility the presence of the stored subset of
data. This method may, optionally, include processing the subset of
data with at least one set of algorithms, models and pattern
recognizers that corresponds to algorithms, models and pattern
recognizers associated with processing the data captured with
predefined lines of resolution covering a predefined frequency
range.
[0102] Methods and systems described herein for industrial machine
sensor data streaming, collection, processing, and storage may be
configured to operate and integrate with existing data collection,
processing and storage systems and may include a method for
identifying a subset of streamed sensor data, the sensor data
captured from sensors deployed to monitor aspects of an industrial
machine associated with at least one moving part of the machine,
the subset of streamed sensor data at predefined lines of
resolution for a predefined frequency range, and establishing a
first logical route for communicating electronically between a
first computing facility performing the identifying and a second
computing facility, wherein identified subset of the streamed
sensor data is communicated exclusively over the established first
logical route when communicating the subset of streamed sensor data
from the first facility to the second facility. This method may
further include establishing a second logical route for
communicating electronically between the first computing facility
and the second computing facility for at least one portion of the
streamed sensor data that is not the identified subset.
Additionally, this method may further include establishing a third
logical route for communicating electronically between the first
computing facility and the second computing facility for at least
one portion of the streamed sensor data that includes the
identified subset and at least one other portion of the data not
represented by the identified subset.
[0103] Methods and systems described herein for industrial machine
sensor data streaming, collection, processing, and storage may be
configured to operate and integrate with existing data collection,
processing and storage systems and may include a first data sensing
and processing system that captures first data from a first set of
sensors deployed to monitor aspects of an industrial machine
associated with at least one moving part of the machine, the first
data covering a set of lines of resolution and a frequency range.
This system may include a second data sensing and processing system
that captures and streams a second set of data from a second set of
sensors deployed to monitor aspects of the industrial machine
associated with at least one moving part of the machine, the second
data covering a plurality of lines of resolution that includes the
set of lines of resolution and a plurality of frequencies that
includes the frequency range. The system may enable selecting a
portion of the second data that corresponds to the set of lines of
resolution and the frequency range of the first data, and
processing the selected portion of the second data with the first
data sensing and processing system.
[0104] Methods and systems described herein for industrial machine
sensor data streaming, collection, processing, and storage may be
configured to operate and integrate with existing data collection,
processing and storage systems and may include a method for
automatically processing a portion of a stream of sensed data. The
sensed data is received from a first set of sensors deployed to
monitor aspects of an industrial machine associated with at least
one moving part of the machine. The sensed data is in response to
an electronic data structure that facilitates extracting a subset
of the stream of sensed data that corresponds to a set of sensed
data received from a second set of sensors deployed to monitor the
aspects of the industrial machine associated with the at least one
moving part of the machine. The set of sensed data is constrained
to a frequency range. The stream of sensed data includes a range of
frequencies that exceeds the frequency range of the set of sensed
data, the processing comprising executing an algorithm on a portion
of the stream of sensed data that is constrained to the frequency
range of the set of sensed data, the algorithm configured to
process the set of sensed data.
[0105] Methods and systems described herein for industrial machine
sensor data streaming, collection, processing, and storage may be
configured to operate and integrate with existing data collection,
processing and storage systems and may include a method for
receiving first data from sensors deployed to monitor aspects of an
industrial machine associated with at least one moving part of the
machine. This method may further include detecting at least one of
a frequency range and lines of resolution represented by the first
data; receiving a stream of data from sensors deployed to monitor
the aspects of the industrial machine associated with the at least
one moving part of the machine. The stream of data includes: (1) a
plurality of frequency ranges and a plurality of lines of
resolution that exceeds the frequency range and the lines of
resolution represented by the first data; (2) a set of data
extracted from the stream of data that corresponds to at least one
of the frequency range and the lines of resolution represented by
the first data; and (3) the extracted set of data which is
processed with a data processing algorithm that is configured to
process data within the frequency range and within the lines of
resolution of the first data.
BRIEF DESCRIPTION OF THE FIGURES
[0106] FIGS. 1 through FIG. 5 are diagrammatic views that each
depicts portions of an overall view of an industrial Internet of
Things (IoT) data collection, monitoring and control system in
accordance with the present disclosure.
[0107] FIG. 6 is a diagrammatic view of a platform including a
local data collection system disposed in an industrial environment
for collecting data from or about the elements of the environment,
such as machines, components, systems, sub-systems, ambient
conditions, states, workflows, processes, and other elements in
accordance with the present disclosure.
[0108] FIG. 7 is a diagrammatic view that depicts elements of an
industrial data collection system for collecting analog sensor data
in an industrial environment in accordance with the present
disclosure.
[0109] FIG. 8 is a diagrammatic view of a rotating or oscillating
machine having a data acquisition module that is configured to
collect waveform data in accordance with the present
disclosure.
[0110] FIG. 9 is a diagrammatic view of an exemplary tri-axial
sensor mounted to a motor bearing of an exemplary rotating machine
in accordance with the present disclosure.
[0111] FIG. 10 and FIG. 11 are diagrammatic views of an exemplary
tri-axial sensor and a single-axis sensor mounted to an exemplary
rotating machine in accordance with the present disclosure.
[0112] FIG. 12 is a diagrammatic view of multiple machines under
survey with ensembles of sensors in accordance with the present
disclosure.
[0113] FIG. 13 is a diagrammatic view of hybrid relational metadata
and a binary storage approach in accordance with the present
disclosure.
[0114] FIG. 14 through FIG. 18 are diagrammatic view of components
and interactions of a data collection architecture involving data
channel methods and systems for data collection of industrial
machines in accordance with the present disclosure.
[0115] FIG. 19 is a diagrammatic view that depicts embodiments of a
data monitoring device in accordance with the present
disclosure.
[0116] FIG. 20 and FIG. 21 are diagrammatic views that depict
embodiments of a data monitoring device in accordance with the
present disclosure.
[0117] FIG. 22 is a diagrammatic view that depicts embodiments of a
data monitoring device in accordance with the present
disclosure.
[0118] FIGS. 23 and 24 are diagrammatic views that depict an
embodiment of a system for data collection in accordance with the
present disclosure.
[0119] FIGS. 25 and 26 are diagrammatic views that depict an
embodiment of a system for data collection comprising a plurality
of data monitoring devices in accordance with the present
disclosure.
[0120] FIG. 27 depicts an embodiment of a data monitoring device
incorporating sensors in accordance with the present
disclosure.
[0121] FIGS. 28 and 29 are diagrammatic views that depict
embodiments of a data monitoring device in communication with
external sensors in accordance with the present disclosure.
[0122] FIG. 30 is a diagrammatic view that depicts embodiments of a
data monitoring device with additional detail in the signal
evaluation circuit in accordance with the present disclosure.
[0123] FIG. 31 is a diagrammatic view that depicts embodiments of a
data monitoring device with additional detail in the signal
evaluation circuit in accordance with the present disclosure.
[0124] FIG. 32 is a diagrammatic view that depicts embodiments of a
data monitoring device with additional detail in the signal
evaluation circuit in accordance with the present disclosure.
[0125] FIG. 33 is a diagrammatic view that depicts embodiments of a
system for data collection in accordance with the present
disclosure.
[0126] FIG. 34 is a diagrammatic view that depicts embodiments of a
system for data collection comprising a plurality of data
monitoring devices in accordance with the present disclosure.
[0127] FIG. 35 is a diagrammatic view that depicts embodiments of a
data monitoring device in accordance with the present
disclosure.
[0128] FIGS. 36 and 37 are diagrammatic views that depict
embodiments of a data monitoring device in accordance with the
present disclosure.
[0129] FIGS. 38 and 39 are diagrammatic views that depict
embodiments of a data monitoring device in accordance with the
present disclosure.
[0130] FIGS. 40 and 41 are diagrammatic views that depict
embodiments of a data monitoring device in accordance with the
present disclosure.
[0131] FIGS. 42 and 43 is a diagrammatic view that depicts
embodiments of a system for data collection comprising a plurality
of data monitoring devices in accordance with the present
disclosure.
[0132] FIG. 44 is a diagrammatic view that depicts embodiments of a
data monitoring device in accordance with the present
disclosure.
[0133] FIGS. 45 and 46 are diagrammatic views that depict
embodiments of a data monitoring device in accordance with the
present disclosure.
[0134] FIG. 47 is a diagrammatic view that depicts embodiments of a
data monitoring device in accordance with the present
disclosure.
[0135] FIG. 48 is a diagrammatic view that depicts embodiments of a
data monitoring device in accordance with the present
disclosure.
[0136] FIGS. 49 and 50 are diagrammatic views that depict
embodiments of a system for data collection in accordance with the
present disclosure.
[0137] FIGS. 51 and 52 are diagrammatic views that depict
embodiments of a system for data collection comprising a plurality
of data monitoring devices in accordance with the present
disclosure.
[0138] FIG. 53 is a diagrammatic view that depicts embodiments of a
data monitoring device in accordance with the present
disclosure.
[0139] FIGS. 54 and 55 are diagrammatic views that depict
embodiments of a data monitoring device in accordance with the
present disclosure.
[0140] FIG. 56 is a diagrammatic view that depicts embodiments of a
data monitoring device in accordance with the present
disclosure.
[0141] FIGS. 57 and 58 are diagrammatic views that depict
embodiments of a system for data collection in accordance with the
present disclosure.
[0142] FIGS. 59 and 60 are diagrammatic views that depict
embodiments of a system for data collection comprising a plurality
of data monitoring devices in accordance with the present
disclosure.
[0143] FIG. 61 is a diagrammatic view that depicts embodiments of a
data monitoring device in accordance with the present
disclosure.
[0144] FIGS. 62 and 63 are diagrammatic views that depict
embodiments of a data monitoring device in accordance with the
present disclosure.
[0145] FIG. 64 is a diagrammatic view that depicts embodiments of a
data monitoring device in accordance with the present
disclosure.
[0146] FIGS. 65 and 66 are diagrammatic views that depict
embodiments of a system for data collection in accordance with the
present disclosure.
[0147] FIGS. 67 and 68 are diagrammatic views that depict
embodiments of a system for data collection comprising a plurality
of data monitoring devices in accordance with the present
disclosure.
[0148] FIG. 69 is a diagrammatic view that depicts embodiments of a
data monitoring device in accordance with the present
disclosure.
[0149] FIG. 70 is a diagrammatic view that depicts embodiments of a
data monitoring device in accordance with the present
disclosure.
[0150] FIG. 71 is a diagrammatic view that depicts a monitoring
system that employs data collection bands in accordance with the
present disclosure.
[0151] FIG. 72 is a diagrammatic view that depicts a system that
employs vibration and other noise in predicting states and outcomes
in accordance with the present disclosure.
[0152] FIG. 73 is a schematic flow diagram of a procedure for data
collection in an industrial environment in accordance with the
present disclosure.
[0153] FIG. 74 is a diagrammatic view that depicts
industry-specific feedback in an industrial environment in
accordance with the present disclosure.
[0154] FIG. 75 is a diagrammatic view that depicts an exemplary
user interface for smart band configuration of a system for data
collection in an industrial environment is depicted in accordance
with the present disclosure.
[0155] FIG. 76 is a diagrammatic view that depicts a graphical
approach 11300 for back-calculation in accordance with the present
disclosure.
[0156] FIG. 77 is a diagrammatic view that depicts a wearable
haptic user interface device for providing haptic stimuli to a user
that is responsive to data collected in an industrial environment
by a system adapted to collect data in the industrial environment
in accordance with the present disclosure.
[0157] FIG. 78 is a diagrammatic view that depicts an augmented
reality display of heat maps based on data collected in an
industrial environment by a system adapted to collect data in the
environment in accordance with the present disclosure.
[0158] FIG. 79 is a diagrammatic view that depicts an augmented
reality display including real time data overlaying a view of an
industrial environment in accordance with the present
disclosure.
[0159] FIG. 80 is a diagrammatic view that depicts data collection
system according to some aspects of the present disclosure.
[0160] FIG. 81 is a diagrammatic view that depicts embodiments of a
storage time definition in accordance with the present
disclosure.
[0161] FIG. 82 is a diagrammatic view that depicts embodiments of a
data resolution description in accordance with the present
disclosure.
[0162] FIG. 83 is a diagrammatic view of an apparatus for
self-organizing network coding for data collection for an
industrial system in accordance with the present disclosure.
DETAILED DESCRIPTION
[0163] Detailed embodiments of the present disclosure are disclosed
herein; however, it is to be understood that the disclosed
embodiments are merely exemplary of the disclosure, which may be
embodied in various forms. Therefore, specific structural and
functional details disclosed herein are not to be interpreted as
limiting, but merely as a basis for the claims and as a
representative basis for teaching one skilled in the art to
variously employ the present disclosure in virtually any
appropriately detailed structure.
[0164] Methods and systems described herein for industrial machine
sensor data streaming, collection, processing, and storage may be
configured to operate with existing data collection, processing,
and storage systems while preserving access to existing
format/frequency range/resolution compatible data. While the
industrial machine sensor data streaming facilities described
herein may collect a greater volume of data (e.g., longer duration
of data collection) from sensors at a wider range of frequencies
and with greater resolution than existing data collection systems,
methods and systems may be employed to provide access to data from
the stream of data that represents one or more ranges of frequency
and/or one or more lines of resolution that are purposely
compatible with existing systems. Further, a portion of the
streamed data may be identified, extracted, stored, and/or
forwarded to existing data processing systems to facilitate
operation of existing data processing systems that substantively
matches operation of existing data processing systems using
existing collection-based data. In this way, a newly deployed
system for sensing aspects of industrial machines, such as aspects
of moving parts of industrial machines, may facilitate continued
use of existing sensed data processing facilities, algorithms,
models, pattern recognizers, user interfaces, and the like.
[0165] Through identification of existing frequency ranges,
formats, and/or resolution, such as by accessing a data structure
that defines these aspects of existing data, higher resolution
streamed data may be configured to represent a specific frequency,
frequency range, format, and/or resolution. This configured
streamed data can be stored in a data structure that is compatible
with existing sensed data structures so that existing processing
systems and facilities can access and process the data
substantially as if it were the existing data. One approach to
adapting streamed data for compatibility with existing sensed data
may include aligning the streamed data with existing data so that
portions of the streamed data that align with the existing data can
be extracted, stored, and made available for processing with
existing data processing methods. Alternatively, data processing
methods may be configured to process portions of the streamed data
that correspond, such as through alignment, to the existing data,
with methods that implement functions substantially similar to the
methods used to process existing data, such as methods that process
data that contain a particular frequency range or a particular
resolution and the like.
[0166] Methods used to process existing data may be associated with
certain characteristics of sensed data, such as certain frequency
ranges, sources of data, and the like. As an example, methods for
processing bearing sensing information for a moving part of an
industrial machine may be capable of processing data from bearing
sensors that fall into a particular frequency range. This method
can thusly be at least partially identifiable by these
characteristics of the data being processed. Therefore, given a set
of conditions, such as moving device being sensed, industrial
machine type, frequency of data being sensed, and the like, a data
processing system may select an appropriate method. Also, given
such a set of conditions, an industrial machine data sensing and
processing facility may configure elements, such as data filters,
routers, processors, and the like, to handle data meeting the
conditions.
[0167] FIGS. 1 through 5 depict portions of an overall view of an
industrial Internet of Things (IoT) data collection, monitoring and
control system 10. FIG. 2 depicts a mobile ad hoc network ("MANET")
20, which may form a secure, temporal network connection 22
(sometimes connected and sometimes isolated), with a cloud 30 or
other remote networking system, so that network functions may occur
over the MANET 20 within the environment, without the need for
external networks, but at other times information can be sent to
and from a central location. This allows the industrial environment
to use the benefits of networking and control technologies, while
also providing security, such as preventing cyber-attacks. The
MANET 20 may use cognitive radio technologies 40, including those
that form up an equivalent to the IP protocol, such as router 42,
MAC 44, and physical layer technologies 46. In certain embodiments,
the system depicted in FIGS. 1 through 5 provides network-sensitive
or network-aware transport of data over the network to and from a
data collection device or a heavy industrial machine.
[0168] FIGS. 3-4 depict intelligent data collection technologies
deployed locally, at the edge of an IoT deployment, where heavy
industrial machines are located. This includes various sensors 52,
IoT devices 54, data storage capabilities (e.g., data pools 60, or
distributed ledger 62) (including intelligent, self-organizing
storage), sensor fusion (including self-organizing sensor fusion),
and the like. Interfaces for data collection, including
multi-sensory interfaces, tablets, smartphones 58, and the like are
shown. FIG. 3 also shows data pools 60 that may collect data
published by machines or sensors that detect conditions of
machines, such as for later consumption by local or remote
intelligence. A distributed ledger system 62 may distribute storage
across the local storage of various elements of the environment, or
more broadly throughout the system. FIG. 4 also shows on-device
sensor fusion 80, such as for storing on a device data from
multiple analog sensors 82, which may be analyzed locally or in the
cloud, such as by machine learning 84, including by training a
machine based on initial models created by humans that are
augmented by providing feedback (such as based on measures of
success) when operating the methods and systems disclosed
herein.
[0169] FIG. 1 depicts a server based portion of an industrial IoT
system that may be deployed in the cloud or on an enterprise
owner's or operator's premises. The server portion includes network
coding (including self-organizing network coding and/or automated
configuration) that may configure a network coding model based on
feedback measures, network conditions, or the like, for highly
efficient transport of large amounts of data across the network to
and from data collection systems and the cloud. Network coding may
provide a wide range of capabilities for intelligence, analytics,
remote control, remote operation, remote optimization, various
storage configurations and the like, as depicted in FIG. 1. The
various storage configurations may include distributed ledger
storage for supporting transactional data or other elements of the
system.
[0170] FIG. 5 depicts a programmatic data marketplace 70, which may
be a self-organizing marketplace, such as for making available data
that is collected in industrial environments, such as from data
collectors, data pools, distributed ledgers, and other elements
disclosed herein. Additional detail on the various components and
sub-components of FIGS. 1 through 5 is provided throughout this
disclosure.
[0171] With reference to FIG. 6, an embodiment of platform 100 may
include a local data collection system 102, which may be disposed
in an environment 104, such as an industrial environment similar to
that shown in FIG. 3, for collecting data from or about the
elements of the environment, such as machines, components, systems,
sub-systems, ambient conditions, states, workflows, processes, and
other elements. The platform 100 may connect to or include portions
of the industrial IoT data collection, monitoring and control
system 10 depicted in FIGS. 1-5. The platform 100 may include a
network data transport system 108, such as for transporting data to
and from the local data collection system 102 over a network 110,
such as to a host processing system 112, such as one that is
disposed in a cloud computing environment or on the premises of an
enterprise, or that consists of distributed components that
interact with each other to process data collected by the local
data collection system 102. The host processing system 112,
referred to for convenience in some cases as the host system 112,
may include various systems, components, methods, processes,
facilities, and the like for enabling automated, or
automation-assisted processing of the data, such as for monitoring
one or more environments 104 or networks 110 or for remotely
controlling one or more elements in a local environment 104 or in a
network 110. The platform 100 may include one or more local
autonomous systems, such as for enabling autonomous behavior, such
as reflecting artificial, or machine-based intelligence or such as
enabling automated action based on the applications of a set of
rules or models upon input data from the local data collection
system 102 or from one or more input sources 116, which may
comprise information feeds and inputs from a wide array of sources,
including those in the local environment 104, in a network 110, in
the host system 112, or in one or more external systems, databases,
or the like. The platform 100 may include one or more intelligent
systems 118, which may be disposed in, integrated with, or acting
as inputs to one or more components of the platform 100. Details of
these and other components of the platform 100 are provided
throughout this disclosure.
[0172] Intelligent systems 118 may include cognitive systems 120,
such as enabling a degree of cognitive behavior as a result of the
coordination of processing elements, such as mesh, peer-to-peer,
ring, serial, and other architectures, where one or more node
elements is coordinated with other node elements to provide
collective, coordinated behavior to assist in processing,
communication, data collection, or the like. The MANET 20 depicted
in FIG. 2 may also use cognitive radio technologies, including
those that form up an equivalent to the IP protocol, such as router
42, MAC 44, and physical layer technologies 46. In one example, the
cognitive system technology stack can include examples disclosed in
U.S. Pat. No. 8,060,017 to Schlicht et al., issued 15 Nov. 2011 and
hereby incorporated by reference as if fully set forth herein.
[0173] Intelligent systems may include machine learning systems
122, such as for learning on one or more data sets. The one or more
data sets may include information collected using local data
collection systems 102 or other information from input sources 116,
such as to recognize states, objects, events, patterns, conditions,
or the like that may, in turn, be used for processing by the host
system 112 as inputs to components of the platform 100 and portions
of the industrial IoT data collection, monitoring and control
system 10, or the like. Learning may be human-supervised or
fully-automated, such as using one or more input sources 116 to
provide a data set, along with information about the item to be
learned. Machine learning may use one or more models, rules,
semantic understandings, workflows, or other structured or
semi-structured understanding of the world, such as for automated
optimization of control of a system or process based on feedback or
feed forward to an operating model for the system or process. One
such machine learning technique for semantic and contextual
understandings, workflows, or other structured or semi-structured
understandings is disclosed in U.S. Pat. No. 8,200,775 to Moore,
issued 12 Jun. 2012, and hereby incorporated by reference as if
fully set forth herein. Machine learning may be used to improve the
foregoing, such as by adjusting one or more weights, structures,
rules, or the like (such as changing a function within a model)
based on feedback (such as regarding the success of a model in a
given situation) or based on iteration (such as in a recursive
process). Where sufficient understanding of the underlying
structure or behavior of a system is not known, insufficient data
is not available, or in other cases where preferred for various
reasons, machine learning may also be undertaken in the absence of
an underlying model; that is, input sources may be weighted,
structured, or the like within a machine learning facility without
regard to any a priori understanding of structure, and outcomes
(such as those based on measures of success at accomplishing
various desired objectives) can be serially fed to the machine
learning system to allow it to learn how to achieve the targeted
objectives. For example, the system may learn to recognize faults,
to recognize patterns, to develop models or functions, to develop
rules, to optimize performance, to minimize failure rates, to
optimize profits, to optimize resource utilization, to optimize
flow (such as flow of traffic), or to optimize many other
parameters that may be relevant to successful outcomes (such as
outcomes in a wide range of environments). Machine learning may use
genetic programming techniques, such as promoting or demoting one
or more input sources, structures, data types, objects, weights,
nodes, links, or other factors based on feedback (such that
successful elements emerge over a series of generations). For
example, alternative available sensor inputs for a data collection
system 102 may be arranged in alternative configurations and
permutations, such that the system may, using generic programming
techniques over a series of data collection events, determine what
permutations provide successful outcomes based on various
conditions (such as conditions of components of the platform 100,
conditions of the network 110, conditions of a data collection
system 102, conditions of an environment 104), or the like. In
embodiments, local machine learning may turn on or off one or more
sensors in a multi-sensor data collector 102 in permutations over
time, while tracking success outcomes such as contributing to
success in predicting a failure, contributing to a performance
indicator (such as efficiency, effectiveness, return on investment,
yield, or the like), contributing to optimization of one or more
parameters, identification of a pattern (such as relating to a
threat, a failure mode, a success mode, or the like) or the like.
For example, a system may learn what sets of sensors should be
turned on or off under given conditions to achieve the highest
value utilization of a data collector 102. In embodiments, similar
techniques may be used to handle optimization of transport of data
in the platform 100 (such as in the network 110) by using generic
programming or other machine learning techniques to learn to
configure network elements (such as configuring network transport
paths, configuring network coding types and architectures,
configuring network security elements), and the like.
[0174] In embodiments, the local data collection system 102 may
include a high-performance, multi-sensor data collector having a
number of novel features for collection and processing of analog
and other sensor data. In embodiments, a local data collection
system 102 may be deployed to the industrial facilities depicted in
FIG. 3. A local data collection system 102 may also be deployed
monitor other machines such as the machine 2300 in FIG. 9 and FIG.
10, the machines 2400, 2600, 2800, 2950, 3000 depicted in FIG. 12,
and the machines 3202, 3204 depicted in FIG. 13. The data
collection system 102 may have on-board intelligent systems 118
(such as for learning to optimize the configuration and operation
of the data collector, such as configuring permutations and
combinations of sensors based on contexts and conditions). In one
example, the data collection system 102 includes a crosspoint
switch 130 or other analog switch. Automated, intelligent
configuration of the local data collection system 102 may be based
on a variety of types of information, such as information from
various input sources, including those based on available power,
power requirements of sensors, the value of the data collected
(such as based on feedback information from other elements of the
platform 100), the relative value of information (such as values
based on the availability of other sources of the same or similar
information), power availability (such as for powering sensors),
network conditions, ambient conditions, operating states, operating
contexts, operating events, and many others.
[0175] FIG. 7 shows elements and sub-components of a data
collection and analysis system 1100 for sensor data (such as analog
sensor data) collected in industrial environments. As depicted in
FIG. 7, embodiments of the methods and systems disclosed herein may
include hardware that has several different modules starting with
the multiplexer ("MUX") main board 1104. In embodiments, there may
be a MUX option board 1108. The MUX 114 main board is where the
sensors connect to the system. These connections are on top to
enable ease of installation. Then there are numerous settings on
the underside of this board as well as on the Mux option board
1108, which attaches to the MUX main board 1104 via two headers one
at either end of the board. In embodiments, the Mux option board
has the male headers, which mesh together with the female header on
the main Mux board. This enables them to be stacked on top of each
other taking up less real estate.
[0176] In embodiments, the main Mux board and/or the MUX option
board then connects to the mother (e.g., with 4 simultaneous
channels) and daughter (e.g., with 4 additional channels for 8
total channels) analog boards 1110 via cables where some of the
signal conditioning (such as hardware integration) occurs. The
signals then move from the analog boards 1110 to an anti-aliasing
board (not shown) where some of the potential aliasing is removed.
The rest of the aliasing removal is done on the delta sigma board
1112. The delta sigma board 1112 provides more aliasing protection
along with other conditioning and digitizing of the signal. Next,
the data moves to the Jennic.TM. board 1114 for more digitizing as
well as communication to a computer via USB or Ethernet. In
embodiments, the Jennic.TM. board 1114 may be replaced with a pic
board 1118 for more advanced and efficient data collection as well
as communication. Once the data moves to the computer software
1102, the computer software 1102 can manipulate the data to show
trending, spectra, waveform, statistics, and analytics.
[0177] In embodiments, the system is meant to take in all types of
data from volts to 4-20 mA signals In embodiments, open formats of
data storage and communication may be used. In some instances,
certain portions of the system may be proprietary especially some
of research and data associated with the analytics and reporting.
In embodiments, smart band analysis is a way to break data down
into easily analyzed parts that can be combined with other smart
bands to make new more simplified yet sophisticated analytics. In
embodiments, this unique information is taken and graphics are used
to depict the conditions because picture depictions are more
helpful to the user. In embodiments, complicated programs and user
interfaces are simplified so that any user can manipulate the data
like an expert.
[0178] In embodiments, the system in essence, works in a big loop.
The system starts in software with a general user interface ("GUI")
1124. In embodiments, rapid route creation may take advantage of
hierarchical templates. In embodiments, a GUI is created so any
general user can populate the information itself with simple
templates. Once the templates are created the user can copy and
paste whatever the user needs. In addition, users can develop their
own templates for future ease of use and to institutionalize the
knowledge. When the user has entered all of the user's information
and connected all of the user's sensors, the user can then start
the system acquiring data.
[0179] Embodiments of the methods and systems disclosed herein may
include unique electrostatic protection for trigger and vibration
inputs. In many critical industrial environments where large
electrostatic forces, which can harm electrical equipment, may
build up, for example rotating machinery or low-speed balancing
using large belts, proper transducer and trigger input protection
is required. In embodiments, a low-cost but efficient method is
described for such protection without the need for external
supplemental devices.
[0180] Typically, vibration data collectors are not designed to
handle large input voltages due to the expense and the fact that,
more often than not, it is not needed. A need exists for these data
collectors to acquire many varied types of RPM data as technology
improves and monitoring costs plummet. In embodiments, a method is
using the already established OptoMOS.TM. technology which permits
the switching up front of high voltage signals rather than using
more conventional reed-relay approaches. Many historic concerns
regarding non-linear zero crossing or other non-linear solid-state
behaviors have been eliminated with regard to the passing through
of weakly buffered analog signals In addition, in embodiments,
printed circuit board routing topologies place all of the
individual channel input circuitry as close to the input connector
as possible. In embodiments, a unique electrostatic protection for
trigger and vibration inputs may be placed upfront on the Mux and
DAQ hardware in order to dissipate the built up electric charge as
the signal passed from the sensor to the hardware. In embodiments,
the Mux and analog board may support high-amperage input using a
design topology comprising wider traces and solid state relays for
upfront circuitry.
[0181] In some systems multiplexers are afterthoughts and the
quality of the signal coming from the multiplexer is not
considered. As a result of a poor quality multiplexer, the quality
of the signal can drop as much as 30 dB or more. Thus, substantial
signal quality may be lost using a 24-bit DAQ that has a signal to
noise ratio of 110 dB and if the signal to noise ratio drops to 80
dB in the Mux, it may not be much better than a 16-bit system from
20 years ago. In embodiments of this system, an important part at
the front of the Mux is upfront signal conditioning on Mux for
improved signal-to-noise ratio. Embodiments may perform signal
conditioning (such as range/gain control, integration, filtering,
etc.) on vibration as well as other signal inputs up front before
Mux switching to achieve the highest signal-to-noise ratio.
[0182] In embodiments, in addition to providing a better signal,
the multiplexer may provide a continuous monitor alarming feature.
Truly continuous systems monitor every sensor all the time but tend
to be expensive. Typical multiplexer systems only monitor a set
number of channels at one time and switch from bank to bank of a
larger set of sensors. As a result, the sensors not being currently
collected are not being monitored; if a level increases the user
may never know. In embodiments, a multiplexer may have a continuous
monitor alarming feature by placing circuitry on the multiplexer
that can measure input channel levels against known alarm
conditions even when the data acquisition ("DAQ") is not monitoring
the input. In embodiments, continuous monitoring Mux bypass offers
a mechanism whereby channels not being currently sampled by the Mux
system may be continuously monitored for significant alarm
conditions via a number of trigger conditions using filtered
peak-hold circuits or functionally similar that are in turn passed
on to the monitoring system in an expedient manner using hardware
interrupts or other means. This, in essence, makes the system
continuously monitoring, although without the ability to instantly
capture data on the problem like a true continuous system. In
embodiments, coupling this capability to alarm with adaptive
scheduling techniques for continuous monitoring and the continuous
monitoring system's software adapting and adjusting the data
collection sequence based on statistics, analytics, data alarms and
dynamic analysis may allow the system to quickly collect dynamic
spectral data on the alarming sensor very soon after the alarm
sounds.
[0183] Another restriction of typical multiplexers is that they may
have a limited number of channels. In embodiments, use of
distributed complex programmable logic device ("CPLD") chips with
dedicated bus for logic control of multiple Mux and data
acquisition sections enables a CPLD to control multiple mux and
DAQs so that there is no limit to the number of channels a system
can handle Interfacing to multiple types of predictive maintenance
and vibration transducers requires a great deal of switching. This
includes AC/DC coupling, 4-20 interfacing, integrated electronic
piezoelectric transducer, channel power-down (for conserving op-amp
power), single-ended or differential grounding options, and so on.
Also required is the control of digital pots for range and gain
control, switches for hardware integration, AA filtering and
triggering. This logic can be performed by a series of CPLD chips
strategically located for the tasks they control. A single giant
CPLD requires long circuit routes with a great deal of density at
the single giant CPLD. In embodiments, distributed CPLDs not only
address these concerns but offer a great deal of flexibility. A bus
is created where each CPLD that has a fixed assignment has its own
unique device address. In embodiments, multiplexers and DAQs can
stack together offering additional input and output channels to the
system. For multiple boards (e.g., for multiple Mux boards),
jumpers are provided for setting multiple addresses. In another
example, three bits permit up to 8 boards that are jumper
configurable. In embodiments, a bus protocol is defined such that
each CPLD on the bus can either be addressed individually or as a
group.
[0184] Typical multiplexers may be limited to collecting only
sensors in the same bank. For detailed analysis, this may be
limiting as there is tremendous value in being able to
simultaneously review data from sensors on the same machine.
Current systems using conventional fixed bank multiplexers can only
compare a limited number of channels (based on the number of
channels per bank) that were assigned to a particular group at the
time of installation. The only way to provide some flexibility is
to either overlap channels or incorporate lots of redundancy in the
system both of which can add considerable expense (in some cases an
exponential increase in cost versus flexibility). The simplest Mux
design selects one of many inputs and routes it into a single
output line. A banked design would consist of a group of these
simple building blocks, each handling a fixed group of inputs and
routing to its respective output. Typically, the inputs are not
overlapping so that the input of one Mux grouping cannot be routed
into another. Unlike conventional Mux chips which typically switch
a fixed group or banks of a fixed selection of channels into a
single output (e.g., in groups of 2, 4, 8, etc.), a cross point Mux
allows the user to assign any input to any output. Previously,
crosspoint multiplexers were used for specialized purposes such as
RGB digital video applications and were as a practical matter too
noisy for analog applications such as vibration analysis; however
more recent advances in the technology now make it feasible.
Another advantage of the crosspoint Mux is the ability to disable
outputs by putting them into a high impedance state. This is ideal
for an output bus so that multiple Mux cards may be stacked, and
their output buses joined together without the need for bus
switches.
[0185] In embodiments, this may be addressed by use of an analog
crosspoint switch for collecting variable groups of vibration input
channels and providing a matrix circuit so the system may access
any set of eight channels from the total number of input
sensors.
[0186] In embodiments, the ability to control multiple multiplexers
with use of distributed CPLD chips with dedicated bus for logic
control of multiple Mux and data acquisition sections is enhanced
with a hierarchical multiplexer which allows for multiple DAQ to
collect data from multiple multiplexers. A hierarchical Mux may
allow modularly output of more channels, such as 16, 24 or more to
multiple of eight channel card sets. In embodiments, this allows
for faster data collection as well as more channels of simultaneous
data collection for more complex analysis. In embodiments, the Mux
may be configured slightly to make it portable and use data
acquisition parking features, which turns SV3X DAQ into a protected
system embodiment.
[0187] In embodiments, once the signals leave the multiplexer and
hierarchical Mux they move to the analog board where there are
other enhancements. In embodiments, power saving techniques may be
used such as: power-down of analog channels when not in use;
powering down of component boards; power-down of analog signal
processing op-amps for non-selected channels ; powering down
channels on the mother and the daughter analog boards. The ability
to power down component boards and other hardware by the low-level
firmware for the DAQ system makes high-level application control
with respect to power-saving capabilities relatively easy. Explicit
control of the hardware is always possible but not required by
default. In embodiments, this power saving benefit may be of value
to a protected system, especially if it is battery operated or
solar powered.
[0188] In embodiments, in order to maximize the signal to noise
ratio and provide the best data, a peak-detector for auto-scaling
routed into a separate A/D will provide the system the highest peak
in each set of data so it can rapidly scale the data to that peak.
For vibration analysis purposes, the built-in A/D convertors in
many microprocessors may be inadequate with regards to number of
bits, number of channels or sampling frequency versus not slowing
the microprocessor down significantly. Despite these limitations,
it is useful to use them for purposes of auto-scaling. In
embodiments, a separate A/D may be used that has reduced
functionality and is cheaper. For each channel of input, after the
signal is buffered (usually with the appropriate coupling: AC or
DC) but before it is signal conditioned, the signal is fed directly
into the microprocessor or low-cost A/D. Unlike the conditioned
signal for which range, gain and filter switches are thrown, no
switches are varied. This permits the simultaneous sampling of the
auto-scaling data while the input data is signal conditioned, fed
into a more robust external A/D, and directed into on-board memory
using direct memory access (DMA) methods where memory is accessed
without requiring a CPU. This significantly simplifies the
auto-scaling process by not having to throw switches and then allow
for settling time, which greatly slows down the auto-scaling
process. Furthermore, the data may be collected simultaneously,
which assures the best signal-to-noise ratio. The reduced number of
bits and other features is usually more than adequate for
auto-scaling purposes. In embodiments, improved integration using
both analog and digital methods create an innovative hybrid
integration which also improves or maintains the highest possible
signal to noise ratio.
[0189] In embodiments, a section of the analog board may allow
routing of a trigger channel, either raw or buffered, into other
analog channels. This may allow a user to route the trigger to any
of the channels for analysis and trouble shooting. Systems may have
trigger channels for the purposes of determining relative phase
between various input data sets or for acquiring significant data
without the needless repetition of unwanted input. In embodiments,
digitally controlled relays may be used to switch either the raw or
buffered trigger signal into one of the input channels. It may be
desirable to examine the quality of the triggering pulse because it
may be corrupted for a variety of reasons including inadequate
placement of the trigger sensor, wiring issues, faulty setup issues
such as a dirty piece of reflective tape if using an optical
sensor, and so on. The ability to look at either the raw or
buffered signal may offer an excellent diagnostic or debugging
vehicle. It also can offer some improved phase analysis capability
by making use of the recorded data signal for various signal
processing techniques such as variable speed filtering
algorithms.
[0190] In embodiments, once the signals leave the analog board, the
signals move into the delta-sigma board where precise voltage
reference for A/D zero reference offers more accurate direct
current sensor data. The delta sigma's high speeds also provide for
using higher input oversampling for delta-sigma A/D for lower
sampling rate outputs to minimize antialiasing filter requirements.
Lower oversampling rates can be used for higher sampling rates. For
example, a 3.sup.rd order AA filter set for the lowest sampling
requirement for 256 Hz (Fmax of 100 Hz) is then adequate for Fmax
ranges of 200 and 500 Hz. Another higher-cutoff AA filter can then
be used for Fmax ranges from 1 kHz and higher (with a secondary
filter kicking in at 2.56.times. the highest sampling rate of 128
kHz). In embodiments, a CPLD may be used as a clock-divider for a
delta-sigma A/D to achieve lower sampling rates without the need
for digital resampling. In embodiments, a high-frequency crystal
reference can be divided down to lower frequencies by employing a
CPLD as a programmable clock divider. The accuracy of the divided
down lower frequencies is even more accurate than the original
source relative to their longer time periods. This also minimizes
or removes the need for resampling processing by the delta-sigma
A/D.
[0191] In embodiments, the data then moves from the delta-sigma
board to the Jennic.TM. board where phase relative to input and
trigger channels using on-board timers may be digitally derived. In
embodiments, the Jennic.TM. board also has the ability to store
calibration data and system maintenance repair history data in an
on-board card set. In embodiments, the Jennic.TM. board will enable
acquiring long blocks of data at high-sampling rate as opposed to
multiple sets of data taken at different sampling rates so it can
stream data and acquire long blocks of data for advanced analysis
in the future.
[0192] In embodiments, after the signal moves through the
Jennic.TM. board it may then be transmitted to the computer. In
embodiments, the computer software will be used to add intelligence
to the system starting with an expert system GUI. The GUI will
offer a graphical expert system with simplified user interface for
defining smart bands and diagnoses which facilitate anyone to
develop complex analytics. In embodiments, this user interface may
revolve around smart bands, which are a simplified approach to
complex yet flexible analytics for the general user. In
embodiments, the smart bands may pair with a self-learning neural
network for an even more advanced analytical approach. In
embodiments, this system may use the machine's hierarchy for
additional analytical insight. One critical part of predictive
maintenance is the ability to learn from known information during
repairs or inspections. In embodiments, graphical approaches for
back calculations may improve the smart bands and correlations
based on a known fault or problem.
[0193] In embodiments, there is a smart route which adapts which
sensors it collects simultaneously in order to gain additional
correlative intelligence. In embodiments, smart operational data
store ("ODS") allows the system to elect to gather data to perform
operational deflection shape analysis in order to further examine
the machinery condition. In embodiments, adaptive scheduling
techniques allow the system to change the scheduled data collected
for full spectral analysis across a number (e.g., eight), of
correlative channels. In embodiments, the system may provide data
to enable extended statistics capabilities for continuous
monitoring as well as ambient local vibration for analysis that
combines ambient temperature and local temperature and vibration
levels changes for identifying machinery issues.
[0194] In embodiments, a data acquisition device may be controlled
by a personal computer (PC) to implement the desired data
acquisition commands In embodiments, the DAQ box may be
self-sufficient. and can acquire, process, analyze and monitor
independent of external PC control. Embodiments may include secure
digital (SD) card storage. In embodiments, significant additional
storage capability may be provided by utilizing an SD card.. This
may prove critical for monitoring applications where critical data
may be stored permanently. Also, if a power failure should occur,
the most recent data may be stored despite the fact that it was not
off-loaded to another system.
[0195] A current trend has been to make DAQ systems as
communicative as possible with the outside world usually in the
form of networks including wireless. In the past it was common to
use a dedicated bus to control a DAQ system with either a
microprocessor or microcontroller/microprocessor paired with a PC.
In embodiments, a DAQ system may comprise one or more
microprocessor/microcontrollers, specialized
microcontrollers/microprocessors, or dedicated processors focused
primarily on the communication aspects with the outside world.
These include USB, Ethernet and wireless with the ability to
provide an IP address or addresses in order to host a webpage. All
communications with the outside world are then accomplished using a
simple text based menu. The usual array of commands (in practice
more than a hundred) such as InitializeCard, AcquireData,
StopAcquisition, RetrieveCalibration Info, and so on, would be
provided.
[0196] In embodiments, intense signal processing activities
including resampling, weighting, filtering, and spectrum processing
may be performed by dedicated processors such as field-programmable
gate array ("FPGAs"), digital signal processor ("DSP"),
microprocessors, micro-controllers, or a combination thereof. In
embodiments, this subsystem may communicate via a specialized
hardware bus with the communication processing section. It will be
facilitated with dual-port memory, semaphore logic, and so on. This
embodiment will not only provide a marked improvement in efficiency
but can significantly improve the processing capability, including
the streaming of the data as well other high-end analytical
techniques. This negates the need for constantly interrupting the
main processes which include the control of the signal conditioning
circuits, triggering, raw data acquisition using the A/D, directing
the A/D output to the appropriate on-board memory and processing
that data.
[0197] Embodiments may include sensor overload identification. A
need exists for monitoring systems to identify when the sensor is
overloading. There may be situations involving high-frequency
inputs that will saturate a standard 100 mv/g sensor (which is most
commonly used in the industry) and having the ability to sense the
overload improves data quality for better analysis. A monitoring
system may identify when their system is overloading, but in
embodiments, the system may look at the voltage of the sensor to
determine if the overload is from the sensor, enabling the user to
get another sensor better suited to the situation, or gather the
data again.
[0198] Embodiments may include radio frequency identification
("RFID") and an inclinometer or accelerometer on a sensor so the
sensor can indicate what machine/bearing it is attached to and what
direction such that the software can automatically store the data
without the user input. In embodiments, users could put the system
on any machine or machines and the system would automatically set
itself up and be ready for data collection in seconds.
[0199] Embodiments may include ultrasonic online monitoring by
placing ultrasonic sensors inside transformers, motor control
centers, breakers and the like and monitoring, via a sound
spectrum, continuously looking for patterns that identify arcing,
corona and other electrical issues indicating a break down or
issue. Embodiments may include providing continuous ultrasonic
monitoring of rotating elements and bearings of an energy
production facility. In embodiments, an analysis engine may be used
in ultrasonic online monitoring as well as identifying other faults
by combining the ultrasonic data with other parameters such as
vibration, temperature, pressure, heat flux, magnetic fields,
electrical fields, currents, voltage, capacitance, inductance, and
combinations (e.g., simple ratios) of the same, among many
others.
[0200] Embodiments of the methods and systems disclosed herein may
include use of an analog crosspoint switch for collecting variable
groups of vibration input channels. For vibration analysis, it is
useful to obtain multiple channels simultaneously from vibration
transducers mounted on different parts of a machine (or machines)
in multiple directions. By obtaining the readings at the same time,
for example, the relative phases of the inputs may be compared for
the purpose of diagnosing various mechanical faults. Other types of
cross channel analyses such as cross-correlation, transfer
functions, Operating Deflection Shape ("ODS") may also be
performed.
[0201] Embodiments of the methods and systems disclosed herein may
include precise voltage reference for A/D zero reference. Some A/D
chips provide their own internal zero voltage reference to be used
as a mid-scale value for external signal conditioning circuitry to
ensure that both the A/D and external op-amps use the same
reference. Although this sounds reasonable in principle, there are
practical complications. In many cases these references are
inherently based on a supply voltage using a resistor-divider. For
many current systems, especially those whose power is derived from
a PC via USB or similar bus, this provides for an unreliable
reference, as the supply voltage will often vary quite
significantly with load. This is especially true for delta-sigma
A/D chips which necessitate increased signal processing. Although
the offsets may drift together with load, a problem arises if one
wants to calibrate the readings digitally. It is typical to modify
the voltage offset expressed as counts coming from the A/D
digitally to compensate for the DC drift. However, for this case,
if the proper calibration offset is determined for one set of
loading conditions, they will not apply for other conditions. An
absolute DC offset expressed in counts will no longer be
applicable. As a result, it becomes necessary to calibrate for all
loading conditions which becomes complex, unreliable, and
ultimately unmanageable. In embodiments, an external voltage
reference is used which is simply independent of the supply voltage
to use as the zero offset.
[0202] In embodiments, the system provides a phase-lock-loop band
pass tracking filter method for obtaining slow-speed RPMs and phase
for balancing purposes to remotely balance slow speed machinery,
such as in paper mills, as well as offering additional analysis
from its data. For balancing purposes, it is sometimes necessary to
balance at very slow speeds. A typical tracking filter may be
constructed based on a phase-lock loop or PLL design; however,
stability and speed range are overriding concerns. In embodiments,
a number of digitally controlled switches are used for selecting
the appropriate RC and damping constants. The switching can be done
all automatically after measuring the frequency of the incoming
tach signal. Embodiments of the methods and systems disclosed
herein may include digital derivation of phase relative to input
and trigger channels using on-board timers. In embodiments, digital
phase derivation uses digital timers to ascertain an exact delay
from a trigger event to the precise start of data acquisition. This
delay, or offset, then, is further refined using interpolation
methods to obtain an even more precise offset which is then applied
to the analytically determined phase of the acquired data such that
the phase is "in essence" an absolute phase with precise mechanical
meaning useful for among other things, one-shot balancing,
alignment analysis, and so on.
[0203] Embodiments of the methods and systems disclosed herein may
include signal processing firmware/hardware. In embodiments, long
blocks of data may be acquired at high-sampling rate as opposed to
multiple sets of data taken at different sampling rates. Typically,
in modern route collection for vibration analysis, it is customary
to collect data at a fixed sampling rate with a specified data
length. The sampling rate and data length may vary from route point
to point based on the specific mechanical analysis requirements at
hand For example, a motor may require a relatively low sampling
rate with high resolution to distinguish running speed harmonics
from line frequency harmonics. The practical trade-off here though
is that it takes more collection time to achieve this improved
resolution. In contrast, some high-speed compressors or gear sets
require much higher sampling rates to measure the amplitudes of
relatively higher frequency data although the precise resolution
may not be as necessary. Ideally, however, it would be better to
collect a very long sample length of data at a very high-sampling
rate. When digital acquisition devices were first popularized in
the early 1980's, the A/D sampling, digital storage, and
computational abilities were not close to what they are today, so
compromises were made between the time required for data collection
and the desired resolution and accuracy. It was because of this
limitation that some analysts in the field even refused to give up
their analog tape recording systems, which did not suffer as much
from these same digitizing drawbacks. A few hybrid systems were
employed that would digitize the play back of the recorded analog
data at multiple sampling rates and lengths desired, though these
systems were admittedly less automated. The more common approach,
as mentioned earlier, is to balance data collection time with
analysis capability and digitally acquire the data blocks at
multiple sampling rates and sampling lengths and digitally store
these blocks separately. In embodiments, a long data length of data
can be collected at the highest practical sampling rate (e.g.,
102.4 kHz; corresponding to a 40 kHz Fmax) and stored. This long
block of data can be acquired in the same amount of time as the
shorter length of the lower sampling rates utilized by a priori
methods so that there is no effective delay added to the sampling
at the measurement point, always a concern in route collection. In
embodiments, analog tape recording of data is digitally simulated
with such a precision that it can be in effect considered
continuous or "analog" for many purposes, including for purposes of
embodiments of the present disclosure, except where context
indicates otherwise.
[0204] Embodiments of the methods and systems disclosed herein may
include storage of calibration data and maintenance history
on-board card sets. Many data acquisition devices which rely on
interfacing to a PC to function store their calibration
coefficients on the PC. This is especially true for complex data
acquisition devices whose signal paths are many and therefore whose
calibration tables can be quite large. In embodiments, calibration
coefficients are stored in flash memory which will remember this
data or any other significant information for that matter, for all
practical purposes, permanently. This information may include
nameplate information such as serial numbers of individual
components, firmware or software version numbers, maintenance
history, and the calibration tables. In embodiments, no matter
which computer the box is ultimately connected to, the DAQ box
remains calibrated and continues to hold all of this critical
information. The PC or external device may poll for this
information at any time for implantation or information exchange
purposes.
[0205] Embodiments of the methods and systems disclosed herein may
include rapid route creation taking advantage of hierarchical
templates. In the field of vibration monitoring, as well as
parametric monitoring in general, it is necessary to establish in a
database or functional equivalent the existence of data monitoring
points. These points are associated a variety of attributes
including the following categories: transducer attributes, data
collection settings, machinery parameters and operating parameters.
The transducer attributes would include probe type, probe mounting
type and probe mounting direction or axis orientation. Data
collection attributes associated with the measurement would involve
a sampling rate, data length, integrated electronic piezoelectric
probe power and coupling requirements, hardware integration
requirements, 4-20 or voltage interfacing, range and gain settings
(if applicable), filter requirements, and so on. Machinery
parametric requirements relative to the specific point would
include such items as operating speed, bearing type, bearing
parametric data which for a rolling element bearing includes the
pitch diameter, number of balls, inner race, and outer-race
diameters. For a tilting pad bearing, this would include the number
of pads and so on. For measurement points on a piece of equipment
such as a gearbox, needed parameters would include, for example,
the number of gear teeth on each of the gears. For induction
motors, it would include the number of rotor bars and poles; for
compressors, the number of blades and/or vanes; for fans, the
number of blades. For belt/pulley systems, the number of belts as
well as the relevant belt-passing frequencies may be calculated
from the dimensions of the pulleys and pulley center-to-center
distance. For measurements near couplings, the coupling type and
number of teeth in a geared coupling may be necessary, and so on.
Operating parametric data would include operating load, which may
be expressed in megawatts, flow (either air or fluid), percentage,
horsepower, feet-per-minute, and so on. Operating temperatures both
ambient and operational, pressures, humidity, and so on, may also
be relevant. As can be seen, the setup information required for an
individual measurement point can be quite large. It is also crucial
to performing any legitimate analysis of the data. Machinery,
equipment, and bearing specific information are essential for
identifying fault frequencies as well as anticipating the various
kinds of specific faults to be expected. The transducer attributes
as well as data collection parameters are vital for properly
interpreting the data along with providing limits for the type of
analytical techniques suitable. The traditional means of entering
this data has been manual and quite tedious, usually at the lowest
hierarchical level (for example, at the bearing level with regards
to machinery parameters), and at the transducer level for data
collection setup information. It cannot be stressed enough,
however, the importance of the hierarchical relationships necessary
to organize data--both for analytical and interpretive purposes as
well as the storage and movement of data. Here, we are focusing
primarily on the storage and movement of data. By its nature, the
aforementioned setup information is extremely redundant at the
level of the lowest hierarchies; however, because of its strong
hierarchical nature, it can be stored quite efficiently in that
form. In embodiments, hierarchical nature can be utilized when
copying data in the form of templates. As an example, hierarchical
storage structure suitable for many purposes is defined from
general to specific of company, plant or site, unit or process,
machine, equipment, shaft element, bearing, and transducer. It is
much easier to copy data associated with a particular machine,
piece of equipment, shaft element or bearing than it is to copy
only at the lowest transducer level. In embodiments, the system not
only stores data in this hierarchical fashion, but robustly
supports the rapid copying of data using these hierarchical
templates. Similarity of elements at specific hierarchical levels
lends itself to effective data storage in hierarchical format. For
example, so many machines have common elements such as motors,
gearboxes, compressors, belts, fans, and so on. More specifically,
many motors can be easily classified as induction, DC, fixed or
variable speed. Many gearboxes can be grouped into commonly
occurring groupings such as input/output, input pinion/intermediate
pinion/output pinion, 4-posters, and so on. Within a plant or
company, there are many similar types of equipment purchased and
standardized on for both cost and maintenance reasons. This results
in an enormous overlapping of similar types of equipment and, as a
result, offers a great opportunity for taking advantage of a
hierarchical template approach.
[0206] Embodiments of the methods and systems disclosed herein may
include smart bands. Smart bands refer to any processed signal
characteristics derived from any dynamic input or group of inputs
for the purposes of analyzing the data and achieving the correct
diagnoses. Furthermore, smart bands may even include mini or
relatively simple diagnoses for the purposes of achieving a more
robust and complex one. Historically, in the field of mechanical
vibration analysis, Alarm Bands have been used to define spectral
frequency bands of interest for the purposes of analyzing and/or
trending significant vibration patterns. The Alarm Band typically
consists of a spectral (amplitude plotted against frequency) region
defined between a low and high frequency border. The amplitude
between these borders is summed in the same manner for which an
overall amplitude is calculated. A Smart Band is more flexible in
that it not only refers to a specific frequency band but can also
refer to a group of spectral peaks such as the harmonics of a
single peak, a true-peak level or crest factor derived from a time
waveform, an overall derived from a vibration envelope spectrum or
other specialized signal analysis technique or a logical
combination (AND, OR, XOR, etc.) of these signal attributes. In
addition, a myriad assortment of other parametric data, including
system load, motor voltage and phase information, bearing
temperature, flow rates, and the like, can likewise be used as the
basis for forming additional smart bands. In embodiments, Smart
Band symptoms may be used as building blocks for an expert system
whose engine would utilize these inputs to derive diagnoses. Some
of these mini-diagnoses may then in turn be used as Smart-Band
symptoms (smart bands can include even diagnoses) for more
generalized diagnoses.
[0207] Embodiments of the methods and systems disclosed herein may
include a neural net expert system using smart bands. Typical
vibration analysis engines are rule-based (i.e., they use a list of
expert rules which, when met, trigger specific diagnoses). In
contrast, a neural approach utilizes the weighted triggering of
multiple input stimuli into smaller analytical engines or neurons
which in turn feed a simplified weighted output to other neurons.
The output of these neurons can be also classified as smart bands
which in turn feed other neurons. This produces a more layered
approach to expert diagnosing as opposed to the one-shot approach
of a rule-based system. In embodiments, the expert system utilizes
this neural approach using smart bands; however, it does not
preclude rule-based diagnoses being reclassified as smart bands as
further stimuli to be utilized by the expert system. From this
point-of-view, it can be overviewed as a hybrid approach, although
at the highest level it is essentially neural.
[0208] Embodiments of the methods and systems disclosed herein may
include use of database hierarchy in analysis smart band symptoms
and diagnoses may be assigned to various hierarchical database
levels. For example, a smart band may be called "Looseness" at the
bearing level, trigger "Looseness" at the equipment level, and
trigger "Looseness" at the machine level. Another example would be
having a smart band diagnosis called "Horizontal Plane Phase Flip"
across a coupling and generate a smart band diagnosis of "Vertical
Coupling Misalignment" at the machine level.
[0209] Embodiments of the methods and systems disclosed herein may
include expert system GUIs. In embodiments, the system undertakes a
graphical approach to defining smart bands and diagnoses for the
expert system. The entry of symptoms, rules, or more generally
smart bands for creating a particular machine diagnosis, may be
tedious and time consuming. One means of making the process more
expedient and efficient is to provide a graphical means by use of
wiring. The proposed graphical interface consists of four major
components: a symptom parts bin, diagnoses bin, tools bin, and
graphical wiring area ("GWA"). In embodiments, a symptom parts bin
includes various spectral, waveform, envelope and any type of
signal processing characteristic or grouping of characteristics
such as a spectral peak, spectral harmonic, waveform true-peak,
waveform crest-factor, spectral alarm band, and so on. Each part
may be assigned additional properties. For example, a spectral peak
part may be assigned a frequency or order (multiple) of running
speed. Some parts may be pre-defined or user defined such as a
1.times., 2.times., 3.times. running speed, 1.times., 2.times.,
3.times. gear mesh, 1.times., 2.times., 3.times. blade pass, number
of motor rotor bars x running speed, and so on.
[0210] In embodiments, the diagnoses bin includes various
pre-defined as well as user-defined diagnoses such as misalignment,
imbalance, looseness, bearing faults, and so on. Like parts,
diagnoses may also be used as parts for the purposes of building
more complex diagnoses. In embodiments, the tools bin includes
logical operations such as AND, OR, XOR, etc. or other ways of
combining the various parts listed above such as Find Max, Find
Min, Interpolate, Average, other Statistical Operations, etc. In
embodiments, a graphical wiring area includes parts from the parts
bin or diagnoses from the diagnoses bin and may be combined using
tools to create diagnoses. The various parts, tools and diagnoses
will be represented with icons which are simply graphically wired
together in the desired manner.
[0211] Embodiments of the methods and systems disclosed herein may
include a graphical approach for back-calculation definition. In
embodiments, the expert system also provides the opportunity for
the system to learn. If one already knows that a unique set of
stimuli or smart bands corresponds to a specific fault or
diagnosis, then it is possible to back-calculate a set of
coefficients that when applied to a future set of similar stimuli
would arrive at the same diagnosis. In embodiments, if there are
multiple sets of data, a best-fit approach may be used. Unlike the
smart band GUI, this embodiment will self-generate a wiring
diagram. In embodiments, the user may tailor the back-propagation
approach settings and use a database browser to match specific sets
of data with the desired diagnoses. In embodiments, the desired
diagnoses may be created or custom tailored with a smart band GUI.
In embodiments, after that, a user may press the GENERATE button
and a dynamic wiring of the symptom-to-diagnosis may appear on the
screen as it works through the algorithms to achieve the best fit.
In embodiments, when complete, a variety of statistics are
presented which detail how well the mapping process proceeded. In
some cases, no mapping may be achieved if, for example, the input
data was all zero or the wrong data (mistakenly assigned) and so
on. Embodiments of the methods and systems disclosed herein may
include bearing analysis methods. In embodiments, bearing analysis
methods may be used in conjunction with a computer aided design
("CAD"), predictive deconvolution, minimum variance distortionless
response ("MVDR") and spectrum sum-of-harmonics.
[0212] In recent years, there has been a strong drive to save power
which has resulted in an influx of variable frequency drives and
variable speed machinery. In embodiments, a bearing analysis method
is provided. In embodiments, torsional vibration detection and
analysis is provided utilizing transitory signal analysis to
provide an advanced torsional vibration analysis for a more
comprehensive way to diagnose machinery where torsional forces are
relevant (such as machinery with rotating components). Due
primarily to the decrease in cost of motor speed control systems,
as well as the increased cost and consciousness of energy-usage, it
has become more economically justifiable to take advantage of the
potentially vast energy savings of load control. Unfortunately, one
frequently overlooked design aspect of this issue is that of
vibration. When a machine is designed to run at only one speed, it
is far easier to design the physical structure accordingly so as to
avoid mechanical resonances both structural and torsional, each of
which can dramatically shorten the mechanical health of a machine.
This would include such structural characteristics as the types of
materials to use, their weight, stiffening member requirements and
placement, bearing types, bearing location, base support
constraints, etc. Even with machines running at one speed,
designing a structure so as to minimize vibration can prove a
daunting task, potentially requiring computer modeling,
finite-element analysis, and field testing. By throwing variable
speeds into the mix, in many cases, it becomes impossible to design
for all desirable speeds. The problem then becomes one of
minimization, e.g., by speed avoidance. This is why many modern
motor controllers are typically programmed to skip or quickly pass
through specific speed ranges or bands. Embodiments may include
identifying speed ranges in a vibration monitoring system.
Non-torsional, structural resonances are typically fairly easy to
detect using conventional vibration analysis techniques. However,
this is not the case for torsion. One special area of current
interest is the increased incidence of torsional resonance
problems, apparently due to the increased torsional stresses of
speed change as well as the operation of equipment at torsional
resonance speeds. Unlike non-torsional structural resonances which
generally manifest their effect with dramatically increased casing
or external vibration, torsional resonances generally show no such
effect. In the case of a shaft torsional resonance, the twisting
motion induced by the resonance may only be discernible by looking
for speed and/or phase changes. The current standard methodology
for analyzing torsional vibration involves the use of specialized
instrumentation. Methods and systems disclosed herein allow
analysis of torsional vibration without such specialized
instrumentation. This may consist of shutting the machine down and
employing the use of strain gauges and/or other special fixturing
such as speed encoder plates and/or gears. Friction wheels are
another alternative, but they typically require manual
implementation and a specialized analyst. In general, these
techniques can be prohibitively expensive and/or inconvenient. An
increasing prevalence of continuous vibration monitoring systems
due to decreasing costs and increasing convenience (e.g., remote
access) exists. In embodiments, there is an ability to discern
torsional speed and/or phase variations with just the vibration
signal. In embodiments, transient analysis techniques may be
utilized to distinguish torsionally induced vibrations from mere
speed changes due to process control. In embodiments, factors for
discernment might focus on one or more of the following aspects:
the rate of speed change due to variable speed motor control would
be relatively slow, sustained and deliberate; torsional speed
changes would tend to be short, impulsive and not sustained;
torsional speed changes would tend to be oscillatory, most likely
decaying exponentially, process speed changes would not; and
smaller speed changes associated with torsion relative to the
shaft's rotational speed which suggest that monitoring phase
behavior would show the quick or transient speed bursts in contrast
to the slow phase changes historically associated with ramping a
machine's speed up or down (as typified with Bode or Nyquist
plots).
[0213] Embodiments of the methods and systems disclosed herein may
include improved integration using both analog and digital methods.
When a signal is digitally integrated using software, essentially
the spectral low-end frequency data has its amplitude multiplied by
a function which quickly blows up as it approaches zero and creates
what is known in the industry as a "ski-slope" effect. The
amplitude of the ski-slope is essentially the noise floor of the
instrument. The simple remedy for this is the traditional hardware
integrator, which can perform at signal-to-noise ratios much
greater than that of an already digitized signal. It can also limit
the amplification factor to a reasonable level so that
multiplication by very large numbers is essentially prohibited.
However, at high frequencies where the frequency becomes large, the
original amplitude which may be well above the noise floor is
multiplied by a very small number (1/f) that plunges it well below
the noise floor. The hardware integrator has a fixed noise floor
that although low floor does not scale down with the now lower
amplitude high-frequency data. In contrast, the same digital
multiplication of a digitized high-frequency signal also scales
down the noise floor proportionally. In embodiments, hardware
integration may be used below the point of unity gain where (at a
value usually determined by units and/or desired signal to noise
ratio based on gain) and software integration may be used above the
value of unity gain to produce an ideal result. In embodiments,
this integration is performed in the frequency domain In
embodiments, the resulting hybrid data can then be transformed back
into a waveform which should be far superior in signal-to-noise
ratio when compared to either hardware integrated or software
integrated data. In embodiments, the strengths of hardware
integration are used in conjunction with those of digital software
integration to achieve the maximum signal-to-noise ratio. In
embodiments, the first order gradual hardware integrator high pass
filter along with curve fitting allow some relatively low frequency
data to get through while reducing or eliminating the noise,
allowing very useful analytical data that steep filters kill to be
salvaged.
[0214] Embodiments of the methods and systems disclosed herein may
include adaptive scheduling techniques for continuous monitoring.
Continuous monitoring is often performed with an up-front Mux whose
purpose it is to select a few channels of data among many to feed
the hardware signal processing, A/D, and processing components of a
DAQ system. This is done primarily out of practical cost
considerations. The tradeoff is that all of the points are not
monitored continuously (although they may be monitored to a lesser
extent via alternative hardware methods). In embodiments, multiple
scheduling levels are provided. In embodiments, at the lowest
level, which is continuous for the most part, all of the
measurement points will be cycled through in round-robin fashion.
For example, if it takes 30 seconds to acquire and process a
measurement point and there are 30 points, then each point is
serviced once every 15 minutes; however, if a point should alarm by
whatever criteria the user selects, its priority level can be
increased so that it is serviced more often. As there can be
multiple grades of severity for each alarm, so can there me
multiple levels of priority with regards to monitoring. In
embodiments, more severe alarms will be monitored more frequently.
In embodiments, a number of additional high-level signal processing
techniques can be applied at less frequent intervals. Embodiments
may take advantage of the increased processing power of a PC and
the PC can temporarily suspend the round-robin route collection
(with its multiple tiers of collection) process and stream the
required amount of data for a point of its choosing. Embodiments
may include various advanced processing techniques such as envelope
processing, wavelet analysis, as well as many other signal
processing techniques. In embodiments, after acquisition of this
data, the DAQ card set will continue with its route at the point it
was interrupted. In embodiments, various PC scheduled data
acquisitions will follow their own schedules which will be less
frequency than the DAQ card route. They may be set up hourly,
daily, by number of route cycles (for example, once every 10
cycles) and also increased scheduling-wise based on their alarm
severity priority or type of measurement (e.g., motors may be
monitored differently than fans).
[0215] Embodiments of the methods and systems disclosed herein may
include data acquisition parking features. In embodiments, a data
acquisition box used for route collection, real time analysis and
in general as an acquisition instrument can be detached from its PC
(tablet or otherwise) and powered by an external power supply or
suitable battery. In embodiments, the data collector still retains
continuous monitoring capability and its on-board firmware can
implement dedicated monitoring functions for an extended period of
time or can be controlled remotely for further analysis.
Embodiments of the methods and systems disclosed herein may include
extended statistical capabilities for continuous monitoring.
[0216] Embodiments of the methods and systems disclosed herein may
include ambient sensing plus local sensing plus vibration for
analysis. In embodiments, ambient environmental temperature and
pressure, sensed temperature and pressure may be combined with
long/medium term vibration analysis for prediction of any of a
range of conditions or characteristics. Variants may add infrared
sensing, infrared thermography, ultrasound, and many other types of
sensors and input types in combination with vibration or with each
other. Embodiments of the methods and systems disclosed herein may
include a smart route. In embodiments, the continuous monitoring
system's software will adapt/adjust the data collection sequence
based on statistics, analytics, data alarms and dynamic analysis.
Typically, the route is set based on the channels the sensors are
attached to. In embodiments, with the crosspoint switch, the Mux
can combine any input Mux channels to the (e.g., eight) output
channels. In embodiments, as channels go into alarm or the system
identifies key deviations, it will pause the normal route set in
the software to gather specific simultaneous data, from the
channels sharing key statistical changes, for more advanced
analysis. Embodiments include conducting a smart OD S or smart
transfer function.
[0217] Embodiments of the methods and systems disclosed herein may
include smart ODS and one or more transfer functions. In
embodiments, due to a system's multiplexer and crosspoint switch,
an ODS, a transfer function, or other special tests on all the
vibration sensors attached to a machine/structure can be performed
and show exactly how the machine's points are moving in
relationship to each other. In embodiments, 40-50 kHz and longer
data lengths (e.g., at least one minute) may be streamed, which may
reveal different information than what a normal ODS or transfer
function will show. In embodiments, the system will be able to
determine, based on the data/statistics/analytics to use, the smart
route feature that breaks from the standard route and conducts an
ODS across a machine, structure or multiple machines and structures
that might show a correlation because the conditions/data directs
it. In embodiments, for the transfer functions there may be an
impact hammer used on one channel and then compared against other
vibration sensors on the machine. In embodiments, the system may
use the condition changes such as load, speed, temperature or other
changes in the machine or system to conduct the transfer function.
In embodiments, different transfer functions may be compared to
each other over time. In embodiments, difference transfer functions
may be strung together like a movie that may show how the machinery
fault changes, such as a bearing that could show how it moves
through the four stages of bearing failure and so on. Embodiments
of the methods and systems disclosed herein may include a
hierarchical Mux.
[0218] With reference to FIG. 8, the present disclosure generally
includes digitally collecting or streaming waveform data 2010 from
a machine 2020 whose operational speed can vary from relatively
slow rotational or oscillational speeds to much higher speeds in
different situations. The waveform data 2010, at least on one
machine, may include data from a single axis sensor 2030 mounted at
an unchanging reference location 2040 and from a three-axis sensor
2050 mounted at changing locations (or located at multiple
locations), including location 2052. In embodiments, the waveform
data 2010 can be vibration data obtained simultaneously from each
sensor 2030, 2050 in a gap-free format for a duration of multiple
minutes with maximum resolvable frequencies sufficiently large to
capture periodic and transient impact events. By way of this
example, the waveform data 2010 can include vibration data that can
be used to create an operational deflecting shape. It can also be
used, as needed, to diagnose vibrations from which a machine repair
solution can be prescribed.
[0219] In embodiments, the machine 2020 can further include a
housing 2100 that can contain a drive motor 2110 that can drive a
shaft 2120. The shaft 2120 can be supported for rotation or
oscillation by a set of bearings 2130, such as including a first
bearing 2140 and a second bearing 2150. A data collection module
2160 can connect to (or be resident on) the machine 2020. In one
example, the data collection module 2160 can be located and
accessible through a cloud network facility 2170, can collect the
waveform data 2010 from the machine 2020, and deliver the waveform
data 2010 to a remote location. A working end 2180 of the drive
shaft 2120 of the machine 2020 can drive a windmill, a fan, a pump,
a drill, a gear system, a drive system, or other working element,
as the techniques described herein can apply to a wide range of
machines, equipment, tools, or the like that include rotating or
oscillating elements. In other instances, a generator can be
substituted for the motor 2110, and the working end of the drive
shaft 2120 can direct rotational energy to the generator to
generate power, rather than consume it.
[0220] In embodiments, the waveform data 2010 can be obtained using
a predetermined route format based on the layout of the machine
2020. The waveform data 2010 may include data from the single axis
sensor 2030 and the three-axis sensor 2050. The single-axis sensor
2030 can serve as a reference probe with its one channel of data
and can be fixed at the unchanging location 2040 on the machine
under survey. The three-axis sensor 2050 can serve as a tri-axial
probe (e.g., three orthogonal axes) with its three channels of data
and can be moved along a predetermined diagnostic route format from
one test point to the next test point. In one example, both sensors
2030, 2050 can be mounted manually to the machine 2020 and can
connect to a separate portable computer in certain service
examples. The reference probe can remain at one location while the
user can move the tri-axial vibration probe along the predetermined
route, such as from bearing-to-bearing on a machine. In this
example, the user is instructed to locate the sensors at the
predetermined locations to complete the survey (or portion thereof)
of the machine.
[0221] With reference to FIG. 9, a portion of an exemplary machine
2200 is shown having a tri-axial sensor 2210 mounted to a location
2220 associated with a motor bearing of the machine 2200 with an
output shaft 2230 and output member 2240 in accordance with the
present disclosure. With reference to FIGS. 10 and 11, an exemplary
machine 2300 is shown having a tri-axial sensor 2310 and a
single-axis vibration sensor 2320 serving as the reference sensor
that is attached on the machine 2300 at an unchanging location for
the duration of the vibration survey in accordance with the present
disclosure. The tri-axial sensor 2310 and the single-axis vibration
sensor 2320 can be connected to a data collection system 2330.
[0222] In further examples, the sensors and data acquisition
modules and equipment can be integral to, or resident on, the
rotating machine. By way of these examples, the machine can contain
many single axis sensors and many tri-axial sensors at
predetermined locations. The sensors can be originally installed
equipment and provided by the original equipment manufacturer or
installed at a different time in a retrofit application. The data
collection module 2160, or the like, can select and use one single
axis sensor and obtain data from it exclusively during the
collection of waveform data 2010 while moving to each of the
tri-axial sensors. The data collection module 2160 can be resident
on the machine 2020 and/or connect via the cloud network facility
2170.
[0223] With reference to FIG. 8, the various embodiments include
collecting the waveform data 2010 by digitally recording locally,
or streaming over, the cloud network facility 2170. The waveform
data 2010 can be collected so as to be gap-five with no
interruptions and, in some respects, can be similar to an analog
recording of waveform data. The waveform data 2010 from all of the
channels can be collected for one to two minutes depending on the
rotating or oscillating speed of the machine being monitored. In
embodiments, the data sampling rate can be at a relatively
high-sampling rate relative to the operating frequency of the
machine 2020.
[0224] In embodiments, a second reference sensor can be used, and a
fifth channel of data can be collected. As such, the single-axis
sensor can be the first channel and tri-axial vibration can occupy
the second, the third, and the fourth data channels. This second
reference sensor, like the first, can be a single axis sensor, such
as an accelerometer. In embodiments, the second reference sensor,
like the first reference sensor, can remain in the same location on
the machine for the entire vibration survey on that machine. The
location of the first reference sensor (i.e., the single axis
sensor) may be different than the location of the second reference
sensors (i.e., another single axis sensor). In certain examples,
the second reference sensor can be used when the machine has two
shafts with different operating speeds, with the two reference
sensors being located on the two different shafts. In accordance
with this example, further single-axis reference sensors can be
employed at additional but different unchanging locations
associated with the rotating machine.
[0225] In embodiments, the waveform data can be transmitted
electronically in a gap-free free format at a significantly high
rate of sampling for a relatively longer period of time. In one
example, the period of time is 60 seconds to 120 seconds. In
another example, the rate of sampling is 100 kHz with a maximum
resolvable frequency (Fmax) of 40 kHz. It will be appreciated in
light of this disclosure that the waveform data can be shown to
approximate more closely some of the wealth of data available from
previous instances of analog recording of waveform data.
[0226] In embodiments, sampling, band selection, and filtering
techniques can permit one or more portions of a long stream of data
(i.e., one to two minutes in duration) to be under sampled or over
sampled to realize varying effective sampling rates. To this end,
interpolation and decimation can be used to further realize varying
effective sampling rates. For example, oversampling may be applied
to frequency bands that are proximal to rotational or oscillational
operating speeds of the sampled machine, or to harmonics thereof,
as vibration effects may tend to be more pronounced at those
frequencies across the operating range of the machine. In
embodiments, the digitally-sampled data set can be decimated to
produce a lower sampling rate. It will be appreciated in light of
the disclosure that decimate in this context can be the opposite of
interpolate. In embodiments, decimating the data set can include
first applying a low-pass filter to the digitally-sampled data set
and then undersampling the data set.
[0227] In one example, a sample waveform at 100 Hz can be
undersampled at every tenth point of the digital waveform to
produce an effective sampling rate of 10 Hz, but the remaining nine
points of that portion of the waveform are effectively discarded
and not included in the modeling of the sample waveform. Moreover,
this type of bare undersampling can create ghost frequencies due to
the undersampling rate (i.e., 10 Hz) relative to the 100 Hz sample
waveform.
[0228] Most hardware for analog-to-digital conversions uses a
sample-and-hold circuit that can charge up a capacitor for a given
amount of time such that an average value of the waveform is
determined over a specific change in time. It will be appreciated
in light of the disclosure that the value of the waveform over the
specific change in time is not linear but more similar to a
cardinal sinusoidal ("sinc") function; therefore, it can be shown
that more emphasis can be placed on the waveform data at the center
of the sampling interval with exponential decay of the cardinal
sinusoidal signal occurring from its center.
[0229] By way of the above example, the sample waveform at 100 Hz
can be hardware-sampled at 10 Hz and therefore each sampling point
is averaged over 100 milliseconds (e.g., a signal sampled at 100 Hz
can have each point averaged over 10 milliseconds). In contrast to
the effective discarding of nine out of the ten data points of the
sampled waveform as discussed above, the present disclosure can
include weighing adjacent data. The adjacent data can refer to the
sample points that were previously discarded and the one remaining
point that was retained. In one example, a low pass filter can
average the adjacent sample data linearly, i.e., determining the
sum of every ten points and then dividing that sum by ten. In a
further example, the adjacent data can be weighted with a sinc
function. The process of weighting the original waveform with the
sinc function can be referred to as an impulse function, or can be
referred to in the time domain as a convolution.
[0230] The present disclosure can be applicable to not only
digitizing a waveform signal based on a detected voltage, but can
also be applicable to digitizing waveform signals based on current
waveforms, vibration waveforms, and image processing signals
including video signal rasterization. In one example, the resizing
of a window on a computer screen can be decimated, albeit in at
least two directions. In these further examples, it will be
appreciated that undersampling by itself can be shown to be
insufficient. To that end, oversampling or upsampling by itself can
similarly be shown to be insufficient, such that interpolation can
be used like decimation but in lieu of only undersampling by
itself
[0231] It will be appreciated in light of the disclosure that
interpolation in this context can refer to first applying a low
pass filter to the digitally-sampled waveform data and then
upsampling the waveform data. It will be appreciated in light of
the disclosure that real-world examples can often require the use
of use non-integer factors for decimation or interpolation, or
both. To that end, the present disclosure includes interpolating
and decimating sequentially in order to realize a non-integer
factor rate for interpolating and decimating In one example,
interpolating and decimating sequentially can define applying a
low-pass filter to the sample waveform, then interpolating the
waveform after the low-pass filter, and then decimating the
waveform after the interpolation. In embodiments, the vibration
data can be looped to purposely emulate conventional tape recorder
loops, with digital filtering techniques used with the effective
splice to facilitate longer analyses. It will be appreciated in
light of the disclosure that the above techniques do not preclude
waveform, spectrum, and other types of analyses to be processed and
displayed with a GUI of the user at the time of collection. It will
be appreciated in light of the disclosure that newer systems can
permit this functionality to be performed in parallel to the
high-performance collection of the raw waveform data.
[0232] With respect to time of collection issues, it will be
appreciated that older systems using the compromised approach of
improving data resolution, by collecting at different sampling
rates and data lengths, do not in fact save as much time as
expected. To that end, every time the data acquisition hardware is
stopped and started, latency issues can be created, especially when
there is hardware auto-scaling performed. The same can be true with
respect to data retrieval of the route information (i.e., test
locations) that is often in a database format and can be
exceedingly slow. The storage of the raw data in bursts to disk
(whether solid state or otherwise) can also be undesirably
slow.
[0233] In contrast, the many embodiments include digitally
streaming the waveform data 2010, as disclosed herein, and also
enjoying the benefit of needing to load the route parameter
information while setting the data acquisition hardware only once.
Because the waveform data 2010 is streamed to only one file, there
is no need to open and close files, or switch between loading and
writing operations with the storage medium. It can be shown that
the collection and storage of the waveform data 2010, as described
herein, can be shown to produce relatively more meaningful data in
significantly less time than the traditional batch data acquisition
approach. An example of this includes an electric motor about which
waveform data can be collected with a data length of 4K points
(i.e., 4,096) for sufficiently high resolution in order to, among
other things, distinguish electrical sideband frequencies. For fans
or blowers, a reduced resolution of 1K (i.e., 1,024) can be used.
In certain instances, 1K can be the minimum waveform data length
requirement. The sampling rate can be 1,280 Hz and that equates to
an Fmax of 500 Hz. It will be appreciated in light of the
disclosure that oversampling by an industry standard factor of 2.56
can satisfy the necessary two-times (2.times.) oversampling for the
Nyquist Criterion with some additional leeway that can accommodate
anti-aliasing filter-rolloff. The time to acquire this waveform
data would be 1,024 points at 1,280 hertz, which are 800
milliseconds.
[0234] To improve accuracy, the waveform data can be averaged.
Eight averages can be used with, for example, fifty percent
overlap. This would extend the time from 800 milliseconds to 3.6
seconds, which is equal to 800 msec .times.8 averages .times.0.5
(overlap ratio) +0.5.times.800 msec (non-overlapped head and tail
ends). After collection at Fmax=500 Hz waveform data, a higher
sampling rate can be used. In one example, ten times (10x) the
previous sampling rate can be used and Fmax=10 kHz. By way of this
example, eight averages can be used with fifty percent (50%)
overlap to collect waveform data at this higher rate that can
amount to a collection time of 360 msec or 0.36 seconds. It will be
appreciated in light of the disclosure that it can be necessary to
read the hardware collection parameters for the higher sampling
rate from the route list, as well as permit hardware auto-scaling,
or the resetting of other necessary hardware collection parameters,
or both. To that end, a few seconds of latency can be added to
accommodate the changes in sampling rate. In other instances,
introducing latency can accommodate hardware autoscaling and
changes to hardware collection parameters that can be required when
using the lower sampling rate disclosed herein. In addition to
accommodating the change in sampling rate, additional time is
needed for reading the route point information from the database
(i.e., where to monitor and where to monitor next), displaying the
route information, and processing the waveform data. Moreover,
display of the waveform data and/or associated spectra can also
consume significant time. In light of the above, 15 seconds to 20
seconds can elapse while obtaining waveform data at each
measurement point.
[0235] In further examples, additional sampling rates can be added
but this can make the total amount time for the vibration survey
even longer because time adds up from changeover time from one
sampling rate to another and from the time to obtain additional
data at different sampling rate. In one example, a lower sampling
rate is used, such as a sampling rate of 128 Hz where Fmax=50 Hz.
By way of this example, the vibration survey would, therefore,
require an additional 36 seconds for the first set of averaged data
at this sampling rate, in addition to others mentioned above, and
consequently the total time spent at each measurement point
increases even more dramatically. Further embodiments include using
similar digital streaming of gap free waveform data as disclosed
herein for use with wind turbines and other machines that can have
relatively slow speed rotating or oscillating systems. In many
examples, the waveform data collected can include long samples of
data at a relatively high-sampling rate. In one example, the
sampling rate can be 100 kHz and the sampling duration can be for
two minutes on all of the channels being recorded. In many
examples, one channel can be for the single axis reference sensor
and three more data channels can be for the tri-axial three channel
sensor. It will be appreciated in light of the disclosure that the
long data length can be shown to facilitate detection of extremely
low frequency phenomena. The long data length can also be shown to
accommodate the inherent speed variability in wind turbine
operations. Additionally, the long data length can further be shown
to provide the opportunity for using numerous averages such as
those discussed herein, to achieve very high spectral resolution,
and to make feasible tape loops for certain spectral analyses. Many
multiple advanced analytical techniques can now become available
because such techniques can use the available long uninterrupted
length of waveform data in accordance with the present
disclosure.
[0236] It will also be appreciated in light of the disclosure that
the simultaneous collection of waveform data from multiple channels
can facilitate performing transfer functions between multiple
channels. Moreover, the simultaneous collection of waveform data
from multiple channels facilitates establishing phase relationships
across the machine so that more sophisticated correlations can be
utilized by relying on the fact that the waveforms from each of the
channels are collected simultaneously. In other examples, more
channels in the data collection can be used to reduce the time it
takes to complete the overall vibration survey by allowing for
simultaneous acquisition of waveform data from multiple sensors
that otherwise would have to be acquired, in a subsequent fashion,
moving sensor to sensor in the vibration survey.
[0237] The present disclosure includes the use of at least one of
the single-axis reference probe on one of the channels to allow for
acquisition of relative phase comparisons between channels. The
reference probe can be an accelerometer or other type of transducer
that is not moved and, therefore, fixed at an unchanging location
during the vibration survey of one machine. Multiple reference
probes can each be deployed as at suitable locations fixed in place
(i.e., at unchanging locations) throughout the acquisition of
vibration data during the vibration survey. In certain examples, up
to seven reference probes can be deployed depending on the capacity
of the data collection module 2160 or the like. Using transfer
functions or similar techniques, the relative phases of all
channels may be compared with one another at all selected
frequencies. By keeping the one or more reference probes fixed at
their unchanging locations while moving or monitoring the other
tri-axial vibration sensors, it can be shown that the entire
machine can be mapped with regard to amplitude and relative phase.
This can be shown to be true even when there are more measurement
points than channels of data collection. With this information, an
operating deflection shape can be created that can show dynamic
movements of the machine in 3 D, which can provide an invaluable
diagnostic tool. In embodiments, the one or more reference probes
can provide relative phase, rather than absolute phase. It will be
appreciated in light of the disclosure that relative phase may not
be as valuable absolute phase for some purposes, but the relative
phase the information can still be shown to be very useful.
[0238] In embodiments, the sampling rates used during the vibration
survey can be digitally synchronized to predetermined operational
frequencies that can relate to pertinent parameters of the machine
such as rotating or oscillating speed. Doing this, permits
extracting even more information using synchronized averaging
techniques. It will be appreciated in light of the disclosure that
this can be done without the use of a key phasor or a reference
pulse from a rotating shaft, which is usually not available for
route collected data. As such, non-synchronous signals can be
removed from a complex signal without the need to deploy
synchronous averaging using the key phasor. This can be shown to be
very powerful when analyzing a particular pinion in a gearbox or
generally applied to any component within a complicated mechanical
mechanism. In many instances, the key phasor or the reference pulse
is rarely available with route collected data, but the techniques
disclosed herein can overcome this absence. In embodiments, there
can be multiple shafts running at different speeds within the
machine being analyzed. In certain instances, there can be a
single-axis reference probe for each shaft. In other instances, it
is possible to relate the phase of one shaft to another shaft using
only one single axis reference probe on one shaft at its unchanging
location. In embodiments, variable speed equipment can be more
readily analyzed with relatively longer duration of data relative
to single speed equipment. The vibration survey can be conducted at
several machine speeds within the same contiguous set of vibration
data using the same techniques disclosed herein. These techniques
can also permit the study of the change of the relationship between
vibration and the change of the rate of speed that was not
available before.
[0239] In embodiments, there are numerous analytical techniques
that can emerge from because raw waveform data can be captured in a
gap-free digital format as disclosed herein. The gap-free digital
format can facilitate many paths to analyze the waveform data in
many ways after the fact to identify specific problems. The
vibration data collected in accordance with the techniques
disclosed herein can provide the analysis of transient,
semi-periodic and very low frequency phenomena. The waveform data
acquired in accordance with the present disclosure can contain
relatively longer streams of raw gap-free waveform data that can be
conveniently played back as needed, and on which many and varied
sophisticated analytical techniques can be performed. A large
number of such techniques can provide for various forms of
filtering to extract low amplitude modulations from transient
impact data that can be included in the relatively longer stream of
raw gap-free waveform data. It will be appreciated in light of the
disclosure that in past data collection practices, these types of
phenomena were typically lost by the averaging process of the
spectral processing algorithms because the goal of the previous
data acquisition module was purely periodic signals; or these
phenomena were lost to file size reduction methodologies due to the
fact that much of the content from an original raw signal was
typically discarded knowing it would not be used.
[0240] In embodiments, there is a method of monitoring vibration of
a machine having at least one shaft supported by a set of bearings.
The method includes monitoring a first data channel assigned to a
single-axis sensor at an unchanging location associated with the
machine. The method also includes monitoring a second, third, and
fourth data channel assigned to a three-axis sensor. The method
further includes recording gap-free digital waveform data
simultaneously from all of the data channels while the machine is
in operation; and determining a change in relative phase based on
the digital waveform data. The method also includes the tri-axial
sensor being located at a plurality of positions associated with
the machine while obtaining the digital waveform. In embodiments,
the second, third, and fourth channels are assigned together to a
sequence of tri-axial sensors each located at different positions
associated with the machine. In embodiments, the data is received
from all of the sensors on all of their channels
simultaneously.
[0241] The method also includes determining an operating deflection
shape based on the change in relative phase information and the
waveform data. In embodiments, the unchanging location of the
reference sensor is a position associated with a shaft of the
machine. In embodiments, the tri-axial sensors in the sequence of
the tri-axial sensors are each located at different positions and
are each associated with different bearings in the machine. In
embodiments, the unchanging location is a position associated with
a shaft of the machine and, wherein, the tri-axial sensors in the
sequence of the tri-axial sensors are each located at different
positions and are each associated with different bearings that
support the shaft in the machine. The various embodiments include
methods of sequentially monitoring vibration or similar process
parameters and signals of a rotating or oscillating machine or
analogous process machinery from a number of channels
simultaneously, which can be known as an ensemble. In various
examples, the ensemble can include one to eight channels. In
further examples, an ensemble can represent a logical measurement
grouping on the equipment being monitored whether those measurement
locations are temporary for measurement, supplied by the original
equipment manufacturer, retrofit at a later date, or one or more
combinations thereof.
[0242] In one example, an ensemble can monitor bearing vibration in
a single direction. In a further example, an ensemble can monitor
three different directions (e.g., orthogonal directions) using a
tri-axial sensor. In yet further examples, an ensemble can monitor
four or more channels where the first channel can monitor a single
axis vibration sensor, and the second, the third, and the fourth
channels can monitor each of the three directions of the tri-axial
sensor. In other examples, the ensemble can be fixed to a group of
adjacent bearings on the same piece of equipment or an associated
shaft. The various embodiments provide methods that include
strategies for collecting waveform data from various ensembles
deployed in vibration studies or the like in a relatively more
efficient manner. The methods also include simultaneously
monitoring of a reference channel assigned to an unchanging
reference location associated with the ensemble monitoring the
machine. The cooperation with the reference channel can be shown to
support a more complete correlation of the collected waveforms from
the ensembles. The reference sensor on the reference channel can be
a single axis vibration sensor, or a phase reference sensor that
can be triggered by a reference location on a rotating shaft or the
like. As disclosed herein, the methods can further include
recording gap-free digital waveform data simultaneously from all of
the channels of each ensemble at a relatively high rate of sampling
so as to include all frequencies deemed necessary for the proper
analysis of the machinery being monitored while it is in operation.
The data from the ensembles can be streamed gap-free to a storage
medium for subsequent processing that can be connected to a cloud
network facility, a local data link, Bluetooth.TM. connectivity,
cellular data connectivity, or the like.
[0243] In embodiments, the methods disclosed herein include
strategies for collecting data from the various ensembles including
digital signal processing techniques that can be subsequently
applied to data from the ensembles to emphasize or better isolate
specific frequencies or waveform phenomena. This can be in contrast
with current methods that collect multiple sets of data at
different sampling rates, or with different hardware filtering
configurations including integration, that provide relatively less
post-processing flexibility because of the commitment to these same
(known as a priori hardware configurations). These same hardware
configurations can also be shown to increase time of the vibration
survey due to the latency delays associated with configuring the
hardware for each independent test. In embodiments, the methods for
collecting data from various ensembles include data marker
technology that can be used for classifying sections of streamed
data as homogenous and belonging to a specific ensemble. In one
example, a classification can be defined as operating speed. In
doing so, a multitude of ensembles can be created from what
conventional systems would collect as only one. The many
embodiments include post-processing analytic techniques for
comparing the relative phases of all the frequencies of interest
not only between each channel of the collected ensemble but also
between all of the channels of all of the ensembles being
monitored, when applicable.
[0244] With reference to FIG. 12, the many embodiments include a
first machine 2400 having rotating or oscillating components 2410,
or both, each supported by a set of bearings 2420 including a
bearing pack 2422, a bearing pack 2424, a bearing pack 2426, and
more as needed. The first machine 2400 can be monitored by a first
sensor ensemble 2450. The first ensemble 2450 can be configured to
receive signals from sensors originally installed (or added later)
on the first machine 2400. The sensors on the machine 2400 can
include single-axis sensors 2460, such as a single-axis sensor
2462, a single-axis sensor 2464, and more as needed. In many
examples, the single axis-sensors 2460 can be positioned in the
machine 2400 at locations that allow for the sensing of one of the
rotating or oscillating components 2410 of the machine 2400.
[0245] The machine 2400 can also have tri-axial (e.g., orthogonal
axes) sensors 2480, such as a tri-axial sensor 2482, a tri-axial
sensor 2484, and more as needed. In many examples, the tri-axial
sensors 2480 can be positioned in the machine 2400 at locations
that allow for the sensing of one of each of the bearing packs in
the sets of bearings 2420 that is associated with the rotating or
oscillating components of the machine 2400. The machine 2400 can
also have temperature sensors 2500, such as a temperature sensor
2502, a temperature sensor 2504, and more as needed. The machine
2400 can also have a tachometer sensor 2510 or more as needed that
each detail the RPMs of one of its rotating components. By way of
the above example, the first sensor ensemble 2450 can survey the
above sensors associated with the first machine 2400. To that end,
the first ensemble 2450 can be configured to receive eight
channels. In other examples, the first sensor ensemble 2450 can be
configured to have more than eight channels, or less than eight
channels as needed. In this example, the eight channels include two
channels that can each monitor a single-axis reference sensor
signal and three channels that can monitor a tri-axial sensor
signal. The remaining three channels can monitor two temperature
signals and a signal from a tachometer. In one example, the first
ensemble 2450 can monitor the single-axis sensor 2462, the
single-axis sensor 2464, the tri-axial sensor 2482, the temperature
sensor 2502, the temperature sensor 2504, and the tachometer sensor
2510 in accordance with the present disclosure. During a vibration
survey on the machine 2400, the first ensemble 2450 can first
monitor the tri-axial sensor 2482 and then move next to the
tri-axial sensor 2484.
[0246] After monitoring the tri-axial sensor 2484, the first
ensemble 2450 can monitor additional tri-axial sensors on the
machine 2400 as needed and that are part of the predetermined route
list associated with the vibration survey of the machine 2400, in
accordance with the present disclosure. During this vibration
survey, the first ensemble 2450 can continually monitor the
single-axis sensor 2462, the single-axis sensor 2464, the two
temperature sensors 2502, 2504, and the tachometer sensor 2510
while the first ensemble 2450 can serially monitor the multiple
tri-axial sensors 2480 in the pre-determined route plan for this
vibration survey.
[0247] With reference to FIG. 12, the many embodiments include a
second machine 2600 having rotating or oscillating components 2610,
or both, each supported by a set of bearings 2620 including a
bearing pack 2622, a bearing pack 2624, a bearing pack 2626, and
more as needed. The second machine 2600 can be monitored by a
second sensor ensemble 2650. The second ensemble 2650 can be
configured to receive signals from sensors originally installed (or
added later) on the second machine 2600. The sensors on the machine
2600 can include single-axis sensors 2660, such as a single-axis
sensor 2662, a single-axis sensor 2664, and more as needed. In many
examples, the single axis-sensors 2660 can be positioned in the
machine 2600 at locations that allow for the sensing of one of the
rotating or oscillating components 2610 of the machine 2600.
[0248] The machine 2600 can also have tri-axial (e.g., orthogonal
axes) sensors 2680, such as a tri-axial sensor 2682, a tri-axial
sensor 2684, a tri-axial sensor 2686, a tri-axial sensor 2688, and
more as needed. In many examples, the tri-axial sensors 2680 can be
positioned in the machine 2600 at locations that allow for the
sensing of one of each of the bearing packs in the sets of bearings
2620 that is associated with the rotating or oscillating components
of the machine 2600. The machine 2600 can also have temperature
sensors 2700, such as a temperature sensor 2702, a temperature
sensor 2704, and more as needed. The machine 2600 can also have a
tachometer sensor 2710 or more as needed that each detail the RPMs
of one of its rotating components.
[0249] By way of the above example, the second sensor ensemble 2650
can survey the above sensors associated with the second machine
2600. To that end, the second ensemble 2650 can be configured to
receive eight channels. In other examples, the second sensor
ensemble 2650 can be configured to have more than eight channels or
less than eight channels as needed. In this example, the eight
channels include one channel that can monitor a single-axis
reference sensor signal and six channels that can monitor two
tri-axial sensor signals. The remaining channel can monitor a
temperature signal. In one example, the second ensemble 2650 can
monitor the single axis sensor 2662, the tri-axial sensor 2682, the
tri-axial sensor 2684, and the temperature sensor 2702. During a
vibration survey on the machine 2600 in accordance with the present
disclosure, the second ensemble 2650 can first monitor the
tri-axial sensor 2682 simultaneously with the tri-axial sensor 2684
and then move onto the tri-axial sensor 2686 simultaneously with
the tri-axial sensor 2688.
[0250] After monitoring the tri-axial sensors 2680, the second
ensemble 2650 can monitor additional tri-axial sensors (in
simultaneous pairs) on the machine 2600 as needed and that are part
of the predetermined route list associated with the vibration
survey of the machine 2600 in accordance with the present
disclosure. During this vibration survey, the second ensemble 2650
can continually monitor the single-axis sensor 2662 at its
unchanging location and the temperature sensor 2702 while the
second ensemble 2650 can serially monitor the multiple tri-axial
sensors in the pre-determined route plan for this vibration
survey.
[0251] With continuing reference to FIG. 12, the many embodiments
include a third machine 2800 having rotating or oscillating
components 2810, or both, each supported by a set of bearings 2820
including a bearing pack 2822, a bearing pack 2824, a bearing pack
2826, and more as needed. The third machine 2800 can be monitored
by a third sensor ensemble 2850. The third ensemble 2850 can be
configured with a single-axis sensor 2860, and two tri-axial (e.g.,
orthogonal axes) sensors 2880, 2882. In many examples, the single
axis-sensor 2860 can be secured by the user on the machine 2800 at
a location that allows for the sensing of one of the rotating or
oscillating components of the machine 2800. The tri-axial sensors
2880, 2882 can be also be located on the machine 2800 by the user
at locations that allow for the sensing of one of each of the
bearings in the sets of bearings that each associated with the
rotating or oscillating components of the machine 2800. The third
ensemble 2850 can also include a temperature sensor 2900. The third
ensemble 2850 and its sensors can be moved to other machines unlike
the first and second ensembles 2450, 2650.
[0252] The many embodiments also include a fourth machine 2950
having rotating or oscillating components 2960, or both, each
supported by a set of bearings 2970 including a bearing pack 2972,
a bearing pack 2974, a bearing pack 2976, and more as needed. The
fourth machine 2950 can be also monitored by the third sensor
ensemble 2850 when the user moves it to the fourth machine 2950.
The many embodiments also include a fifth machine 3000 having
rotating or oscillating components 3010, or both. The fifth machine
3000 may not be explicitly monitored by any sensor or any sensor
ensembles in operation but it can create vibrations or other
impulse energy of sufficient magnitude to be recorded in the data
associated with any one of the machines 2400, 2600, 2800, 2950
under a vibration survey.
[0253] The many embodiments include monitoring the first sensor
ensemble 2450 on the first machine 2400 through the predetermined
route as disclosed herein. The many embodiments also include
monitoring the second sensor ensemble 2650 on the second machine
2600 through the predetermined route. The locations of machine 2400
being close to machine 2600 can be included in the contextual
metadata of both vibration surveys. The third ensemble 2850 can be
moved between machine 2800, machine 2950, and other suitable
machines. The machine 3000 has no sensors onboard as configured,
but could be monitored as needed by the third sensor ensemble 2850.
The machine 3000 and its operational characteristics can be
recorded in the metadata in relation to the vibration surveys on
the other machines to note its contribution due to its
proximity.
[0254] The many embodiments include hybrid database adaptation for
harmonizing relational metadata and streaming raw data formats.
Unlike older systems that utilized traditional database structure
for associating nameplate and operational parameters (sometimes
deemed metadata) with individual data measurements that are
discrete and relatively simple, it will be appreciated in light of
the disclosure that more modern systems can collect relatively
larger quantities of raw streaming data with higher sampling rates
and greater resolutions. At the same time, it will also be
appreciated in light of the disclosure that the network of metadata
with which to link and obtain this raw data or correlate with this
raw data, or both, is expanding at ever-increasing rates.
[0255] In one example, a single overall vibration level can be
collected as part of a route or prescribed list of measurement
points. This data collected can then be associated with database
measurement location information for a point located on a surface
of a bearing housing on a specific piece of the machine adjacent to
a coupling in a vertical direction. Machinery analysis parameters
relevant to the proper analysis can be associated with the point
located on the surface. Examples of machinery analysis parameters
relevant to the proper analysis can include a running speed of a
shaft passing through the measurement point on the surface. Further
examples of machinery analysis parameters relevant to the proper
analysis can include one of, or a combination of: running speeds of
all component shafts for that piece of equipment and/or machine,
bearing types being analyzed such as sleeve or rolling element
bearings, the number of gear teeth on gears should there be a
gearbox, the number of poles in a motor, slip and line frequency of
a motor, roller bearing element dimensions, number of fan blades,
or the like. Examples of machinery analysis parameters relevant to
the proper analysis can further include machine operating
conditions such as the load on the machines and whether load is
expressed in percentage, wattage, air flow, head pressure,
horsepower, and the like. Further examples of machinery analysis
parameters include information relevant to adjacent machines that
might influence the data obtained during the vibration study.
[0256] It will be appreciated in light of the disclosure that the
vast array of equipment and machinery types can support many
different classifications, each of which can be analyzed in
distinctly different ways. For example, some machines, like screw
compressors and hammer mills, can be shown to run much noisier and
can be expected to vibrate significantly more than other machines.
Machines known to vibrate more significantly can be shown to
require a change in vibration levels that can be considered
acceptable relative to quieter machines.
[0257] The present disclosure further includes hierarchical
relationships found in the vibrational data collected that can be
used to support proper analysis of the data. One example of the
hierarchical data includes the interconnection of mechanical
componentry such as a bearing being measured in a vibration survey
and the relationship between that bearing, including how that
bearing connects to a particular shaft on which is mounted a
specific pinion within a particular gearbox, and the relationship
between the shaft, the pinion, and the gearbox. The hierarchical
data can further include in what particular spot within a machinery
gear train that the bearing being monitored is located relative to
other components in the machine. The hierarchical data can also
detail whether the bearing being measured in a machine is in close
proximity to another machine whose vibrations may affect what is
being measured in the machine that is the subject of the vibration
study.
[0258] The analysis of the vibration data from the bearing or other
components related to one another in the hierarchical data can use
table lookups, searches for correlations between frequency patterns
derived from the raw data, and specific frequencies from the
metadata of the machine. In some embodiments, the above can be
stored in and retrieved from a relational database. In embodiments,
National Instrument's Technical Data Management Solution (TDMS)
file format can be used. The TDMS file format can be optimized for
streaming various types of measurement data (i.e., binary digital
samples of waveforms), as well as also being able to handle
hierarchical metadata
[0259] The many embodiments include a hybrid relational
metadata--binary storage approach (HRM-BSA). The HRM-BSA can
include a structured query language (SQL) based relational database
engine. The structured query language based relational database
engine can also include a raw data engine that can be optimized for
throughput and storage density for data that is flat and relatively
structureless. It will be appreciated in light of the disclosure
that benefits can be shown in the cooperation between the
hierarchical metadata and the SQL relational database engine. In
one example, marker technologies and pointer sign-posts can be used
to make correlations between the raw database engine and the SQL
relational database engine. Three examples of correlations between
the raw database engine and the SQL relational database engine
linkages include: (1) pointers from the SQL database to the raw
data; (2) pointers from the ancillary metadata tables or similar
grouping of the raw data to the SQL database; and (3) independent
storage tables outside the domain of either the SQL database or raw
data technologies.
[0260] With reference to FIG. 13, the present disclosure can
include pointers for Group 1 and Group 2 that can include
associated filenames, path information, table names, database key
fields as employed with existing SQL database technologies that can
be used to associate a specific database segments or locations,
asset properties to specific measurement raw data streams, records
with associated time/date stamps, or associated metadata such as
operating parameters, panel conditions, and the like. By way of
this example, a plant 3200 can include machine one 3202, machine
two 3204, and many others in the plant 3200. The machine one 3202
can include a gearbox 3210, a motor 3212, and other elements. The
machine two 3204 can include a motor 3220, and other elements. Many
waveforms 3230 including waveform 3240, waveform 3242, waveform
3244, and additional waveforms as needed can be acquired from the
machines 3202, 3204 in the plant 3200. The waveforms 3230 can be
associated with the local marker linking tables 3300 and the
linking raw data tables 3400. The machines 3202, 3204 and their
elements can be associated with linking tables having relational
databases 3500. The linking tables raw data tables 3400 and the
linking tables having relational databases 3500 can be associated
with the linking tables with optional independent storage tables
3600.
[0261] The present disclosure can include markers that can be
applied to a time mark or a sample length within the raw waveform
data. The markers generally fall into two categories: preset or
dynamic The preset markers can correlate to preset or existing
operating conditions (e.g., load, head pressure, air flow cubic
feet per minute, ambient temperature, RPMs, and the like.). These
preset markers can be fed into the data acquisition system
directly. In certain instances, the preset markers can be collected
on data channels in parallel with the waveform data (e.g.,
waveforms for vibration, current, voltage, etc.). Alternatively,
the values for the preset markers can be entered manually
[0262] For dynamic markers such as trending data, it can be
important to compare similar data like comparing vibration
amplitudes and patterns with a repeatable set of operating
parameters. One example of the present disclosure includes one of
the parallel channel inputs being a key phasor trigger pulse from
an operating shaft that can provide RPM information at the
instantaneous time of collection. In this example of dynamic
markers, sections of collected waveform data can be marked with
appropriate speeds or speed ranges.
[0263] The present disclosure can also include dynamic markers that
can correlate to data that can be derived from post processing and
analytics performed on the sample waveform. In further embodiments,
the dynamic markers can also correlate to post-collection derived
parameters including RPMs, as well as other operationally derived
metrics such as alarm conditions like a maximum RPM. In certain
examples, many modern pieces of equipment that are candidates for a
vibration survey with the portable data collection systems
described herein do not include tachometer information. This can be
true because it is not always practical or cost-justifiable to add
a tachometer even though the measurement of RPM can be of primary
importance for the vibration survey and analysis. It will be
appreciated that for fixed speed machinery obtaining an accurate
RPM measurement can be less important especially when the
approximate speed of the machine can be ascertained before-hand;
however, variable-speed drives are becoming more and more
prevalent. It will also be appreciated in light of the disclosure
that various signal processing techniques can permit the derivation
of RPM from the raw data without the need for a dedicated
tachometer signal.
[0264] In many embodiments, the RPM information can be used to mark
segments of the raw waveform data over its collection history.
Further embodiments include techniques for collecting instrument
data following a prescribed route of a vibration study. The dynamic
markers can enable analysis and trending software to utilize
multiple segments of the collection interval indicated by the
markers (e.g., two minutes) as multiple historical collection
ensembles, rather than just one as done in previous systems where
route collection systems would historically store data for only one
RPM setting. This could, in turn, be extended to any other
operational parameter such as load setting, ambient temperature,
and the like, as previously described. The dynamic markers,
however, that can be placed in a type of index file pointing to the
raw data stream can classify portions of the stream in homogenous
entities that can be more readily compared to previously collected
portions of the raw data stream
[0265] The many embodiments include the hybrid relational
metadata-binary storage approach that can use the best of
pre-existing technologies for both relational and raw data streams.
In embodiments, the hybrid relational metadata--binary storage
approach can marry them together with a variety of marker linkages.
The marker linkages can permit rapid searches through the
relational metadata and can allow for more efficient analyses of
the raw data using conventional SQL techniques with pre-existing
technology. This can be shown to permit utilization of many of the
capabilities, linkages, compatibilities, and extensions that
conventional database technologies do not provide.
[0266] The marker linkages can also permit rapid and efficient
storage of the raw data using conventional binary storage and data
compression techniques. This can be shown to permit utilization of
many of the capabilities, linkages, compatibilities, and extensions
that conventional raw data technologies provide such as TMDS
(National Instruments), UFF (Universal File Format such as UFF58),
and the like. The marker linkages can further permit using the
marker technology links where a vastly richer set of data from the
ensembles can be amassed in the same collection time as more
conventional systems. The richer set of data from the ensembles can
store data snapshots associated with predetermined collection
criterion and the proposed system can derive multiple snapshots
from the collected data streams utilizing the marker technology. In
doing so, it can be shown that a relatively richer analysis of the
collected data can be achieved. One such benefit can include more
trending points of vibration at a specific frequency or order of
running speed versus RPM, load, operating temperature, flow rates,
and the like, which can be collected for a similar time relative to
what is spent collecting data with a conventional system.
[0267] In embodiments, the platform 100 may include the local data
collection system 102 deployed in the environment 104 to monitor
signals from machines, elements of the machines and the environment
of the machines including heavy duty machines deployed at a local
job site or at distributed job sites under common control. The
heavy-duty machines may include earthmoving equipment, heavy duty
on-road industrial vehicles, heavy duty off-road industrial
vehicles, industrial machines deployed in various settings such as
turbines, turbomachinery, generators, pumps, pulley systems,
manifold and valve systems, and the like. In embodiments, heavy
industrial machinery may also include earth-moving equipment,
earth-compacting equipment, hauling equipment, hoisting equipment,
conveying equipment, aggregate production equipment, equipment used
in concrete construction, and piledriving equipment. In examples,
earth moving equipment may include excavators, backhoes, loaders,
bulldozers, skid steer loaders, trenchers, motor graders, motor
scrapers, crawler loaders, and wheeled loading shovels. In
examples, construction vehicles may include dumpers, tankers,
tippers, and trailers. In examples, material handling equipment may
include cranes, conveyors, forklift, and hoists. In examples,
construction equipment may include tunnel and handling equipment,
road rollers, concrete mixers, hot mix plants, road making machines
(compactors), stone crashers, pavers, slurry seal machines,
spraying and plastering machines, and heavy-duty pumps. Further
examples of heavy industrial equipment may include different
systems such as implement traction, structure, power train,
control, and information. Heavy industrial equipment may include
many different powertrains and combinations thereof to provide
power for locomotion and to also provide power to accessories and
onboard functionality. In each of these examples, the platform 100
may deploy the local data collection system 102 into the
environment 104 in which these machines, motors, pumps, and the
like, operate and directly connected integrated into each of the
machines, motors, pumps, and the like.
[0268] In embodiments, the platform 100 may include the local data
collection system 102 deployed in the environment 104 to monitor
signals from machines in operation and machines in being
constructed such as turbine and generator sets like Siemens.TM.
SGT6-5000F.TM. gas turbine, an SST-900.TM. steam turbine, an
SGen6-1000A.TM. generator, and an SGen6-100A.TM. generator, and the
like. In embodiments, the local data collection system 102 may be
deployed to monitor steam turbines as they rotate in the currents
caused by hot water vapor that may be directed through the turbine
but otherwise generated from a different source such as from
gas-fired burners, nuclear cores, molten salt loops and the like.
In these systems, the local data collection system 102 may monitor
the turbines and the water or other fluids in a closed loop cycle
in which water condenses and is then heated until it evaporates
again. The local data collection system 102 may monitor the steam
turbines separately from the fuel source deployed to heat the water
to steam. In examples, working temperatures of steam turbines may
be between 500 and 650.degree. C. In many embodiments, an array of
steam turbines may be arranged and configured for high, medium, and
low pressure, so they may optimally convert the respective steam
pressure into rotational movement.
[0269] The local data collection system 102 may also be deployed in
a gas turbines arrangement and therefore not only monitor the
turbine in operation but also monitor the hot combustion gases feed
into the turbine that may be in excess of 1,500.degree. C. Because
these gases are much hotter than those in steam turbines, the
blades may be cooled with air that may flow out of small openings
to create a protective film or boundary layer between the exhaust
gases and the blades. This temperature profile may be monitored by
the local data collection system 102. Gas turbine engines, unlike
typical steam turbines, include a compressor, a combustion chamber,
and a turbine all of which are journaled for rotation with a
rotating shaft. The construction and operation of each of these
components may be monitored by the local data collection system
102.
[0270] In embodiments, the platform 100 may include the local data
collection system 102 deployed in the environment 104 to monitor
signals from water turbines serving as rotary engines that may
harvest energy from moving water and are used for electric power
generation. The type of water turbine or hydro-power selected for a
project may be based on the height of standing water, often
referred to as head, and the flow (or volume of water) at the site.
In this example, a generator may be placed at the top of a shaft
that connects to the water turbine. As the turbine catches the
naturally moving water in its blade and rotates, the turbine sends
rotational power to the generator to generate electrical energy. In
doing so, the platform 100 may monitor signals from the generators,
the turbines, the local water system, flow controls such as dam
windows and sluices. Moreover, the platform 100 may monitor local
conditions on the electric grid including load, predicted demand,
frequency response, and the like, and include such information in
the monitoring and control deployed by platform 100 in these
hydroelectric settings.
[0271] In embodiments, the platform 100 may include the local data
collection system 102 deployed in the environment 104 to monitor
signals from energy production environments, such as thermal,
nuclear, geothermal, chemical, biomass, carbon-based fuels,
hybrid-renewable energy plants, and the like. Many of these plants
may use multiple forms of energy harvesting equipment like wind
turbines, hydro turbines, and steam turbines powered by heat from
nuclear, gas-fired, solar, and molten salt heat sources. In
embodiments, elements in such systems may include transmission
lines, heat exchangers, desulphurization scrubbers, pumps, coolers,
recuperators, chillers, and the like. In embodiments, certain
implementations of turbomachinery, turbines, scroll compressors,
and the like may be configured in arrayed control so as to monitor
large facilities creating electricity for consumption, providing
refrigeration, creating steam for local manufacture and heating,
and the like, and that arrayed control platforms may be provided by
the provider of the industrial equipment such as Honeywell and
their Experion.TM. PKS platform. In embodiments, the platform 100
may specifically communicate with and integrate the local
manufacturer-specific controls and may allow equipment from one
manufacturer to communicate with other equipment. Moreover, the
platform 100 provides allows for the local data collection system
102 to collect information across systems from many different
manufacturers. In embodiments, the platform 100 may include the
local data collection system 102 deployed in the environment 104 to
monitor signals from marine industrial equipment, marine diesel
engines, shipbuilding, oil and gas plants, refineries,
petrochemical plant, ballast water treatment solutions, marine
pumps and turbines, and the like.
[0272] In embodiments, the platform 100 may include the local data
collection system 102 deployed in the environment 104 to monitor
signals from heavy industrial equipment and processes including
monitoring one or more sensors. By way of this example, sensors may
be devices that may be used to detect or respond to some type of
input from a physical environment, such as an electrical, heat, or
optical signal. In embodiments, the local data collection system
102 may include multiple sensors such as, without limitation, a
temperature sensor, a pressure sensor, a torque sensor, a flow
sensor, a heat sensor, a smoke sensor, an arc sensor, a radiation
sensor, a position sensor, an acceleration sensor, a strain sensor,
a pressure cycle sensor, a pressure sensor, an air temperature
sensor, and the like. The torque sensor may encompass a magnetic
twist angle sensor. In one example, the torque and speed sensors in
the local data collection system 102 may be similar to those
discussed in U.S. Pat. No. 8,352,149 to Meachem, issued 8 Jan. 2013
and hereby incorporated by reference as if fully set forth herein.
In embodiments, one or more sensors may be provided such as a
tactile sensor, a biosensor, a chemical sensor, an image sensor, a
humidity sensor, an inertial sensor, and the like.
[0273] In embodiments, the platform 100 may include the local data
collection system 102 deployed in the environment 104 to monitor
signals from sensors that may provide signals for fault detection
including excessive vibration, incorrect material, incorrect
material properties, trueness to the proper size, trueness to the
proper shape, proper weight, trueness to balance. Additional fault
sensors include those for inventory control and for inspections
such as to confirm that parts are packaged to plan, parts are to
tolerance in a plan, occurrence of packaging damage or stress, and
sensors that may indicate the occurrence of shock or damage in
transit. Additional fault sensors may include detection of the lack
of lubrication, over lubrication, the need for cleaning of the
sensor detection window, the need for maintenance due to low
lubrication, the need for maintenance due to blocking or reduced
flow in a lubrication region, and the like.
[0274] In embodiments, the platform 100 may include the local data
collection system 102 deployed in the environment 104 that includes
aircraft operations and manufacture including monitoring signals
from sensors for specialized applications such as sensors used in
an aircraft's Attitude and Heading Reference System (AHRS), such as
gyroscopes, accelerometers, and magnetometers. In embodiments, the
platform 100 may include the local data collection system 102
deployed in the environment 104 to monitor signals from image
sensors such as semiconductor charge coupled devices (CCDs), active
pixel sensors, in complementary metal-oxide-semiconductor (CMOS) or
N-type metal-oxide-semiconductor (NMOS, Live MOS) technologies. In
embodiments, the platform 100 may include the local data collection
system 102 deployed in the environment 104 to monitor signals from
sensors such as an infra-red (IR) sensor, an ultraviolet (UV)
sensor, a touch sensor, a proximity sensor, and the like. In
embodiments, the platform 100 may include the local data collection
system 102 deployed in the environment 104 to monitor signals from
sensors configured for optical character recognition (OCR), reading
barcodes, detecting surface acoustic waves, detecting transponders,
communicating with home automation systems, medical diagnostics,
health monitoring, and the like.
[0275] In embodiments, the platform 100 may include the local data
collection system 102 deployed in the environment 104 to monitor
signals from sensors such as a Micro-Electro-Mechanical Systems
(MEMS) sensor, such as ST Microelectronic's.TM. LSM303AH smart MEMS
sensor, which may include an ultra-low-power high-performance
system-in-package featuring a 3D digital linear acceleration sensor
and a 3D digital magnetic sensor.
[0276] In embodiments, the platform 100 may include the local data
collection system 102 deployed in the environment 104 to monitor
signals from additional large machines such as turbines, windmills,
industrial vehicles, robots, and the like. These large mechanical
machines include multiple components and elements providing
multiple subsystems on each machine. To that end, the platform 100
may include the local data collection system 102 deployed in the
environment 104 to monitor signals from individual elements such as
axles, bearings, belts, buckets, gears, shafts, gear boxes, cams,
carriages, camshafts, clutches, brakes, drums, dynamos, feeds,
flywheels, gaskets, pumps, jaws, robotic arms, seals, sockets,
sleeves, valves, wheels, actuators, motors, servomotor, and the
like. Many of the machines and their elements may include
servomotors. The local data collection system 102 may monitor the
motor, the rotary encoder, and the potentiometer of the
servomechanism to provide three-dimensional detail of position,
placement, and progress of industrial processes.
[0277] In embodiments, the platform 100 may include the local data
collection system 102 deployed in the environment 104 to monitor
signals from gear drives, powertrains, transfer cases, multispeed
axles, transmissions, direct drives, chain drives, belt-drives,
shaft-drives, magnetic drives, and similar meshing mechanical
drives. In embodiments, the platform 100 may include the local data
collection system 102 deployed in the environment 104 to monitor
signals from fault conditions of industrial machines that may
include overheating, noise, grinding gears, locked gears, excessive
vibration, wobbling, under-inflation, over-inflation, and the like.
Operation faults, maintenance indicators, and interactions from
other machines may cause maintenance or operational issues may
occur during operation, during installation, and during
maintenance. The faults may occur in the mechanisms of the
industrial machines but may also occur in infrastructure that
supports the machine such as its wiring and local installation
platforms. In embodiments, the large industrial machines may face
different types of fault conditions such as overheating, noise,
grinding gears, excessive vibration of machine parts, fan vibration
problems, problems with large industrial machines rotating
parts.
[0278] In embodiments, the platform 100 may include the local data
collection system 102 deployed in the environment 104 to monitor
signals from industrial machinery including failures that may be
caused by premature bearing failure that may occur due to
contamination or loss of bearing lubricant. In another example, a
mechanical defect such as misalignment of bearings may occur. Many
factors may contribute to the failure such as metal fatigue,
therefore, the local data collection system 102 may monitor cycles
and local stresses. By way of this example, the platform 100 may
monitor the incorrect operation of machine parts, lack of
maintenance and servicing of parts, corrosion of vital machine
parts, such as couplings or gearboxes, misalignment of machine
parts, and the like. Though the fault occurrences cannot be
completely stopped, many industrial breakdowns may be mitigated to
reduce operational and financial losses. The platform 100 provides
real-time monitoring and predictive maintenance in many industrial
environments wherein it has been shown to present a cost-savings
over regularly-scheduled maintenance processes that replace parts
according to a rigid expiration of time and not actual load and
wear and tear on the element or machine. To that end, the platform
10 may provide reminders of, or perform some, preventive measures
such as adhering to operating manual and mode instructions for
machines, proper lubrication, and maintenance of machine parts,
minimizing or eliminating overrun of machines beyond their defined
capacities, replacement of worn but still functional parts as
needed, properly training the personnel for machine use, and the
like.
[0279] In embodiments, methods and systems of data collection in an
industrial environment may include a plurality of industrial
condition sensing and acquisition modules that may include at least
one programmable logic component per module that may control a
portion of the sensing and acquisition functionality of its module.
The programmable logic components on each of the modules may be
interconnected by a dedicated logic bus that may include data and
control channels. The dedicated logic bus may extend logically
and/or physically to other programmable logic components on other
sensing and acquisition modules. In embodiments, the programmable
logic components may be programmed via the dedicated
interconnection bus, via a dedicated programming portion of the
dedicated interconnection bus, via a program that is passed between
programmable logic components, sensing and acquisition modules, or
whole systems. A programmable logic component for use in an
industrial environment data sensing and acquisition system may be a
Complex Programmable Logic Device, an Application-Specific
Integrated Circuit, microcontrollers, and combinations thereof.
[0280] A programmable logic component in an industrial data
collection environment may perform control functions associated
with data collection. Control examples include power control of
analog channels, sensors, analog receivers, analog switches,
portions of logic modules (e.g., a logic board, system, and the
like) on which the programmable logic component is disposed,
self-power-up/down, self-sleep/wake up, and the like. Control
functions, such as these and others, may be performed in
coordination with control and operational functions of other
programmable logic components, such as other components on a single
data collection module and components on other such modules. Other
functions that a programmable logic component may provide may
include generation of a voltage reference, such as a precise
voltage reference for input signal condition detection. A
programmable logic component may generate, set, reset, adjust,
calibrate, or otherwise determine the voltage of the reference, its
tolerance, and the like. Other functions of a programmable logic
component may include enabling a digital phase lock loop to
facilitate tracking slowly transitioning input signals, and further
to facilitate detecting the phase of such signals. Relative phase
detection may also be implemented, including phase relative to
trigger signals, other analog inputs, on-board references (e.g.,
on-board timers), and the like. A programmable logic component may
be programmed to perform input signal peak voltage detection and
control input signal circuitry, such as to implement auto-scaling
of the input to an operating voltage range of the input. Other
functions that may be programmed into a programmable logic
component may include determining an appropriate sampling frequency
for sampling inputs independently of their operating frequencies. A
programmable logic component may be programmed to detect a maximum
frequency among a plurality of input signals and set a sampling
frequency for each of the input signals that is greater than the
detected maximum frequency.
[0281] A programmable logic component may be programmed to
configure and control data routing components, such as
multiplexers, crosspoint switches, analog-to-digital converters,
and the like, to implement a data collection template for the
industrial environment. A data collection template may be included
in a program for a programmable logic component. Alternatively, an
algorithm that interprets a data collection template to configure
and control data routing resources in the industrial environment
may be included in the program.
[0282] In embodiments, one or more programmable logic components in
an industrial environment may be programmed to perform smart-band
signal analysis and testing. Results of such analysis and testing
may include triggering smart band data collection actions, that may
include reconfiguring one or more data routing resources in the
industrial environment. A programmable logic component may be
configured to perform a portion of smart band analysis, such as
collection and validation of signal activity from one or more
sensors that may be local to the programmable logic component.
Smart band signal analysis results from a plurality of programmable
logic components may be further processed by other programmable
logic components, servers, machine learning systems, and the like
to determine compliance with a smart band.
[0283] In embodiments, one or more programmable logic components in
an industrial environment may be programmed to control data routing
resources and sensors for outcomes, such as reducing power
consumption (e.g., powering on/off resources as needed),
implementing security in the industrial environment by managing
user authentication, and the like. In embodiments, certain data
routing resources, such as multiplexers and the like, may be
configured to support certain input signal types. A programmable
logic component may configure the resources based on the type of
signals to be routed to the resources. In embodiments, the
programmable logic component may facilitate coordination of sensor
and data routing resource signal type matching by indicating to a
configurable sensor a protocol or signal type to present to the
routing resource. A programmable logic component may facilitate
detecting a protocol of a signal being input to a data routing
resource, such as an analog crosspoint switch and the like. Based
on the detected protocol, the programmable logic component may
configure routing resources to facilitate support and efficient
processing of the protocol. In an example, a programmable logic
component configured data collection module in an industrial
environment may implement an intelligent sensor interface
specification, such as IEEE 1451.2 intelligent sensor interface
specification.
[0284] In embodiments, distributing programmable logic components
across a plurality of data sensing, collection, and/or routing
modules in an industrial environment may facilitate greater
functionality and local inter-operational control. In an example,
modules may perform operational functions independently based on a
program installed in one or more programmable logic components
associated with each module. Two modules may be constructed with
substantially identical physical components, but may perform
different functions in the industrial environment based on the
program(s) loaded into programmable logic component(s) on the
modules. In this way, even if one module were to experience a
fault, or be powered down, other modules may continue to perform
their functions due at least in part to each having its own
programmable logic component(s). In embodiments, configuring a
plurality of programmable logic components distributed across a
plurality of data collection modules in an industrial environment
may facilitate scalability in terms of conditions in the
environment that may be sensed, the number of data routing options
for routing sensed data throughout the industrial environment, the
types of conditions that may be sensed, the computing capability in
the environment, and the like.
[0285] In embodiments, a programmable logic controller-configured
data collection and routing system may facilitate validation of
external systems for use as storage nodes, such as for a
distributed ledger, and the like. A programmable logic component
may be programmed to perform validation of a protocol for
communicating with such an external system, such as an external
storage node.
[0286] In embodiments, programming of programmable logic
components, such as CPLDs and the like may be performed to
accommodate a range of data sensing, collection and configuration
differences. In embodiments, reprogramming may be performed on one
or more components when adding and/or removing sensors, when
changing sensor types, when changing sensor configurations or
settings, when changing data storage configurations, when embedding
data collection template(s) into device programs, when adding
and/or removing data collection modules (e.g., scaling a system),
when a lower cost device is used that may limit functionality or
resources over a higher cost device, and the like. A programmable
logic component may be programmed to propagate programs for other
programmable components via a dedicated programmable logic device
programming channel, via a daisy chain programming architecture,
via a mesh of programmable logic components, via a hub-and-spoke
architecture of interconnected components, via a ring configuration
(e.g., using a communication token, and the like).
[0287] In embodiments, a system for data collection in an
industrial environment comprising distributed programmable logic
devices connected by a dedicated control bus may be deployed with
drilling machines in an oil and gas harvesting environment, such as
an oil and/or gas field. A drilling machine has many active
portions that may be operated, monitored, and adjusted during a
drilling operation. Sensors to monitor a crown block may be
physically isolated from sensors for monitoring a blowout preventer
and the like. To effectively maintain control of this wide range
and diverse disposition of sensors, programmable logic components,
such as Complex Programmable Logic Devices ("CPLD") may be
distributed throughout the drilling machine. While each CPLD may be
configured with a program to facilitate operation of a limited set
of sensors, at least portions of the CPLD may be connected by a
dedicated bus for facilitating coordination of sensor control,
operation and use. In an example, a set of sensors may be disposed
proximal to a mud pump or the like to monitor flow, density, mud
tank levels, and the like. One or more CPLD may be deployed with
each sensor (or a group of sensors) to operate the sensors and
sensor signal routing and collection resources. The CPLD in this
mud pump group may be interconnected by a dedicated control bus to
facilitate coordination of sensor and data collection resource
control and the like. This dedicated bus may extend physically
and/or logically beyond the mud pump control portion of the drill
machine so that CPLD of other portions (e.g., the crown block and
the like) may coordinate data collection and related activity
through portions of the drilling machine.
[0288] In embodiments, a system for data collection in an
industrial environment comprising distributed programmable logic
devices connected by a dedicated control bus may be deployed with
compressors in an oil and gas harvesting environment, such as an
oil and/or gas field. Compressors are used in the oil and gas
industry for compressing a variety of gases and purposes include
flash gas, gas lift, reinjection, boosting, vapor-recovery, casing
head and the like. Collecting data from sensors for these different
compressor functions may require substantively different control
regimes. Distributing CPLDs programmed with different control
regimes is an approach that may accommodate these diverse data
collection requirements. One or more CPLDs may be disposed with
sets of sensors for the different compressor functions. A dedicated
control bus may be used to facilitate coordination of control
and/or programming of CPLDs in and across compressor instances. In
an example, a CPLD may be configured to manage a data collection
infrastructure for sensors disposed to collect compressor-related
conditions for flash gas compression; a second CPLD or group of
CPLDs may be configured to manage a data collection infrastructure
for sensors disposed to collect compressor related conditions for
vapor-recovery gas compression. These groups of CPLDs may operate
control programs.
[0289] In embodiments, a system for data collection in an
industrial environment comprising distributed programmable logic
devices connected by a dedicated control bus may be deployed in a
refinery with turbines for oil and gas production, such as with
modular impulse steam turbines. A system for collection of data
from impulse steam turbines may be configured with a plurality of
condition sensing and collection modules adapted for specific
functions of an impulse steam turbine. Distributing CPLDs along
with these modules can facilitate adaptable data collection to suit
individual installations. As an example, blade conditions, such as
tip rotational rate, temperature rise of the blades, impulse
pressure, blade acceleration rate, and the like may be captured in
data collection modules configured with sensors for sensing these
conditions. Other modules may be configured to collect data
associated with valves (e.g., in a multi-valve configuration, one
or more modules may be configured for each valve or for a set of
valves), turbine exhaust (e g , radial exhaust data collection may
be configured differently than axial exhaust data collection),
turbine speed sensing may be configured differently for fixed
versus variable speed implementations, and the like. Additionally,
impulse gas turbine systems may be installed with other systems,
such as combined cycle systems, cogeneration systems, solar power
generation systems, wind power generation systems, hydropower
generation systems, and the like. Data collection requirements for
these installations may also vary. Using a CPLD-based, modular data
collection system that uses a dedicated interconnection bus for the
CPLDs may facilitate programming and/or reprogramming of each
module directly in place without having to shut down or physically
access each module.
[0290] Referring to FIG. 14, an exemplary embodiment of a system
for data collection in an industrial environment comprising
distributed CPLDs interconnected by a bus for control and/or
programming thereof is depicted. An exemplary data collection
module 7200 may comprise one or more CPLDs 7206 for controlling one
or more data collection system resources, such as sensors 7202 and
the like. Other data collection resources that a CPLD may control
may include crosspoint switches, multiplexers, data converters, and
the like. CPLDs on a module may be interconnected by a bus, such as
a dedicated logic bus 7204 that may extend beyond a data collection
module to CPLDs on other data collection modules. Data collection
modules, such as module 7200 may be configured in the environment,
such as on an industrial machine 7208 (e.g., an impulse gas
turbine) and/or 7210 (e.g., a co-generation system), and the like.
Control and/or configuration of the CPLDs may be handled by a
controller 7212 in the environment. Data collection and routing
resources and interconnection (not shown) may also be configured
within and among data collection modules 7200 as well as between
and among industrial machines 7208 and 7210, and/or with external
systems, such as Internet portals, data analysis servers, and the
like to facilitate data collection, routing, storage, analysis, and
the like.
[0291] An example system for data collection in an industrial
environment includes a number of industrial condition sensing and
acquisition modules, with a programmable logic component disposed
on each of the modules, where the programmable logic component
controls a portion of the sensing and acquisition functional of the
corresponding module. The system includes communication bus that is
dedicated to interconnecting the at least one programmable logic
component disposed on at least one of the plurality of modules,
wherein the communication bus extends to other programmable logic
components on other sensing and acquisition modules.
[0292] In certain further embodiments, a system includes the
programmable logic component programmed via the communication bus,
the communication bus including a portion dedicated to programming
of the programmable logic components, controlling a portion of the
sensing and acquisition functionality of a module by a power
control function such as: controlling power of a sensor, a
multiplexer, a portion of the module, and/or controlling a sleep
mode of the programmable logic component; controlling a portion of
the sensing and acquisition functionality of a module by providing
a voltage reference to a sensor and/or an analog-to-digital
converter disposed on the module, by detecting a relative the phase
of at least two analog signals derived from at least two sensors
disposed on the module; by controlling sampling of data provided by
at least one sensor disposed on the module; by detecting a peak
voltage of a signal provided by a sensor disposed on the module;
and/or by configuring at least one multiplexer disposed on the
module by specifying to the multiplexer a mapping of at least one
input and one output. In certain embodiments, the communication bus
extends to other programmable logic components on other condition
sensing and/or acquisition modules. In certain embodiments, a
module may be an industrial environment condition sensing module.
In certain embodiments, a module control program includes an
algorithm for implementing an intelligent sensor interface
communication protocol, such as an IEEE1451.2 compatible
intelligent sensor interface communication protocol. In certain
embodiments, a programmable logic component includes configuring
the programmable logic component and/or the sensing or acquisition
module to implement a smart band data collection template. Example
and non-limiting programmable logic components include field
programmable gate arrays, complex programmable logic devices,
and/or microcontrollers.
[0293] An example system includes a drilling machine for oil and
gas field use, with a condition sensing and/or acquisition module
to monitor aspects of a drilling machine. Without limitation, a
further example system includes monitoring a compressor and/or
monitoring an impulse steam engine.
[0294] In embodiments, a system for data collection in an
industrial environment may include a trigger signal and at least
one data signal that share a common output of a signal multiplexer
and upon detection of a condition in the industrial environment,
such as a state of the trigger signal, the common output is
switched to propagate either the data signal or the trigger signal.
Sharing an output between a data signal and a trigger signal may
also facilitate reducing a number of individually routed signals in
an industrial environment. Benefits of reducing individually routed
signals may include reducing the number of interconnections between
data collection module, thereby reducing the complexity of the
industrial environment. Trade-offs for reducing individually routed
signals may include increasing sophistication of logic at signal
switching modules to implement the detection and conditional
switching of signals. A net benefit of this added localized logic
complexity may be an overall reduction in the implementation
complexity of such a data collection system in an industrial
environment.
[0295] Exemplary deployment environments may include environments
with trigger signal channel limitations, such as existing data
collection systems that do not have separate trigger support for
transporting an additional trigger signal to a module with
sufficient computing sophistication to perform trigger detection.
Another exemplary deployment may include systems that require at
least some autonomous control for performing data collection.
[0296] In embodiments, a system for data collection in an
industrial environment may include an analog switch that switches
between a first input, such as a trigger input and a second input,
such as a data input based on a condition of the first input. A
trigger input may be monitored by a portion of the analog switch to
detect a change in the signal, such as from a lower voltage to a
higher voltage relative to a reference or trigger threshold
voltage. In embodiments, a device that may receive the switched
signal from the analog switch may monitor the trigger signal for a
condition that indicates a condition for switching from the trigger
input to the data input. When a condition of the trigger input is
detected, the analog switch may be reconfigured, to direct the data
input to the same output that was propagating the trigger
output.
[0297] In embodiments, a system for data collection in an
industrial environment may include an analog switch that directs a
first input to an output of the analog switch until such time as
the output of the analog switch indicates that a second input
should be directed to the output of the analog switch. The output
of the analog switch may propagate a trigger signal to the output.
In response to the trigger signal propagating through the switch
transitioning from a first condition (e.g., a first voltage below a
trigger threshold voltage value) to a second condition (e.g., a
second voltage above the trigger threshold voltage value), the
switch may stop propagating the trigger signal and instead
propagate another input signal to the output. In embodiments, the
trigger signal and the other data signal may be related, such as
the trigger signal may indicate a presence of an object being
placed on a conveyer and the data signal represents a strain placed
on the conveyer.
[0298] In embodiments, to facilitate timely detection of the
trigger condition, a rate of sampling of the output of the analog
switch may be adjustable, so that, for example, the rate of
sampling is higher while the trigger signal is propagated and lower
when the data signal is propagated. Alternatively, a rate of
sampling may be fixed for either the trigger or the data signal. In
embodiments, the rate of sampling may be based on a predefined time
from trigger occurrence to trigger detection and may be faster than
a minimum sample rate to capture the data signal.
[0299] In embodiments, routing a plurality of hierarchically
organized triggers onto another analog channel may facilitate
implementing a hierarchical data collection triggering structure in
an industrial environment. A data collection template to implement
a hierarchical trigger signal architecture may include signal
switch configuration and function data that may facilitate a signal
switch facility, such as an analog crosspoint switch or multiplexer
to output a first input trigger in a hierarchy, and based on the
first trigger condition being detected, output a second input
trigger in the hierarchy on the same output as the first input
trigger by changing an internal mapping of inputs to outputs. Upon
detection of the second input trigger condition, the output may be
switched to a data signal, such as data from a sensor in an
industrial environment.
[0300] In embodiments, upon detection of a trigger condition, in
addition to switching from the trigger signal to a data signal, an
alarm may be generated and optionally propagated to a higher
functioning device/module. In addition to switching to a data
signal, upon detection of a state of the trigger, sensors that
otherwise may be disabled or powered down may be
energized/activated to begin to produce data for the newly selected
data signal. Activating might alternatively include sending a reset
or refresh signal to the sensor(s).
[0301] In embodiments, a system for data collection in an
industrial environment may include a system for routing a trigger
signal onto a data signal path in association with a gearbox of an
industrial vehicle. Combining a trigger signal onto a signal path
that is also used for a data signal may be useful in gearbox
applications by reducing the number of signal lines that need to be
routed, while enabling advanced functions, such as data collection
based on pressure changes in the hydraulic fluid and the like. As
an example, a sensor may be configured to detect a pressure
difference in the hydraulic fluid that exceeds a certain threshold
as may occur when the hydraulic fluid flow is directed back into
the impeller to give higher torque at low speeds. The output of
such a sensor may be configured as a trigger for collecting data
about the gearbox when operating at low speeds. In an example, a
data collection system for an industrial environment may have a
multiplexer or switch that facilitates routing either a trigger or
a data channel over a single signal path. Detecting the trigger
signal from the pressure sensor may result in a different signal
being routed through the same line that the trigger signal was
routed by switching a set of controls. A multiplexer may, for
example, output the trigger signal until the trigger signal is
detected as indicating that the output should be changed to the
data signal. As a result of detecting the high-pressure condition,
a data collection activity may be activated so that data can be
collected using the same line that was recently used by the trigger
signal.
[0302] In embodiments, a system for data collection in an
industrial environment may include a system for routing a trigger
signal onto a data signal path in association with a vehicle
suspension for truck and car operation. Vehicle suspension,
particularly active suspension may include sensors for detecting
road events, suspension conditions, and vehicle data, such as
speed, steering, and the like. These conditions may not always need
to be detected, except, for example, upon detection of a trigger
condition. Therefore, combining the trigger condition signal and at
least one data signal on a single physical signal routing path
could be implemented. Doing so may reduce costs due to fewer
physical connections required in such a data collection system. In
an example, a sensor may be configured to detect a condition, such
as a pot hole, to which the suspension must react. Data from the
suspension may be routed along the same signal routing path as this
road condition trigger signal so that upon detection of the pot
hole, data may be collected that may facilitate determining aspects
of the suspension's reaction to the pot hole.
[0303] In embodiments, a system for data collection in an
industrial environment may include a system for routing a trigger
signal onto a data signal path in association with a turbine for
power generation in a power station. A turbine used for power
generation may be retrofitted with a data collection system that
optimizes existing data signal lines to implement greater data
collection functions. One such approach involves routing new
sources of data over existing lines. While multiplexing signals
generally satisfies this need, combining a trigger signal with a
data signal via a multiplexer or the like can further improve data
collection. In an example, a first sensor may include a thermal
threshold sensor that may measure the temperature of an aspect of a
power generation turbine. Upon detection of that trigger (e.g., by
the temperature rising above the thermal threshold), a data
collection system controller may send a different data collection
signal over the same line that was used to detect the trigger
condition. This may be accomplished by a controller or the like
sensing the trigger signal change condition and then signaling to
the multiplexer to switch from the trigger signal to a data signal
to be output on the same line as the trigger signal for data
collection. In this example, when a turbine is detected as having a
portion that exceeds its safe thermal threshold, a secondary safety
signal may be routed over the trigger signal path and monitored for
additional safety conditions, such as overheating and the like.
[0304] Referring to FIG. 15, an embodiment of routing a trigger
signal over a data signal path in a data collection system in an
industrial environment is depicted. Signal multiplexer 7400 may
receive a trigger signal on a first input from a sensor or other
trigger source 7404 and a data signal on a second input from a
sensor for detecting a temperature associated with an industrial
machine in the environment 7402. The multiplexer 7400 may be
configured to output the trigger signal onto an output signal path
7406. A data collection module 7410 may process the signal on the
data path 7406 looking for a change in the signal indicative of a
trigger condition provided from the trigger sensor 7404 through the
multiplexer 7400. Upon detection, a control output 7408 may be
changed and thereby control the multiplexer 7400 to start
outputting data from the temperature probe 7402 by switching an
internal switch or the like that may control one or more of the
inputs that may be routed to the output 7406. Data collection
facility 7410 may activate a data collection template in response
to the detected trigger that may include switching the multiplexer
and collecting data into triggered data storage 7412. Upon
completion of the data collection activity, multiplexer control
signal 7408 may revert to its initial condition so that trigger
sensor 7404 may be monitored again.
[0305] An example system for data collection in an industrial
environment includes an analog switch that directs a first input to
an output of the analog switch until such time as the output of the
analog switch indicates that a second input should be directed to
the output of the analog switch. In certain further embodiments,
the example system includes: where the output of the analog switch
indicated that the second input should be directed to the output
based on the output transitioning from a pending condition to a
triggered condition; wherein the triggered condition includes
detecting the output presenting a voltage above a trigger voltage
value; routing a number of signals with the analog switch from
inputs on the analog switch to outputs on the analog switch in
response to the output of the analog switch indicating that the
second input should be directed to the output; sampling the output
of the analog switch at a rate that exceeds a rate of transition
for a number of signals input to the analog switch; and/or
generating an alarm signal when the output of the analog switch
indicates that a second input should be directed to the output of
the analog switch.
[0306] An example system for data collection in an industrial
environment includes an analog switch that switches between a first
input and a second input based on a condition of the first input.
In certain further embodiments, the condition of the first input
comprises the first input presenting a triggered condition, and/or
the triggered condition includes detecting the first input
presenting a voltage above a trigger voltage value. In certain
embodiments, the analog switch includes routing a plurality of
signals with the analog from inputs on the analog switch to outputs
on the analog switch based on the condition of the first input,
sampling an input of the analog switch at a rate that exceeds a
rate of transition for a plurality of signals input to the analog
switch, and/or generating an alarm signal based on the condition of
the first input.
[0307] An example system for data collection in an industrial
environment includes a trigger signal and at least one data signal
that share a common output of a signal multiplexer, and upon
detection of a predefined state of the trigger signal, the common
output is configured to propagate the at least one data signal
through the signal multiplexer. In certain further embodiments, the
signal multiplexer is an analog multiplexer, the predefined state
of the trigger signal is detected on the common output, detection
of the predefined state of the trigger signal includes detecting
the common output presenting a voltage above a trigger voltage
value, the multiplexer includes routing a plurality of signals with
the multiplexer from inputs on the multiplexer to outputs on the
multiplexer in response to detection of the predefined state of the
trigger signal, the multiplexer includes sampling the output of the
multiplexer at a rate that exceeds a rate of transition for a
plurality of signals input to the multiplexer, the multiplexer
includes generating an alarm in response to detection of the
predefined state of the trigger signal, and/or the multiplexer
includes activating at least one sensor to produce the at least one
data signal. Without limitation, example systems include:
monitoring a gearbox of an industrial vehicle by directing a
trigger signal representing a condition of the gearbox to an output
of the analog switch until such time as the output of the analog
switch indicates that a second input representing a condition of
the gearbox related to the trigger signal should be directed to the
output of the analog switch; monitoring a suspension system of an
industrial vehicle by directing a trigger signal representing a
condition of the suspension to an output of the analog switch until
such time as the output of the analog switch indicates that a
second input representing a condition of the suspension related to
the trigger signal should be directed to the output of the analog
switch; and/or monitoring a power generation turbine by directing a
trigger signal representing a condition of the power generation
turbine to an output of the analog switch until such time as the
output of the analog switch indicates that a second input
representing a condition of the power generation turbine related to
the trigger signal should be directed to the output of the analog
switch.
[0308] In embodiments, a system for data collection in an
industrial environment may include a data collection system that
monitors at least one signal for a set of collection band
parameters and upon detection of a parameter from the set of
collection band parameters in the signal, configures collection of
data from a set of sensors based on the detected parameter. The set
of selected sensors, the signal, and the set of collection band
parameters may be part of a smart bands data collection template
that may be used by the system when collecting data in an
industrial environment. A motivation for preparing a smart-bands
data collection template may include monitoring a set of conditions
of an industrial machine to facilitate improved operation, reduce
down time, preventive maintenance, failure prevention, and the
like. Based on analysis of data about the industrial machine, such
as those conditions that may be detected by the set of sensors, an
action may be taken, such as notifying a user of a change in the
condition, adjusting operating parameters, scheduling preventive
maintenance, triggering data collection from additional sets of
sensors, and the like. An example of data that may indicate a need
for some action may include changes that may be detectable through
trends present in the data from the set of sensors. Another example
is trends of analysis values derived from the set of sensors.
[0309] In embodiments, the set of collection band parameters may
include values received from a sensor that is configured to sense a
condition of the industrial machine (e.g., bearing vibration).
However, a set of collection band parameters may instead be a trend
of data received from the sensor (e.g., a trend of bearing
vibration across a plurality of vibration measurements by a bearing
vibration sensor). In embodiments, a set of collection band
parameters may be a composite of data and/or trends of data from a
plurality of sensors (e.g., a trend of data from on-axis and
off-axis vibration sensors). In embodiments, when a data value
derived from one or more sensors as described herein is
sufficiently close to a value of data in the set of collection band
parameters, the data collection activity from the set of sensors
may be triggered. Alternatively, a data collection activity from
the set of sensors may be triggered when a data value derived from
the one or more sensors (e.g., trends and the like) falls outside
of a set of collection band parameters. In an example, a set of
data collection band parameters for a motor may be a range of
rotational speeds from 95% to 105% of a select operational
rotational speed. So long as a trend of rotational speed of the
motor stays within this range, a data collection activity may be
deferred. However, when the trend reaches or exceeds this range,
then a data collection activity, such as one defined by a smart
bands data collection template may be triggered.
[0310] In embodiments, triggering a data collection activity, such
as one defined by a smart bands data collection template, may
result in a change to a data collection system for an industrial
environment that may impact aspects of the system such as data
sensing, switching, routing, storage allocation, storage
configuration, and the like. This change to the data collection
system may occur in near real time to the detection of the
condition; however, it may be scheduled to occur in the future. It
may also be coordinated with other data collection activities so
that active data collection activities, such as a data collection
activity for a different smart bands data collection template, can
complete prior to the system being reconfigured to meet the smart
bands data collection template that is triggered by the sensed
condition meeting the smart bands data collection trigger.
[0311] In embodiments, processing of data from sensors may be
cumulative over time, over a set of sensors, across machines in an
industrial environment, and the like. While a sensed value of a
condition may be sufficient to trigger a smart bands data
collection template activity, data may need to be collected and
processed over time from a plurality of sensors to generate a data
value that may be compared to a set of data collection band
parameters for conditionally triggering the data collection
activity. Using data from multiple sensors and/or processing data,
such as to generate a trend of data values and the like may
facilitate preventing inconsequential instances of a sensed data
value being outside of an acceptable range from causing unwarranted
smart bands data collection activity. In an example, if a vibration
from a bearing is detected outside of an acceptable range
infrequently, then trending for this value over time may be useful
to detect if the frequency is increasing, decreasing, or staying
substantially constant or within a range of values. If the
frequency of such a value is found to be increasing, then such a
trend is indicative of changes occurring in operation of the
industrial machine as experienced by the bearing. An acceptable
range of values of this trended vibration value may be established
as a set of data collection band parameters against which vibration
data for the bearing will be monitored. When the trended vibration
value is outside of this range of acceptable values, a smart bands
data collection activity may be activated.
[0312] In embodiments, a system for data collection in an
industrial environment that supports smart band data collection
templates may be configured with data processing capability at a
point of sensing of one or more conditions that may trigger a smart
bands data collection template data collection activity, such as:
by use of an intelligent sensor that may include data processing
capabilities; by use of a programmable logic component that
interfaces with a sensor and processes data from the sensor; by use
of a computer processor, such as a microprocessor and the like
disposed proximal to the sensor; and the like. In embodiments,
processing of data collected from one or more sensors for detecting
a smart bands template data collection activity may be performed by
remote processors, servers, and the like that may have access to
data from a plurality of sensors, sensor modules, industrial
machines, industrial environments, and the like.
[0313] In embodiments, a system for data collection in an
industrial environment may include a data collection system that
monitors an industrial environment for a set of parameters, and
upon detection of at least one parameter, configures the collection
of data from a set of sensors and causes a data storage controller
to adapt a configuration of data storage facilities to support
collection of data from the set of sensors based on the detected
parameter. The methods and systems described herein for
conditionally changing a configuration of a data collection system
in an industrial environment to implement a smart bands data
collection template may further include changes to data storage
architectures. As an example, a data storage facility may be
disposed on a data collection module that may include one or more
sensors for monitoring conditions in an industrial environment.
This local data storage facility may typically be configured for
rapid movement of sensed data from the module to a next level
sensing or processing module or server. When a smart bands data
collection condition is detected, sensor data from a plurality of
sensors may need to be captured concurrently. To accommodate this
concurrent collection, the local memory may be reconfigured to
capture data from each of the plurality of sensors in a coordinated
manner, such as repeatedly sampling each of the sensors
synchronously, or with a known offset, and the like, to build up a
set of sensed data that may be much larger than would typically be
captured and moved through the local memory. A storage control
facility for controlling the local storage may monitor the movement
of sensor data into and out of the local data storage, thereby
ensuring safe movement of data from the plurality of sensors to the
local data storage and on to a destination, such as a server,
networked storage facility, and the like. The local data storage
facility may be configured so that data from the set of sensors
associated with a smart bands data collection template are securely
stored and readily accessible as a set of smart band data to
facilitate processing the smart band-specific data. As an example,
local storage may comprise non-volatile memory (NVM). To prepare
for data collection in response to a smart band data collection
template being triggered, portions of the NVM may be erased to
prepare the NVM to receive data as indicated in the template.
[0314] In embodiments, multiple sensors may be arranged into a set
of sensors for condition-specific monitoring. Each set, which may
be a logical set of sensors, may be selected to provide information
about elements in an industrial environment that may provide
insight into potential problems, root causes of problems, and the
like. Each set may be associated with a condition that may be
monitored for compliance with an acceptable range of values. The
set of sensors may be based on a machine architecture, hierarchy of
components, or a hierarchy of data that contributes to a finding
about a machine that may usefully be applied to maintaining or
improving performance in the industrial environment. Smart band
sensor sets may be configured based on expert system analysis of
complex conditions, such as machine failures and the like. Smart
band sensor sets may be arranged to facilitate knowledge gathering
independent of a particular failure mode or history. Smart band
sensor sets may be arranged to test a suggested smart band data
collection template prior to implementing it as part of an
industrial machine operations program. Gathering and processing
data from sets of sensors may facilitate determining which sensors
contribute meaningful data to the set, and those sensors that do
not contribute can be removed from the set. Smart band sensor sets
may be adjusted based on external data, such as industry studies
that indicate the types of sensor data that is most helpful to
reduce failures in an industrial environment.
[0315] In embodiments, a system for data collection in an
industrial environment may include a data collection system that
monitors at least one signal for compliance to a set of collection
band conditions and upon detection of a lack of compliance,
configures the collection of data from a predetermined set of
sensors associated with the monitored signal. Upon detection of a
lack of compliance, a collection band template associated with the
monitored signal may be accessed, and resources identified in the
template may be configured to perform the data collection. In
embodiments, the template may identify sensors to activate, data
from the sensors to collect, duration of collection or quantity of
data to be collected, destination (e.g., memory structure) to store
the collected data, and the like. In embodiments, a smart band
method for data collection in an industrial environment may include
periodic collection of data from one or more sensors configured to
sense a condition of an industrial machine in the environment. The
collected data may be checked against a set of criteria that define
an acceptable range of the condition. Upon validation that the
collected data is either approaching one end of the acceptable
limit or is beyond the acceptable range of the condition, data
collection may commence from a smart-band group of sensors
associated with the sensed condition based on a smart-band
collection protocol configured as a data collection template. In
embodiments, an acceptable range of the condition is based on a
history of applied analytics of the condition. In embodiments, upon
validation of the acceptable range being exceeded, data storage
resources of a module in which the sensed condition is detected may
be configured to facilitate capturing data from the smart band
group of sensors.
[0316] In embodiments, monitoring a condition to trigger a smart
band data collection template data collection action may be: in
response to: a regulation, such as a safety regulation; in response
to an upcoming activity, such as a portion of the industrial
environment being shut down for preventive maintenance; in response
to sensor data missing from routine data collection activities; and
the like. In embodiments, in response to a faulty sensor or sensor
data missing from a smart band template data collection activity,
one or more alternate sensors may be temporarily included in the
set of sensors so as to provide data that may effectively
substitute for the missing data in data processing algorithms.
[0317] In embodiments, smart band data collection templates may be
configured for detecting and gathering data for smart band analysis
covering vibration spectra, such as vibration envelope and current
signature for spectral regions or peaks that may be combinations of
absolute frequency or factors of machine related parameters,
vibration time waveforms for time-domain derived calculations
including, without limitation. RMS overall, peak overall, true
peak, crest factor, and the like; vibration vectors, spectral
energy humps in various regions (e.g., low-frequency region, high
frequency region, low orders, and the like); pressure-volume
analysis and the like.
[0318] In embodiments, a system for data collection that applies
smart band data collection templates may be applied to an
industrial environment, such as ball screw actuators in an
automated production environment. Smart band analysis may be
applied to ball screw actuators in industrial environments such as
precision manufacturing or positioning applications (e.g.,
semiconductor photolithography machines, and the like). As a
typical primary objective of using a ball screw is for precise
positioning, detection of variation in the positioning mechanism
can help avoid costly defective production runs. Smart bands
triggering and data collection may help in such applications by
detecting, through smart band analysis, potential variations in the
positioning mechanism such as in the ball screw mechanism, a worm
drive, a linear motor, and the like. In an example, data related to
a ball screw positioning system may be collected with a system for
data collection in an industrial environment as described herein. A
plurality of sensors may be configured to collect data such as
screw torque, screw direction, screw speed, screw step, screw home
detection, and the like. Some portion of this data may be processed
by a smart bands data analysis facility to determine if variances,
such as trends in screw speed as a function of torque, approach or
exceed an acceptable threshold. Upon such a determination, a data
collection template for the ball screw production system may be
activated to configure the data sensing, routing, and collection
resources of the data collection system to perform data collection
to facilitate further analysis. The smart band data collection
template facilitates rapid collection of data from other sensors
than screw speed and torque, such as position, direction,
acceleration, and the like by routing data from corresponding
sensors over one or more signal paths to a data collector. The
duration and order of collection of the data from these sources may
be specified in the smart bands data collection template so that
data required for further analysis is effectively captured.
[0319] In embodiments, a system for data collection that applies
smart band data collection templates to configure and utilize data
collection and routing infrastructure may be applied to ventilation
systems in mining environments. Ventilation provides a crucial role
in mining safety. Early detection of potential problems with
ventilation equipment can be aided by applying a smart bands
approach to data collection in such an environment. Sensors may be
disposed for collecting information about ventilation operation,
quality, and performance throughout a mining operation. At each
ventilation device, ventilation-related elements, such as fans,
motors, belts, filters, temperature gauges, voltage, current, air
quality, poison detection, and the like may be configured with a
corresponding sensor. While variation in any one element (e.g., air
volume per minute, and the like) may not be indicative of a
problem, smart band analysis may be applied to detect trends over
time that may be suggestive of potential problems with ventilation
equipment. To perform smart bands analysis, data from a plurality
of sensors may be required to form a basis for analysis. By
implementing data collection systems for ventilation stations, data
from a ventilation system may be captured. In an example, a smart
band analysis may be indicated for a ventilation station. In
response to this indication, a data collection system may be
configured to collect data by routing data from sensors disposed at
the ventilation station to a central monitoring facility that may
gather and analyze data from several ventilation stations.
[0320] In embodiments, a system for data collection that applies
smart band data collection templates to configure and utilize data
collection and routing infrastructure may be applied to drivetrain
data collection and analysis in mining environments. A drivetrain,
such as a drivetrain for a mining vehicle, may include a range of
elements that could benefit from use of the methods and systems of
data collection in an industrial environment as described herein.
In particular, smart band-based data collection may be used to
collect data from heavy duty mining vehicle drivetrains under
certain conditions that may be detectable by smart bands analysis.
A smart bands-based data collection template may be used by a
drivetrain data collection and routing system to configure sensors,
data paths, and data collection resources to perform data
collection under certain circumstances, such as those that may
indicate an unacceptable trend of drivetrain performance. A data
collection system for an industrial drivetrain may include sensing
aspects of a non-steering axle, a planetary steering axle,
driveshafts, (e.g., main and wing shafts), transmissions, (e g ,
standard, torque converters, long drop), and the like. A range of
data related to these operational parts may be collected. However,
data for support and structural members that support the drivetrain
may also need to be collected for thorough smart band analysis.
Therefore, collection across this wide range of drivetrain-related
components may be triggered based on a smart band analysis
determination of a need for this data. In an example, a smart band
analysis may indicate potential slippage between a main and wing
driveshaft that may represented by an increasing trend in response
delay time of the wing drive shaft to main drive shaft operation.
In response to this increasing trend, data collection modules
disposed throughout the mining vehicle's drive train may be
configured to route data from local sensors to be collected and
analyzed by data collectors. Mining vehicle drivetrain smart based
data collection may include a range of templates based on which
type of trend is detected. If a trend related to a steering axle is
detected, a data collection template to be implemented may be
different in sensor content, duration, and the like than for a
trend related to power demand for a normalized payload. Each
template could configure data sensing, routing, and collection
resources throughout the vehicle drive train accordingly.
[0321] Referring to FIG. 16, a system for data collection in an
industrial environment that facilitates data collection for smart
band analysis is depicted. A system for data collection in an
industrial environment may include a smart band analysis data
collection template repository 7600 in which smart band templates
7610 for data collection system configuration and collection of
data may be stored and accessed by a data collection controller
7602. The templates 7610 may include data collection system
configuration 7604 and operation information 7606 that may identify
sensors, collectors, signal paths, and information for initiation
and coordination of collection, and the like. A controller 7602 may
receive an indication, such as a command from a smart band analysis
facility 7608 to select and implement a specific smart band
template 7610. The controller 7602 may access the template 7610 and
configure the data collection system resources based on the
information in that template. In embodiments, the template may
identify: specific sensors; a multiplexer/switch configuration,
data collection trigger/initiation signals and/or conditions, time
duration and/or amount of data for collection; destination of
collected data; intermediate processing, if any; and any other
useful information, (e.g., instance identifier, and the like). The
controller 7602 may configure and operate the data collection
system to perform the collection for the smart band template and
optionally return the system configuration to a previous
configuration.
[0322] An example system for data collection in an industrial
environment includes a data collection system that monitors at
least one signal for a set of collection band parameters and, upon
detection of a parameter from the set of collection band
parameters, configures portions of the system and performs
collection of data from a set of sensors based on the detected
parameter. In certain further embodiments, the signal includes an
output of a sensor that senses a condition in the industrial
environment, where the set of collection band parameters comprises
values derivable from the signal that are beyond an acceptable
range of values derivable from the signal; where the at least one
signal includes an output of a sensor that senses a condition in
the industrial environment; wherein configuring portions of the
system includes configuring a storage facility to accept data
collected from the set of sensors; where configuring portions of
the system includes configuring a data routing portion includes at
least one of: an analog crosspoint switch, a hierarchical
multiplexer, an analog-to-digital converter, an intelligent sensor,
and/or a programmable logic component; wherein detection of a
parameter from the set of collection band parameters comprises
detecting a trend value for the signal being beyond an acceptable
range of trend values; and/or where configuring portions of the
system includes implementing a smart band data collection template
associated with the detected parameter. In certain embodiments, a
data collection system monitors a signal for data values within a
set of acceptable data values that represent acceptable collection
band conditions for the signal and, upon detection of a data value
for the at least one signal outside of the set of acceptable data
values, triggers a data collection activity that causes collecting
data from a predetermined set of sensors associated with the
monitored signal. In certain further embodiment, a data collection
system includes the signal including an output of a sensor that
senses a condition in the industrial environment; where the set of
acceptable data value includes values derivable from the signal
that are within an acceptable range of values derivable from the
signal; configuring a storage facility of the system to facilitate
collecting data from the predetermined set of sensors in response
to the detection of a data value outside of the set of acceptable
data values; configuring a data routing portion of the system
including an analog crosspoint switch, a hierarchical multiplexer,
an analog-to-digital converter, an intelligent sensor, and/or a
programmable logic component in response to detecting a data value
outside of the set of acceptable data values; where detection of a
data value for the signal outside of the set of acceptable data
values includes detecting a trend value for the signal being beyond
an acceptable range of trend values; and/or where the data
collection activity is defined by a smart band data collection
template associated with the detected parameter.
[0323] An example method for data collection in an industrial
environment comprising includes an operation to collect data from
sensor(s) configured to sense a condition of an industrial machine
in the environment; an operation to check the collected data
against a set of criteria that define an acceptable range of the
condition; and in response to the collected data violating the
acceptable range of the condition, an operation to collect data
from a smart-band group of sensors associated with the sensed
condition based on a smart-band collection protocol configured as a
smart band data collection template. In certain further
embodiments, a method includes where violating the acceptable range
of the condition includes a trend of the data from the sensor(s)
approaching a maximum value of the acceptable range; where the
smart-band group of sensors is defined by the smart band data
collection template; where the smart band data collection template
includes a list of sensors to activate, data from the sensors to
collect, duration of collection of data from the sensors, and/or a
destination location for storing the collected data; where
collecting data from a smart-band group of sensors includes
configuring at least one data routing resource of the industrial
environment that facilitates routing data from the smart band group
of sensors to a plurality of data collectors; and/or where the set
of criteria includes a range of trend values derived by processing
the data from sensor(s).
[0324] Without limitation, an example system monitors a ball screw
actuator in an automated production environment, and monitors at
least one signal from the ball screw actuator for a set of
collection band parameters and, upon detection of a parameter from
the set of collection band parameters, configures portions of the
system and performs collection of data from a set of sensors
disposed to monitor conditions of the ball screw actuator based on
the detected parameter; another example system monitors a
ventilation system in a mining environment, and monitors at least
one signal from the ventilation system for a set of collection band
parameters and, upon detection of a parameter from the set of
collection band parameters, configures portions of the system and
performs collection of data from a set of sensors disposed to
monitor conditions of the ventilation system based on the detected
parameter; an example system monitors a drivetrain of a mining
vehicle, and monitors at least one signal from the drive train for
a set of collection band parameters and, upon detection of a
parameter from the set of collection band parameters, configures
portions of the system and performs collection of data from a set
of sensors disposed to monitor conditions of the drivetrain based
on the detected parameter.
[0325] In embodiments, a system for data collection in an
industrial environment may automatically configure local and remote
data collection resources and may perform data collection from a
plurality of system sensors that are identified as part of a group
of sensors that produce data that is required to perform
operational deflection shape rendering. In embodiments, the system
sensors are distributed throughout structural portions of an
industrial machine in the industrial environment. In embodiments,
the system sensors sense a range of system conditions including
vibration, rotation, balance, friction, and the like. In
embodiments, automatically configuring is in response to a
condition in the environment being detected outside of an
acceptable range of condition values. In embodiments, a sensor in
the identified group of system sensors senses the condition.
[0326] In embodiments, a system for data collection in an
industrial environment may configure a data collection plan, such
as a template, to collect data from a plurality of system sensors
distributed throughout a machine to facilitate automatically
producing an operational deflection shape visualization ("ODSV")
based on machine structural information and a data set used to
produce an ODSV of the machine.
[0327] In embodiments, a system for data collection in an
industrial environment may configure a data collection template for
collecting data in an industrial environment by identifying sensors
disposed for sensing conditions of preselected structural members
of an industrial machine in the environment based on an ODSV of the
industrial machine. In embodiments, the template may include an
order and timing of data collection from the identified
sensors.
[0328] In embodiments, methods and systems for data collection in
an industrial environment may include a method of establishing an
acceptable range of sensor values for a plurality of industrial
machine condition sensors by validating an operational deflection
shape visualization of structural elements of the machine as
exhibiting deflection within an acceptable range, wherein data from
the plurality of sensors used in the validated ODSV define the
acceptable range of sensor values.
[0329] In embodiments, a system for data collection in an
industrial environment may include a plurality of data sources,
such as sensors, that may be grouped for coordinated data
collection to provide data required to produce an ODSV. Information
regarding the sensors to group, data collection coordination
requirements, and the like may be retrieved from an OD SV data
collection template. Coordinated data collection may include
concurrent data collection. To facilitate concurrent data
collection from a portion of the group of sensors, sensor routing
resources of the system for data collection may be configured, such
as by configuring a data multiplexer to route data from the portion
of the group of sensors to which it connects to data collectors. In
embodiments, each such source that connects an input of the
multiplexer may be routed within the multiplexer to separate
outputs so that data from all of the connected sources may be
routed on to data collection elements of the industrial
environment. In embodiments, the multiplexer may include data
storage capabilities that may facilitate sharing a common output
for at least a portion of the inputs. In embodiments, a multiplexer
may include data storage capabilities and data bus-enabled outputs
so that data for each source may be captured in a memory and
transmitted over a data bus, such as a data bus that is common to
the outputs of the multiplexer. In embodiments, sensors may be
smart sensors that may include data storage capabilities and may
send data from the data storage to the multiplexer in a coordinated
manner that supports use of a common output of the multiplexer
and/or use of a common data bus.
[0330] In embodiments, a system for data collection in an
industrial environment may comprise templates for configuring the
data collection system to collect data from a plurality of sensors
to perform ODSV for a plurality of deflection shapes. Individual
templates may be configured for visualization of looseness, soft
joints, bending, twisting, and the like. Individual deflection
shape data collection templates may be configured for different
portions of a machine in an industrial environment.
[0331] In embodiments, a system for data collection in an
industrial environment may facilitate operational deflection shape
visualization that may include visualization of locations of
sensors that contributed data to the visualization In the
visualization, each sensor that contributed data to generate the
visualization may be indicated by a visual element. The visual
element may facilitate user access to information about the sensor,
such as location, type, representative data contributed, path of
data from the sensor to a data collector, a deflection shape
template identifier, a configuration of a switch or multiplexer
through which the data is routed, and the like. The visual element
may be determined by associating sensor identification information
received from a sensor with information, such as a sensor map, that
correlates sensor identification information with physical location
in the environment. The information may appear in the visualization
in response to the visual element representing the sensor being
selected, such as by a user positioning a cursor on the sensor
visual element.
[0332] In embodiments, ODSV may benefit from data satisfying a
phase relationship requirement. A data collection system in the
environment may be configured to facilitate collecting data that
complies with the phase relationship requirement. Alternatively,
the data collection system may be configured to collect data from a
plurality of sensors that contains data that satisfies the phase
relationship requirements but may also include data that does not.
A post processing operation that may access phase detection data
may select a subset of the collected data.
[0333] In embodiments, a system for data collection in an
industrial environment may include a multiplexer receiving data
from a plurality of sensors and multiplexing the received data for
delivery to a data collector. The data collector may process the
data to facilitate ODSV. ODSV may require data from several
different sensors, and may benefit from using a reference signal,
such as data from a sensor, when processing data from the different
sensors. The multiplexer may be configured to provide data from the
different sensors, such as by switching among its inputs over time
so that data from each sensor may be received by the data
collector. However, the multiplexer may include a plurality of
outputs so that at least a portion of the inputs may be routed to
least two of the plurality of outputs. Therefore, in embodiments, a
multiple output multiplexer may be configured to facilitate data
collection that may be suitable for ODSV by routing a reference
signal from one of its inputs (e.g., data from an accelerometer) to
one of its outputs and multiplexing data from a plurality of its
outputs onto one or more of its outputs while maintaining the
reference signal output routing. A data collector may collect the
data from the reference output and use that to align the
multiplexed data from the other sensors.
[0334] In embodiments, a system for data collection in an
industrial environment may facilitate ODSV through coordinated data
collection related to conveyors for mining applications. Mining
operations may rely on conveyor systems to move material, supplies,
and equipment into and out of a mine. Mining operations may
typically operate around the clock; therefore, conveyor downtime
may have a substantive impact on productivity and costs. Advanced
analysis of conveyor and related systems that focuses on secondary
affects that may be challenging to detect merely through point
observation may be more readily detected via ODSV. Capturing
operational data related to vibration, stresses, and the like can
facilitate ODSV. However, coordination of data capture provides
more reliable results. Therefore, a data collection system that may
have sensors dispersed throughout a conveyor system can be
configured to facilitate such coordinated data collection. In an
example, capture of data affecting structural components of a
conveyor, such as; landing points and the horizontal members that
connect them and support the conveyer between landing points;
conveyer segment handoff points; motor mounts; mounts of conveyer
rollers and the like may need to be coordinated with data related
to conveyor dynamic loading, drive systems, motors, gates, and the
like. A system for data collection in an industrial environment,
such as a mining environment may include data sensing and
collection modules placed throughout the conveyor at locations such
as segment handoff points, drive systems, and the like. Each module
may be configured by one or more controllers, such as programmable
logic controllers, that may be connected through a physical or
logical (e.g., wireless) communication bus that aids in performing
coordinated data collection. To facilitate coordination, a
reference signal, such as a trigger and the like, may be
communicated among the modules for use when collecting data. In
embodiments, data collection and storage may be performed at each
module so as to reduce the need for real-time transfer of sensed
data throughout the mining environment. Transfer of data from the
modules to an ODSV processing facility may be performed after
collection, or as communication bandwidth between the modules and
the processing facility allows. ODSV can provide insight into
conditions in the conveyer, such as deflection of structural
members that may, over time cause premature failure. Coordinated
data collection with a data collection system for use in an
industrial environment, such as mining, can enable ODSV that may
reduce operating costs by reducing downtime due to unexpected
component failure.
[0335] In embodiments, a system for data collection in an
industrial environment may facilitate operational deflection shape
visualization through coordinated data collection related to fans
for mining applications. Fans provide a crucial function in mining
operations of moving air throughout a mine to provide ventilation,
equipment cooling, combustion exhaust evacuation, and the like.
Ensuring reliable and often continuous operation of fans may be
critical for miner safety and cost-effective operations. Dozens or
hundreds of fans may be used in large mining operations. Fans, such
as fans for ventilation management may include circuit, booster,
and auxiliary types. High capacity auxiliary fans may operate at
high speeds, over 2500 RPMs. Performing ODSV may reveal important
reliability information about fans deployed in a mining
environment. Collecting the range of data needed for ODSV of mining
fans may be performed by a system for collecting data in industrial
environments as described herein. In embodiments, sensing elements,
such as intelligent sensing and data collection modules may be
deployed with fans and/or fan subsystems. These modules may
exchange collection control information (e.g., over a dedicated
control bus and the like) so that data collection may be
coordinated in time and phase to facilitate ODSV.
[0336] A large auxiliary fan for use in mining may be constructed
for transportability into and through the mine and therefore may
include a fan body, intake and outlet ports, dilution valves,
protection cage, electrical enclosure, wheels, access panels, and
other structural and/or operational elements. The ODSV of such an
auxiliary fan may require collection of data from many different
elements. A system for data collection may be configured to sense
and collect data that may be combined with structural engineering
data to facilitate ODSV for this type of industrial fan.
[0337] Referring to FIG. 17, an embodiment of a system for data
collection in an industrial environment that performs coordinated
data collection suitable for ODSV is depicted. A system for data
collection in an industrial environment may include a ODSV data
collection template repository 7800 in which ODSV templates 7810
for data collection system configuration and collection of data may
be stored and accessed by a system for data collection controller
7802. The templates 7810 may include data collection system
configuration 7804 and operation information 7806 that may identify
sensors, collectors, signal paths, reference signal information,
information for initiation and coordination of collection, and the
like. A controller 7802 may receive an indication, such as a
command from a ODSV analysis facility 7808 to select and implement
a specific ODSV template 7810. The controller 7802 may access the
template 7810 and configure the data collection system resources
based on the information in that template. In embodiments, the
template may identify specific sensors, multiplexer/switch
configuration, reference signals for coordinating data collection,
data collection trigger/initiation signals and/or conditions, time
duration, and/or amount of data for collection, destination of
collected data, intermediate processing, if any, and any other
useful information (e.g., instance identifier, and the like). The
controller 7802 may configure and operate the data collection
system to perform the collection for the ODSV template and
optionally return the system configuration to a previous
configuration.
[0338] An example method of data collection for performing OD SV in
an industrial environment includes automatically configuring local
and remote data collection resources and collecting data from a
number of sensors using the configured resources, where the number
of sensors include a group of sensors that produce data that is
required to perform the ODSV. In certain further embodiments, an
example method further includes where the sensors are distributed
throughout structural portions of an industrial machine in the
industrial environment; where the sensors sense a range of system
conditions including vibration, rotation, balance, and/or friction;
where the automatically configuring is in response to a condition
in the environment being detected outside of an acceptable range of
condition values; where the condition is sensed by a sensor in a
group of system sensors; where automatically configuring includes
configuring a signal switching resource to concurrently connect a
portion of the group of sensors to data collection resources;
and/or where the signal switching resource is configured to
maintain a connection between a reference sensor and the data
collection resources throughout a period of collecting data from
the sensors to perform ODSV.
[0339] An example method of data collection in an industrial
environment includes configuring a data collection plan to collect
data from a number of system sensors distributed throughout a
machine in the industrial environment, the plan based on machine
structural information and an indication of data needed to produce
an ODS V of the machine; configuring data sensing, routing and
collection resources in the environment based on the data
collection plan; and collecting data based on the data collection
plan. In certain further embodiments, an example method further
includes: producing the ODSV; where the configuring data sensing,
routing, and collection resources is in response to a condition in
the environment being detected outside of an acceptable range of
condition values; where the condition is sensed by a sensor
identified in the data collection plan; where configuring resources
includes configuring a signal switching resource to concurrently
connect the plurality of system sensors to data collection
resources; and/or where the signal switching resource is configured
to maintain a connection between a reference sensor and the data
collection resources throughout a period of collecting data from
the sensors to perform OD SV.
[0340] An example system for data collection in an industrial
environment includes: a number of sensors disposed throughout the
environment; multiplexer that connects signals from the plurality
of sensors to data collection resources; and a processor for
processing data collected from the number of sensors in response to
the data collection template, where the processing results in an
ODSV of a portion of a machine disposed in the environment. In
certain further embodiments, an example system includes: where the
ODSV collection template further identifies a condition in the
environment on which performing data collection from the identified
sensors is dependent; where the condition is sensed by a sensor
identified in the ODSV data collection template; where the data
collection template specified inputs of the multiplexer to
concurrently connect to data collection resources; where the
multiplexer is configured to maintain a connection between a
reference sensor and the data collection resources throughout a
period of collecting data from the sensors to perform ODSV; where
the ODSV data collection template specifies data collection
requirements for performing ODSV for looseness, soft joints,
bending, and/or twisting of a portion of a machine in the
industrial environment; and/or where the ODSV collection template
specifies an order and timing of data collection from a plurality
of identified sensors.
[0341] An example method of monitoring a mining conveyer for
performing ODSV of the conveyer includes automatically configuring
local and remote data collection resources and collecting data from
a number of sensors disposed to sense the mining conveyor using the
configured resources, wherein the plurality of sensors comprise a
group of sensors that produce data that is required to perform the
operational deflection shape visualization of a portion of the
conveyor. An example method of monitoring a mining fan for
performing ODSV of the fan includes automatically configuring local
and remote data collection resources collecting data from a number
of sensors disposed to sense the fan using the configured
resources, and where the number of sensors include a group of
sensors that produce data that is sufficient or required to perform
ODSV of a portion of the fan.
[0342] In embodiments, a system for data collection in an
industrial environment may include a hierarchical multiplexer that
facilitates successive multiplexing of input data channels
according to a configurable hierarchy, such as a user configurable
hierarchy. The system for data collection in an industrial
environment may include the hierarchical multiplexer that
facilitates successive multiplexing of a plurality of input data
channels according to a configurable hierarchy. The hierarchy may
be automatically configured by a controller based on an operational
parameter in the industrial environment, such as a parameter of a
machine in the industrial environment.
[0343] In embodiments, a system for data collection in an
industrial environment may include a plurality of sensors that may
output data at different rates. The system may also include a
multiplexer module that receives sensor outputs from a first
portion of the plurality of sensors with similar output rates into
separate inputs of a first hierarchical multiplexer of the
multiplexer module. The first hierarchical multiplexer of the
multiplexer module may provide at least one multiplexed output of a
portion of its inputs to a second hierarchical multiplexer that
receives sensor outputs from a second portion of the plurality of
sensors with similar output rates and that provides at least one
multiplexed output of a portion of its inputs. In embodiments, the
output rates of the first set of sensors may be slower than the
output rates of the second set of sensors. In embodiments, data
collection rate requirements of the first set of sensors may be
lower than the data collection rate requirements of the second set
of sensors. In embodiments, the first hierarchical multiplexer
output is a time-multiplexed combination of a portion of its
inputs. In embodiments, the second hierarchical multiplexer
receives sensor signals with output rates that are similar to a
rate of output of the first multiplexer, wherein the first
multiplexer produces time-based multiplexing of the portion of its
plurality of inputs.
[0344] In embodiments, a system for data collection in an
industrial environment may include a hierarchical multiplexer that
is dynamically configured based on a data acquisition template. The
hierarchical multiplexer may include a plurality of inputs and a
plurality of outputs, wherein any input can be directed to any
output in response to sensor output collection requirements of the
template, and wherein a subset of the inputs can be multiplexed at
a first switching rate and output to at least one of the plurality
of outputs.
[0345] In embodiments, a system for data collection in an
industrial environment may include a plurality of sensors for
sensing conditions of a machine in the environment, a hierarchical
multiplexer, a plurality of analog-to-digital converters (ADCs), a
processor, local storage, and an external interface. The system may
use the processor to access a data acquisition template of
parameters for data collection from a portion of the plurality of
sensors, configure the hierarchical multiplexer, the ADCs and the
local storage to facilitate data collection based on the defined
parameters, and execute the data collection with the configured
elements including storing a set of data collected from a portion
of the plurality of sensors into the local storage. In embodiments,
the ADCs convert analog sensor data into a digital form that is
compatible with the hierarchical multiplexer. In embodiments, the
processor monitors at least one signal generated by the sensors for
a trigger condition and, upon detection of the trigger condition,
responds by at least one of communicating an alert over the
external interface and performing data acquisition according to a
template that corresponds to the trigger condition.
[0346] In embodiments, a system for data collection in an
industrial environment may include a hierarchical multiplexer that
may be configurable based on a data collection template of the
environment. The multiplexer may support receiving a large number
of data signals (e.g., from sensors in the environment)
simultaneously. In embodiments, all sensors for a portion of an
industrial machine in the environment may be individually connected
to inputs of a first stage of the multiplexer. The first stage of
the multiplexer may provide a plurality of outputs that may feed
into a second multiplexer stage. The second stage multiplexer may
provide multiple outputs that feed into a third stage, and so on.
Data collection templates for the environment may be configured for
certain data collection sets, such as a set to determine
temperature throughout a machine or a set to determine vibration
throughout a machine, and the like. Each template may identify a
plurality of sensors in the environment from which data is to be
collected, such as during a data collection event. When a template
is presented to the hierarchical multiplexer, mapping of inputs to
outputs for each multiplexing stage may be configured so that the
required data is available at output(s) of a final multiplexing
hierarchical stage for data collection. In an example, a data
collection template to collect a set of data to determine
temperature throughout a machine in the environment may identify
many temperature sensors. The first stage multiplexer may respond
to the template by selecting all of the available inputs that
connect to temperature sensors. The data from these sensors maybe
multiplexed onto multiple inputs of a second stage sensor that may
perform time-based multiplexing to produce a time-multiplexed
output(s) of temperature data from a portion of the sensors. These
outputs may be gathered by a data collector and de-multiplexed into
individual sensor temperature readings.
[0347] In embodiments, time-sensitive signals, such as triggers and
the like, may connect to inputs that directly connect to a final
multiplexer stage, thereby reducing any potential delay caused by
routing through multiple multiplexing stages.
[0348] In embodiments, a hierarchical multiplexer in a system for
data collection in an industrial environment may comprise an array
of relays, a programmable logic component, such as a CPLD, a field
programmable gate array (FPGA), and the like.
[0349] In embodiments, a system for data collection in an
industrial environment that may include a hierarchical multiplexer
for routing sensor outputs onto signal paths may be used with
explosive systems in mining applications. Blast initiating and
electronic blasting systems may be configured to provide computer
assisted blasting systems. Ensuring that blasting occurs safely may
involve effective sensing and analysis of a range of conditions. A
system for data collection in an industrial environment may be
deployed to sense and collect data associated with explosive
systems, such as explosive systems used for mining A data
collection system can use a hierarchical multiplexer to capture
data from explosive system installations automatically by aligning,
for example, a deployment of the explosive system including its
layout plans, integration, interconnectivity, cascading plan, and
the like with the hierarchical multiplexer. An explosive system may
be deployed with a form of hierarchy that starts with a primary
initiator and follows detonation connections through successive
layers of electronic blast control to sequenced detonation. Data
collected from each of these layers of blast systems configuration
may be associated with stages of a hierarchical multiplexer so that
data collected from bulk explosive detonation can be captured in a
hierarchy that corresponds to its blast control hierarchy.
[0350] In embodiments, a system for data collection in an
industrial environment that may include a hierarchical multiplexer
for routing sensor outputs onto signal paths may be used with
refinery blowers in oil and gas pipeline applications. Refinery
blower applications include fired heater combustion air preheat
systems and the like. Forced draft blowers may include a range of
moving and moveable parts that may benefit from condition sensing
and monitoring. Sensing may include detecting conditions of:
couplings (e.g., temperature, rotational rate, and the like);
motors (vibration, temperature, RPMs, torque, power usage, and the
like); louver mechanics (actuators, louvers, and the like); and
plenums (flow rate, blockage, back pressure, and the like). A
system for data collection in an industrial environment that uses a
hierarchical multiplexer for routing signals from sensors and the
like to data collectors may be configured to collect data from a
refinery blower. In an example, a plurality of sensors may be
deployed to sense air flow into, throughout, and out of a forced
draft blower used in a refinery application, such as to preheat
combustion air. Sensors may be grouped based on a frequency of a
signal produced by sensors. Sensors that detect louver position and
control may produce data at a lower rate than sensors that detect
blower RPMs. Therefore, louver position and control sensor signals
can be applied to a lower stage in a multiplexer hierarchy than the
blower RPM sensors because data from louvers change less often than
data from RPM sensors. A data collection system could switch among
a plurality of louver sensors and still capture enough information
to properly detect louver position. However, properly detecting
blower RPM data may require greater bandwidth of connection between
the blower RPM sensor and a data collector. A hierarchical
multiplexer may enable capturing blower RPM data at a rate that is
required for proper detection (perhaps by outputting the RPM sensor
data for long durations of time), while switching among several
louver sensor inputs and directing them onto (or through) an output
that is different than the blower RPM output. Alternatively, the
louver inputs may be time-multiplexed with the blower RPM data onto
a single output that can be de-multiplexed by a data collector that
is configured to determine when blower RPM data is being output and
when louver position data is being output.
[0351] In embodiments, a system for data collection in an
industrial environment that may include a hierarchical multiplexer
for routing sensor outputs onto signal paths may be used with
pipeline-related compressors (e.g., reciprocating) in oil and gas
pipeline applications. A typical use of a reciprocating compressor
for pipeline application is production of compressed air for
pipeline testing. A system for data collection in an industrial
environment may apply a hierarchical multiplexer while collecting
data from a pipeline testing-based reciprocating compressor. Data
from sensors deployed along a portion of a pipeline being tested
may be input to the lowest stage of the hierarchical multiplexer
because these sensors may be periodically sampled prior to and
during testing. However, the rate of sampling may be low relative
to sensors that detect compressor operation, such as parts of the
compressor that operate at higher frequencies, such as the
reciprocating linkage, motor, and the like. The sensors that
provide data at frequencies that enable reproduction of the
detected motion may be input to higher stages in the hierarchical
multiplexer. Time multiplexing among the pipeline sensors may
provide for coverage of a large number of sensors while capturing
events such as seal leakage and the like. However, time
multiplexing among reciprocating linkage sensors may require output
signal bandwidth that may exceed the bandwidth available for
routing data from the multiplexer to a data collector. Therefore,
in embodiments, a plurality of pipeline sensors may be
time-multiplexed onto a single multiplexer output and a compressor
sensor detecting rapidly moving parts, such as the compressor
motor, may be routed to separate outputs of the multiplexer.
[0352] Referring to FIG. 18, a system for data collection in an
industrial environment that uses a hierarchical multiplexer for
routing sensor signals to data collectors is depicted. Outputs from
a plurality of sensors, such as sensors that monitor conditions
that change with relatively low frequency (e.g., blower louver
position sensors) may be input to a lowest hierarchical stage 8000
of a hierarchical multiplexer 8002 and routed to successively
higher stages in the multiplexer, ultimately being output from the
multiplexer, perhaps as a time-multiplexed signal comprising
time-specific samples of each of the plurality of low frequency
sensors. Outputs from a second plurality of sensors, such as
sensors that monitor motor operation that may run at more than 1000
RPMs may be input to a higher hierarchical stage 8004 of the
hierarchical multiplexer and routed to outputs that support the
required bandwidth
[0353] An example system for data collection in an industrial
environment includes a controller for controlling data collection
resources in the industrial environment and a hierarchical
multiplexer that facilitates successive multiplexing of a number of
input data channels according to a configurable hierarchy, wherein
the hierarchy is automatically configured by the controller based
on an operational parameter of a machine in the industrial
environment. In certain further embodiments, an example system
includes: where the operational parameter of the machine is
identified in a data collection template; where the hierarchy is
automatically configured in response to smart band data collection
activation further including an analog-to-digital converter
disposed between a source of the input data channels and the
hierarchical multiplexer; and/or where the operational parameter of
the machine comprises a trigger condition of at least one of the
data channels. Another example system for data collection in an
industrial environment includes a plurality of sensors and a
multiplexer module that receives sensor outputs from a first
portion of the sensors with similar output rates into separate
inputs of a first hierarchical multiplexer that provides at least
one multiplexed output of a portion of its inputs to a second
hierarchical multiplexer, the second hierarchical multiplexer
receiving sensor outputs from a second portion of the sensors and
providing at least one multiplexed output of a portion of its
inputs. In certain further embodiments, an example system includes:
where the second portion of the sensors output data at rates that
are higher than the output rates of the first portion of the
sensors; where the first portion and the second portion of the
sensors output data at different rates; where the first
hierarchical multiplexer output is a time-multiplexed combination
of a portion of its inputs; where the second multiplexer receives
sensor signals with output rates that are similar to a rate of
output of the first multiplexer; and/or where the first multiplexer
produces time-based multiplexing of the portion of its inputs.
[0354] An example system for data collection in an industrial
environment includes a number of sensors for sensing conditions of
a machine in the environment a hierarchical multiplexer, a number
of analog-to-digital converters, a controller, local storage, an
external interface, where the system includes using the controller
to access a data acquisition template that defines parameters for
data collection from a portion of the sensors, to configure the
hierarchical multiplexer, the ADCs, and the local storage to
facilitate data collection based on the defined parameters, and to
execute the data collection with the configured elements including
storing a set of data collected from a portion of the sensors into
the local storage. In certain further embodiments, an example
system includes: where the ADCs convert analog sensor data into a
digital form that is compatible with the hierarchical multiplexer;
where the processor monitors at least one signal generated by the
sensors for a trigger condition and, upon detection of the trigger
condition, responds by communicating an alert over the external
interface and/or performing data acquisition according to a
template that corresponds to the trigger condition; where the
hierarchical multiplexer performs successive multiplexing of data
received from the sensors according to a configurable hierarchy;
where the hierarchy is automatically configured by the controller
based on an operational parameter of a machine in the industrial
environment; where the operational parameter of the machine is
identified in a data collection template; where the hierarchy is
automatically configured in response to smart band data collection
activation; the system further including an ADC disposed between a
source of the input data channels and the hierarchical multiplexer;
where the operational parameter of the machine includes a trigger
condition of at least one of the data channels; where the
hierarchical multiplexer performs successive multiplexing of data
received from the plurality of sensors according to a configurable
hierarchy; and/or where the hierarchy is automatically configured
by a controller based on a detected parameter of an industrial
environment. Without limitation, n example system is configured for
monitoring a mining explosive system, and includes a controller for
controlling data collection resources associated with the explosive
system, and a hierarchical multiplexer that facilitates successive
multiplexing of a number of input data channels according to a
configurable hierarchy, where the hierarchy is automatically
configured by the controller based on a configuration of the
explosive system. Without limitation, an example system is
configured for monitoring a refinery blower in an oil and gas
pipeline applications, and includes a controller for controlling
data collection resources associated with the refinery blower, and
a hierarchical multiplexer that facilitates successive multiplexing
of a number of input data channels according to a configurable
hierarchy, where the hierarchy is automatically configured by the
controller based on a configuration of the refinery blower. Without
limitation, an example system is configured for monitoring a
reciprocating compressor in an oil and gas pipeline applications
comprising, and includes controller for controlling data collection
resources associated with the reciprocating compressor, and a
hierarchical multiplexer that facilitates successive multiplexing
of a number of input data channels according to a configurable
hierarchy, where the hierarchy is automatically configured by the
controller based on a configuration of the reciprocating
compressor.
[0355] Industrial components such as pumps, compressors, air
conditioning units, mixers, agitators, motors, and engines may play
critical roles in the operation of equipment in a variety of
environments including as part of manufacturing equipment in
industrial environments such as factories, gas handling systems,
mining operations, automotive systems, and the like.
[0356] There are a wide variety of pumps such as a variety of
positive displacement pumps, velocity pumps, and impulse pumps.
Velocity or centrifugal pumps typically comprise an impeller with
curved blades which, when an impeller is immersed in a fluid, such
as water or a gas, causes the fluid or gas to rotate in the same
rotational direction as the impeller. As the fluid or gas rotates,
centrifugal force causes it to move to the outer diameter of the
pump, e.g., the pump housing, where it can be collected and further
processed. The removal of the fluid or gas from the outer
circumference may result in lower pressure at a pump input orifice
causing new fluid or gas to be drawn into the pump.
[0357] Positive displacement pumps may comprise reciprocating
pumps, progressive cavity pumps, gear or screw pumps, such as
reciprocating pumps typically comprise a piston which alternately
creates suction, which opens an inlet valve and draws a liquid or
gas into a cylinder, and pressure, which closes the inlet valve and
forces the liquid or gas present out of the cylinder through an
outlet valve. This method of pumping may result in periodic waves
of pressurized liquid or gas being introduced into the downstream
system.
[0358] Some automotive vehicles such as cars and trucks may use a
water cooling system to keep the engine from overheating. In some
automobiles, a centrifugal water pump, driven by a belt associated
with a driveshaft of the vehicle, is used to force a mixture of
water and coolant through the engine to maintain an acceptable
engine temperature. Overheating of the engine may be highly
destructive to the engine and yet it may be difficult or costly to
access a water pump installed in a vehicle.
[0359] In embodiments, a vehicle water pump may be equipped with a
plurality of sensors for measuring attributes associated with the
water pump such as temperature of bearings or pump housing,
vibration of a driveshaft associated with the pump, liquid leakage,
and the like. These sensors may be connected either directly to a
monitoring device or through an intermediary device using a mix of
wired and wireless connection techniques. A monitoring device may
have access to detection values corresponding to the sensors where
the detection values correspond directly to the sensor output or a
processed version of the data output such as a digitized or sampled
version of the sensor output, and/or a virtual sensor or modeled
value correlated from other sensed values. The monitoring device
may access and process the detection values using methods discussed
elsewhere herein to evaluate the health of the water pump and
various components of the water pump prone to wear and failure,
e.g., bearings or sets of bearings, drive shafts, motors, and the
like. The monitoring device may process the detection values to
identify a torsion of the drive shaft of the pump. The identified
torsion may then be evaluated relative to expected torsion based on
the specific geometry of the water pump and how it is installed in
the vehicle. Unexpected torsion may put undue stress on the
driveshaft and may be a sign of deteriorating health of the pump.
The monitoring device may process the detection values to identify
unexpected vibrations in the shaft or unexpected temperature values
or temperature changes in the bearings or in the housing in
proximity to the bearings. In some embodiments, the sensors may
include multiple temperature sensors positioned around the water
pump to identify hot spots among the bearings or across the pump
housing which might indicate potential bearing failure. The
monitoring device may process the detection values associated with
water sensors to identify liquid leakage near the pump which may
indicate a bad seal. The detection values may be jointly analyzed
to provide insight into the health of the pump.
[0360] In an illustrative example, detection values associated with
a vehicle water pump may show a sudden increase in vibration at a
higher frequency than the operational rotation of the pump with a
corresponding localized increase of temperature associated with a
specific phase in the pump cycle. Together these may indicate a
localized bearing failure.
[0361] Production lines may also include one or more pumps for
moving a variety of material including acidic or corrosive
materials, flammable materials, minerals, fluids comprising
particulates of varying sizes, high viscosity fluids, variable
viscosity fluids, or high-density fluids. Production line pumps may
be designed to specifically meet the needs of the production line
including pump composition to handle the various material types, or
torque needed to move the fluid at the desired speed or with the
desired pressure. Because these production lines may be continuous
process lines, it may be desirable to perform proactive maintenance
rather than wait for a component to fail. Variations in pump speed
and pressure may have the potential to negatively impact the final
product, and the ability to identify issues in the final product
may lag the actual component deterioration by an unacceptably long
period.
[0362] In embodiments, an industrial pump may be equipped with a
plurality of sensors for measuring attributes associated with the
pump such as temperature of bearings or pump housing, vibration of
a driveshaft associated with the pump, vibration of input or output
lines, pressure, flow rate, fluid particulate measures, vibrations
of the pump housing, and the like. These sensors may be connected
either directly to a monitoring device or through an intermediary
device using a mix of wired and wireless connection techniques. A
monitoring device may have access to detection values corresponding
to the sensors where the detection values correspond directly to
the sensor output of a processed version of the data output such as
a digitized or sampled version of the sensor output. The monitoring
device may access and process the detection values using methods
discussed elsewhere herein to evaluate the health of the pump
overall, evaluate the health of pump components, predict potential
down line issues arising from atypical pump performance, or changes
in fluid being pumped. The monitoring device may process the
detection values to identify torsion on the drive shaft of the
pump. The identified torsion may then be evaluated relative to
expected torsion based on the specific geometry of the pump and how
it is installed in the equipment relative to other components on
the assembly line. Unexpected torsion may put undue stress on the
driveshaft and may be a sign of deteriorating health of the pump.
Vibration of the inlet and outlet pipes may also be evaluated for
unexpected or resonant vibrations which may be used to drive
process controls to avoid certain pump frequencies. Changes in
vibration may also be due to changes in fluid composition or
density, amplifying or dampening vibrations at certain frequencies.
The monitoring device may process the detection values to identify
unexpected vibrations in the shaft, unexpected temperature values,
or temperature changes in the bearings or in the housing in
proximity to the bearings. In some embodiments, the sensors may
include multiple temperature sensors positioned around the pump to
identify hot spots among the bearings or across the pump housing
which might indicated potential bearing failure. For some pumps,
when the fluid being pumped is corrosive or contains large amounts
of particulates, there may be damage to the interior components of
the pump in contact with the fluid due to cumulative exposure to
the fluid. This may be reflected in unanticipated variations in
output pressure. Additionally or alternatively, if a gear in a gear
pump begins to corrode and no longer forces all the trapped fluid
out this may result in increased pump speed, fluid cavitation,
and/or unexpected vibrations in the output pipe.
[0363] Compressors increase the pressure of a gas by decreasing the
volume occupied by the gas or increasing the amount of the gas in a
confined volume. There may be positive-displacement compressors
that utilize the motion of pistons or rotary screws to move the gas
into a pressurized holding chamber. There are dynamic displacement
gas compressors that use centrifugal force to accelerate the gas
into a stationary compressor where the kinetic energy is converted
to pressure. Compressors may be used to compress various gases for
use on an assembly line. Compressed air may power pneumatic
equipment on an assembly line. In the oil and gas industry, flash
gas compressors may be used to compress gas so that it leaves a
hydrocarbon liquid when it enters a lower pressure environment.
Compressors may be used to restore pressure in gas and oil
pipelines, to mix fluids of interest, and/or to transfer or
transport fluids of interest. Compressors may be used to enable the
underground storage of natural gas.
[0364] Like pumps, compressors may be equipped with a plurality of
sensors for measuring attributes associated with the compressor
such as temperature of bearings or compressor housing, vibration of
a driveshaft, transmission, gear box and the like associated with
the compressor, vessel pressure, flow rate, and the like. These
sensors may be connected either directly to a monitoring device or
through an intermediary device using a mix of wired and wireless
connection techniques. A monitoring device may have access to
detection values corresponding to the sensors where the detection
values correspond directly to the sensor output of a processed
version of the data output such as a digitized or sampled version
of the sensor output. The monitoring device may access and process
the detection values using methods described elsewhere herein to
evaluate the health of the compressor overall, evaluate the health
of compressor components and/or predict potential down line issues
arising from atypical compressor performance. The monitoring device
may process the detection values to identify torsion on a
driveshaft of the compressor. The identified torsion may then be
evaluated relative to expected torsion based on the specific
geometry of the compressor and how it is installed in the equipment
relative to other components and pieces of equipment. Unexpected
torsion may put undue stress on the driveshaft and may be a sign of
deteriorating health of the compressor. Vibration of the inlet and
outlet pipes may also be evaluated for unexpected or resonant
vibrations which may be used to drive process controls to avoid
certain compressor frequencies. The monitoring device may process
the detection values to identify unexpected vibrations in the
shaft, unexpected temperature values or temperature changes in the
bearings or in the housing in proximity to the bearings. In some
embodiments, the sensors may include multiple temperature sensors
positioned around the compressor to identify hot spots among the
bearings or across the compressor housing, which might indicate
potential bearing failure. In some embodiments, sensors may monitor
the pressure in a vessel storing the compressed gas. Changes in the
pressure or rate of pressure change may be indicative of problems
with the compressor.
[0365] Agitators and mixers are used in a variety of industrial
environments. Agitators may be used to mix together different
components such as liquids, solids, or gases. Agitators may be used
to promote a more homogenous mixture of component materials.
Agitators may be used to promote a chemical reaction by increasing
exposure between different component materials and adding energy to
the system. Agitators may be used to promote heat transfer to
facilitate uniform heating or cooling of a material.
[0366] Mixers and agitators are used in such diverse industries as
chemical production, food production, pharmaceutical production,
and the like. There are paint and coating mixers, adhesive and
sealant mixers, oil and gas mixers, water treatment mixers,
wastewater treatment mixers, and the like.
[0367] Agitators may comprise equipment that rotates or agitates an
entire tank or vessel in which the materials to be mixed are
located, such as a concrete mixer. Effective agitations may be
influenced by the number and shape of baffles in the interior of
the tank. Agitation by rotation of the tank or vessel may be
influenced by the axis of rotation relative to the shape of the
tank, direction of rotation, and external forces such as gravity
acting on the material in the tank. Factors affecting the efficacy
of material agitation or mixing by agitation of the tank or vessel
may include axes of rotation, and amplitude and frequency of
vibration along different axes. These factors may be selected based
on the types of materials being selected, their relative
viscosities, specific gravities, particulate count, any shear
thinning or shear thickening anticipated for the component
materials or mixture, flow rates of material entering or exiting
the vessel or tank, direction and location of flows of material
entering of exiting the vessel, and the like.
[0368] Agitators, large tank mixers, portable tank mixers, tote
tank mixers, drum mixers, and mounted mixers (with various mount
types) may comprise a propeller or other mechanical device such as
a blade, vane, or stator inserted into a tank of materials to be
mixed, while rotating a propeller or otherwise moving a mechanical
device. These may include airfoil impellers, fixed pitch blade
impellers, variable pitch blade impellers, anti-ragging impellers,
fixed radial blade impellers, marine-type propellers, collapsible
airfoil impellers, collapsible pitched blade impellers, collapsible
radial blade impellers, and variable pitch impellers. Agitators may
be mounted such that the mechanical agitation is centered in the
tank. Agitators may be mounted such that they are angled in a tank
or are vertically or horizontally offset from the center of the
vessel. The agitators may enter the tank from above, below, or the
side of the tank. There may be a plurality of agitators in a single
tank to achieve uniform mixing throughout the tank or container of
chemicals.
[0369] Agitators may include the strategic flow or introduction of
component materials into the vessel including the location and
direction of entry, rate of entry, pressure of entry, viscosity of
material, specific gravity of the material, and the like.
[0370] Successful agitation of mixing of materials may occur with a
combination of techniques such as one or more propellers in a
baffled tank where components are being introduced at different
locations and at different rates.
[0371] In embodiments, an industrial mixer or agitator may be
equipped with a plurality of sensors for measuring attributes
associated with the industrial mixer such as: temperature of
bearings or tank housing, vibration of driveshafts associated with
a propeller or other mechanical device such as a blade, vane or
stator, vibration of input or output lines, pressure, flow rate,
fluid particulate measures, vibrations of the tank housing and the
like. These sensors may be connected either directly to a
monitoring device or through an intermediary device using a mix of
wired and wireless connection techniques. A monitoring device may
have access to detection values corresponding to the sensors where
the detection values correspond directly to the sensor output of a
processed version of the data, output such as a digitized or
sampled version of the sensor output, fusion of data from multiple
sensors, and the like. The monitoring device may access and process
the detection values using methods discussed elsewhere herein to
evaluate the health of the agitator or mixer overall, evaluate the
health of agitator or mixer components, predict potential down line
issues arising from atypical performance or changes in composition
of material being agitated. For example, the monitoring device may
process the detection values to identify torsion on the driveshaft
of an agitating impeller. The identified torsion may then be
evaluated relative to expected torsion based on the specific
geometry of the agitator and how it is installed in the equipment
relative to other components and/or pieces of equipment. Unexpected
torsion may put undue stress on the driveshaft and may be a sign of
deteriorating health of the agitator. Vibration of inflow and
outflow pipes may be monitored for unexpected or resonant
vibrations which may be used to drive process controls to avoid
certain agitation frequencies. Inflow and outflow pipes may also be
monitored for unexpected flow rates, unexpected particulate
content, and the like. Changes in vibration may also be due to
changes in fluid composition, or density amplifying or dampening
vibrations at certain frequencies. The monitoring device may
distribute sensors to collect detection values which may be used to
identify unexpected vibrations in the shaft, or unexpected
temperature values or temperature changes in the bearings or in the
housing in proximity to the bearings. For some agitators, when the
fluid being agitated is corrosive or contains large amounts of
particulates, there may be damage to the interior components of the
agitator (e.g., baffles, propellers, blades, and the like) which
are in contact with the materials, due to cumulative exposure to
the materials.
[0372] HVAC, air-conditioning systems, and the like may use a
combination of compressors and fans to cool and circulate air in
industrial environments. Similar to the discussion of compressors
and agitators, these systems may include a number of rotating
components whose failure or reduced performance might negatively
impact the working environment and potentially degrade product
quality. A monitoring device may be used to monitor sensors
measuring various aspects of the one or more rotating components,
the venting system, environmental conditions, and the like.
Components of the HVAC/air-conditioning systems may include fan
motors, driveshafts, bearings, compressors, and the like. The
monitoring device may access and process the detection values
corresponding to the sensor outputs according to methods discussed
elsewhere herein to evaluate the overall health of the
air-conditioning unit, HVAC system, and like as well as components
of these systems, identify operational states, predict potential
issues arising from atypical performance, and the like. Evaluation
techniques may include bearing analysis, torsional analysis of
driveshafts, rotors and stators, peak value detection, and the
like. The monitoring device may process the detection values to
identify issues such as torsion on a driveshaft, potential bearing
failures, and the like.
[0373] Assembly line conveyors may comprise a number of moving and
rotating components as part of a system for moving material through
a manufacturing process. These assembly line conveyors may operate
over a wide range of speeds. These conveyances may also vibrate at
a variety of frequencies as they convey material horizontally to
facilitate screening, grading, laning for packaging, spreading,
dewatering, feeding product into the next in-line process, and the
like.
[0374] Conveyance systems may include engines or motors, one or
more driveshafts turning rollers or bearings along which a conveyor
belt may move. A vibrating conveyor may include springs and a
plurality of vibrators which vibrate the conveyor forward in a
sinusoidal manner.
[0375] In embodiments, conveyors and vibrating conveyors may be
equipped with a plurality of sensors for measuring attributes
associated with the conveyor such as temperature of bearings,
vibration of driveshafts, vibrations of rollers along which the
conveyor travels, velocity and speed associated with the conveyor,
and the like. The monitoring device may access and process the
detection values using methods discussed elsewhere herein to
evaluate the overall health of the conveyor as well as components
of the conveyor, predict potential issues arising from atypical
performance, and the like. Techniques for evaluating the conveyors
may include bearing analysis, torsional analysis, phase
detection/phase lock loops to align detection values from different
parts of the conveyor, frequency transformations and frequency
analysis, peak value detection, and the like. The monitoring device
may process the detection values to identify torsion on a
driveshaft, potential bearing failures, uneven conveyance and
like.
[0376] In an illustrative example, a paper-mill conveyance system
may comprise a mesh onto which the paper slurry is coated. The mesh
transports the slurry as liquid evaporates and the paper dries. The
paper may then be wound onto a core until the roll reaches
diameters of up to three meters. The transport speeds of the
paper-mill range from traditional equipment operating at 14-48
meters/minute to new, high-speed equipment operating at close to
2000 meters/minute. For slower machines, the paper may be winding
onto the roll at 14 meters/minute which, towards the end of the
roll having a diameter of approximately three meters would indicate
that the take up roll may be rotating at speeds on the order of a
couple of rotations a minute. Vibrations in the web conveyance or
torsion across the take up roller may result in damage to the
paper, skewing of the paper on the web, or skewed rolls which may
result in equipment downtime or product that is lower in quality or
unusable. Additionally, equipment failure may result in costly
machine shutdowns and loss of product. Therefore, the ability to
predict problems and provide preventative maintenance and the like
may be useful.
[0377] Monitoring truck engines and steering systems to facilitate
timely maintenance and avoid unexpected breakdowns may be
important. Health of the combustion chamber, rotating crankshafts,
bearings, and the like may be monitored using a monitoring device
structured to interpret detection values received from a plurality
of sensors measuring a variety of characteristics associated with
engine components including temperature, torsion, vibration, and
the like. As discussed above, the monitoring device may process the
detection values to identify engine bearing health, torsional
vibrations on a crankshaft/driveshaft, unexpected vibrations in the
combustion chambers, overheating of different components, and the
like. Processing may be done locally or data may be collected
across a number of vehicles and jointly analyzed. The monitoring
device may process detection values associated with the engine,
combustion chambers, and the like. Sensors may monitor temperature,
vibration, torsion, acoustics, and the like to identify issues. A
monitoring device or system may use techniques such as peak
detection, bearing analysis, torsion analysis, phase detection,
PLL, band pass filtering, and the like to identify potential issues
with the steering system and bearing and torsion analysis to
identify potential issues with rotating components on the engine.
This identification of potential issues may be used to schedule
timely maintenance, reduce operation prior to maintenance, and
influence future component design.
[0378] Drilling machines and screwdrivers in the oil and gas
industries may be subjected to significant stresses. Because they
are frequently situated in remote locations, an unexpected
breakdown may result in extended down time due to the lead-time
associated with bringing in replacement components. The health of a
drilling machine or screwdriver and associated rotating
crankshafts, bearings, and the like may be monitored using a
monitoring device structured to interpret detection values received
from a plurality of sensors measuring a variety of characteristics
associated with the drilling machine or screwdriver including
temperature, torsion, vibration, rotational speed, vertical speed,
acceleration, image sensors, and the like. As discussed above, the
monitoring device may process the detection values to identify
equipment health, torsional vibrations on a crankshaft/driveshaft,
unexpected vibrations in the component, overheating of different
components, and the like. Processing may be done locally or data
collected across a number of machines and jointly analyzed. The
monitoring device may jointly process detection values, equipment
maintenance records, product records, historical data, and the like
to identify correlations between detection values, current and
future states of the component, anticipated lifetime of the
component or piece of equipment, and the like. Sensors may monitor
temperature, vibration, torsion, acoustics, and the like to
identify issues such as unanticipated torsion in the drill shaft,
slippage in the gears, overheating, and the like. A monitoring
device or system may use techniques such as peak detection, bearing
analysis, torsion analysis, phase detection, PLL, band pass
filtering, and the like to identify potential issues. This
identification of potential issues may be used to schedule timely
maintenance, order new or replacement components, reduce operation
prior to maintenance, and influence future component design.
[0379] Similarly, it may be desirable to monitor the health of
gearboxes operating in an oil and gas field. A monitoring device
may be structured to interpret detection values received from a
plurality of sensors measuring a variety of characteristics
associated with the gearbox such as temperature, vibration, and the
like. The monitoring device may process the detection values to
identify gear and gearbox health and anticipated life. Processing
may be done locally or data collected across a number of gearboxes
and jointly analyzed. The monitoring device may jointly process
detection values, equipment maintenance records, product records
historical data, and the like to identify correlations between
detection values, current and future states of the gearbox,
anticipated lifetime of the gearbox and associated components, and
the like. A monitoring device or system may use techniques such as
peak detection, bearing analysis, torsion analysis, phase
detection, PLL, band pass filtering, to identify potential issues.
This identification of potential issues may be used to schedule
timely maintenance, order new or replacement components, reduce
operation prior to maintenance, and influence future equipment
design.
[0380] Refining tanks in the oil and gas industries may be
subjected to significant stresses due to the chemical reactions
occurring inside. Because a breach in a tank could result in the
release of potentially toxic chemicals, it may be beneficial to
monitor the condition of the refining tank and associated
components. Monitoring a refining tank to collect a variety of
ongoing data may be used to predict equipment wear, component wear,
unexpected stress, and the like. Given predictions about equipment
health, such as the status of a refining tank, may be used to
schedule timely maintenance, order new or replacement components,
reduce operation prior to maintenance, and influence future
component design. Similar to the discussion above, a refining tank
may be monitored using a monitoring device structured to interpret
detection values received from a plurality of sensors measuring a
variety of characteristics associated with the refining tank such
as temperature, vibration, internal and external pressure, the
presence of liquid or gas at seams and ports, and the like. The
monitoring device may process the detection values to identify
equipment health, unexpected vibrations in the tank, overheating of
the tank or uneven heating across the tank, and the like.
Processing may be done locally or data collected across a number of
tanks and jointly analyzed. The monitoring device may jointly
process detection values, equipment maintenance records, product
records historical data, and the like to identify correlations
between detection values, current and future states of the tank,
anticipated lifetime of the tank and associated components, and the
like. A monitoring device or system may use techniques such as peak
detection, bearing analysis, torsion analysis, phase detection,
PLL, band pass filtering, and the like to identify potential
issues.
[0381] Similarly, it may be desirable to monitor the health of
centrifuges operating in an oil and gas refinery. A monitoring
device may be structured to interpret detection values received
from a plurality of sensors measuring a variety of characteristics
associated with the centrifuge such as temperature, vibration,
pressure, and the like. The monitoring device may process the
detection values to identify equipment health, unexpected
vibrations in the centrifuge, overheating, pressure across the
centrifuge, and the like. Processing may be done locally or data
collected across a number of centrifuges and jointly analyzed. The
monitoring device may jointly process detection values, equipment
maintenance records, product records historical data, and the like
to identify correlations between detection values, current and
future states of the centrifuge, anticipated lifetime of the
centrifuge and associated components, and the like. A monitoring
device or system may use techniques such as peak detection, bearing
analysis, torsion analysis, phase detection, PLL, band pass
filtering, to identify potential issues. This identification of
potential issues may be used to schedule timely maintenance, order
new or replacement components, reduce operation prior to
maintenance and influence future equipment design.
[0382] In embodiments, information about the health or other status
or state information of or regarding a component or piece of
industrial equipment may be obtained by monitoring the condition of
various components throughout a process. Monitoring may include
monitoring the amplitude of a sensor signal measuring attributes
such as temperature, humidity, acceleration, displacement, and the
like. An embodiment of a data monitoring device 8100 is shown in
FIG. 19 and may include a plurality of sensors 8106 communicatively
coupled to a controller 8102. The controller 8102 may include a
data acquisition circuit 8104, a data analysis circuit 8108, a MUX
control circuit 8114, and a response circuit 8110. The data
acquisition circuit 8104 may include a MUX 8112 where the inputs
correspond to a subset of the detection values. The MUX control
circuit 8114 may be structured to provide adaptive scheduling of
the logical control of the MUX and the correspondence of MUX input
and detected values based on a subset of the plurality of detection
values and/or a command from the response circuit 8110 and/or the
output of the data analysis circuit 8104. The data analysis circuit
8108 may comprise one or more of a peak detection circuit, a phase
differential circuit, a PLL circuit, a bandpass filter circuit, a
frequency transformation circuit, a frequency analysis circuit, a
torsional analysis circuit, a bearing analysis circuit, an overload
detection circuit, a sensor fault detection circuit, a vibrational
resonance circuit for the identification of unfavorable interaction
among machines or components, a distortion identification circuit
for the identification of unfavorable distortions such as
deflections shapes upon operation, overloading of weight, excessive
forces, stress and strain-based effects, and the like. The data
analysis circuit 8108 may output a component health status as a
result of the analysis.
[0383] The data analysis circuit 8108 may determine a state,
condition, or status of a component, part, sub-system, or the like
of a machine, device, system or item of equipment (collectively
referred to herein as a component health status) based on a maximum
value of a MUX output for a given input or a rate of change of the
value of a MUX output for a given input. The data analysis circuit
8108 may determine a component health status based on a time
integration of the value of a MUX for a given input. The data
analysis circuit 8108 may determine a component health status based
on phase differential of MUX output relative to an on-board time or
another sensor. The data analysis circuit 8108 may determine a
component health status based on a relationship of value, phase,
phase differential, and rate of change for MUX outputs
corresponding to one or more input detection values. The data
analysis circuit 8108 may determine a component health status based
on process stage or component specification or component
anticipated state.
[0384] The multiplexer control circuit 8114 may adapt the
scheduling of the logical control of the multiplexer based on a
component health status, an anticipated component health status,
the type of component, the type of equipment being measured, an
anticipated state of the equipment, a process stage (different
parameters/sensor values) may be important at different stages in a
process. The multiplexer control circuit 8114 may adapt the
scheduling of the logical control of the multiplexer based on a
sequence selected by a user or a remote monitoring application, or
on the basis of a user request for a specific value. The
multiplexer control circuit 8114 may adapt the scheduling of the
logical control of the multiplexer based on the basis of a storage
profile or plan (such as based on type and availability of storage
elements and parameters as described elsewhere in this disclosure
and in the documents incorporated herein by reference), network
conditions or availability (also as described elsewhere in this
disclosure and in the documents incorporated herein by reference),
or value or cost of component or equipment.
[0385] The plurality of sensors 8106 may be wired to ports on the
data acquisition circuit 8104. The plurality of sensors 8106 may be
wirelessly connected to the data acquisition circuit 8104. The data
acquisition circuit 8104 may be able to access detection values
corresponding to the output of at least one of the plurality of
sensors 8106 where the sensors 8106 may be capturing data on
different operational aspects of a piece of equipment or an
operating component.
[0386] The selection of the plurality of sensors 8106 for a data
monitoring device 8100 designed for a specific component or piece
of equipment may depend on a variety of considerations such as
accessibility for installing new sensors, incorporation of sensors
in the initial design, anticipated operational and failure
conditions, resolution desired at various positions in a process or
plant, reliability of the sensors, and the like. The impact of a
failure, time response of a failure (e.g., warning time and/or
off-nominal modes occurring before failure), likelihood of failure,
and/or sensitivity required, and/or difficulty to detect failure
conditions may drive the extent to which a component or piece of
equipment is monitored with more sensors, and/or higher capability
sensors being dedicated to systems where unexpected or undetected
failure would be costly or have severe consequences.
[0387] Depending on the type of equipment, the component being
measured, the environment in which the equipment is operating, and
the like, sensors 8106 may comprise one or more of, without
limitation, a vibration sensor, a thermometer, a hygrometer, a
voltage sensor and/or a current sensor (for the component and/or
other sensors measuring the component), an accelerometer, a
velocity detector, a light or electromagnetic sensor (e.g.,
determining temperature, composition, and/or spectral analysis,
and/or object position or movement), an image sensor, a structured
light sensor, a laser-based image sensor, a thermal imager, an
acoustic wave sensor, a displacement sensor, a turbidity meter, a
viscosity meter, an axial load sensor, a radial load sensor, a
tri-axial sensor, an accelerometer, a speedometer, a tachometer, a
fluid pressure meter, an air flow meter, a horsepower meter, a flow
rate meter, a fluid particle detector, an optical (laser) particle
counter, an ultrasonic sensor, an acoustical sensor, a heat flux
sensor, a galvanic sensor, a magnetometer, a pH sensor, and the
like, including, without limitation, any of the sensors described
throughout this disclosure and the documents incorporated by
reference.
[0388] The sensors 8106 may provide a stream of data over time that
has a phase component, such as relating to acceleration or
vibration, allowing for the evaluation of phase or frequency
analysis of different operational aspects of a piece of equipment
or an operating component. The sensors 8106 may provide a stream of
data that is not conventionally phase-based, such as temperature,
humidity, load, and the like. The sensors 8106 may provide a
continuous or near continuous stream of data over time, periodic
readings, event-driven readings, and/or readings according to a
selected interval or schedule.
[0389] The sensors 8106 may monitor components such as bearings,
sets of bearings, motors, driveshafts, pistons, pumps, conveyors,
vibrating conveyors, compressors, drills, and the like in vehicles,
oil and gas equipment in the field, in assembly line components,
and the like.
[0390] In embodiments, as illustrated in FIG. 19, the sensors 8106
may be part of the data monitoring device 8100, referred to herein
in some cases as a data collector, which in some cases may comprise
a mobile or portable data collector. In embodiments, as illustrated
in FIGS. 20 and 21, one or more external sensors 8126, which are
not explicitly part of a monitoring device 8120 but rather are new,
previously attached to or integrated into the equipment or
component, may be opportunistically connected to, or accessed by
the monitoring device 8120. The monitoring device 8120 may include
a controller 8122. The controller 8122 may include a data
acquisition circuit 8104, a data analysis circuit 8108, a MUX
control circuit 8114, and a response circuit 8110. The data
acquisition circuit 8104 may comprise a MUX 8112 where the inputs
correspond to a subset of the detection values. The MUX control
circuit 8114 may be structured to provide the logical control of
the MUX and the correspondence of MUX input and detected values
based on a subset of the plurality of detection values and/or a
command from the response circuit 8110 and/or the output of the
data analysis circuit 8108. The data analysis circuit 8108 may
comprise one or more of a peak detection circuit, a phase
differential circuit, a PLL circuit, a bandpass filter circuit, a
frequency transformation circuit, a frequency analysis circuit, a
torsional analysis circuit, a bearing analysis circuit, an overload
detection circuit, vibrational resonance circuit for the
identification of unfavorable interaction among machines or
components, a distortion identification circuit for the
identification of unfavorable distortions such as deflections
shapes upon operation ,stress and strain-based effects, and the
like.
[0391] The one or more external sensors 8126 may be directly
connected to the one or more input ports 8128 on the data
acquisition circuit 8104 of the controller 8122 or may be accessed
by the data acquisition circuit 8104 wirelessly, such as by a
reader, interrogator, or other wireless connection, such as over a
short-distance wireless protocol. In embodiments, as shown in FIG.
23, a data acquisition circuit 8104 may further comprise a wireless
communication circuit 8130. The data acquisition circuit 8104 may
use the wireless communication circuit 8130 to access detection
values corresponding to the one or more external sensors 8126
wirelessly or via a separate source or some combination of these
methods.
[0392] In embodiments, as illustrated in FIG. 22, the controller
8134 may further comprise a data storage circuit 8136. The data
storage circuit 8136 may be structured to store one or more of
sensor specifications, component specifications, anticipated state
information, detected values, multiplexer output, component models,
and the like. The data storage circuit 8136 may provide
specifications and anticipated state information to the data
analysis circuit 8108.
[0393] In embodiments, the response circuit 8110 may initiate a
variety of actions based on the sensor status provided by the data
analysis circuit 8108. The response circuit 8110 may adjust a
sensor scaling value (e.g., from 100 mV/gram to 10 mV/gram). The
response circuit 8110 may select an alternate sensor from a
plurality available. The response circuit 8110 may acquire data
from a plurality of sensors of different ranges. The response
circuit 8110 may recommend an alternate sensor. The response
circuit 8110 may issue an alarm or an alert.
[0394] In embodiments, the response circuit 8110 may cause the data
acquisition circuit 8104 to enable or disable the processing of
detection values corresponding to certain sensors based on the
component status. This may include switching to sensors having
different response rates, sensitivity, ranges, and the like;
accessing new sensors or types of sensors, accessing data from
multiple sensors, and the like. Switching may be undertaken based
on a model, a set of rules, or the like. In embodiments, switching
may be under control of a machine learning system, such that
switching is controlled based on one or more metrics of success,
combined with input data, over a set of trials, which may occur
under supervision of a human supervisor or under control of an
automated system. Switching may involve switching from one input
port to another (such as to switch from one sensor to another).
Switching may involve altering the multiplexing of data, such as
combining different streams under different circumstances.
Switching may involve activating a system to obtain additional
data, such as moving a mobile system (such as a robotic or drone
system), to a location where different or additional data is
available, such as positioning an image sensor for a different view
or positioning a sonar sensor for a different direction of
collection, or to a location where different sensors can be
accessed, such as moving a collector to connect up to a sensor at a
location in an environment by a wired or wireless connection. This
switching may be implemented by directing changes to the
multiplexer (MUX) control circuit 8114.
[0395] In embodiments, the response circuit 8110 may make
recommendations for the replacement of certain sensors in the
future with sensors having different response rates, sensitivity,
ranges, and the like. The response circuit 8110 may recommend
design alterations for future embodiments of the component, the
piece of equipment, the operating conditions, the process, and the
like.
[0396] In embodiments, the response circuit 8110 may recommend
maintenance at an upcoming process stop or initiate a maintenance
call where the maintenance may include the replacement of the
sensor with the same or an alternate type of sensor having a
different response rate, sensitivity, range, and the like. In
embodiments, the response circuit 8110 may implement or recommend
process changes--for example to lower the utilization of a
component that is near a maintenance interval, operating
off-nominally, or failed for purpose but is still at least
partially operational, to change the operating speed of a component
(such as to put it in a lower-demand mode), to initiate
amelioration of an issue (such as to signal for additional
lubrication of a roller bearing set, or to signal for an alignment
process for a system that is out of balance), and the like.
[0397] In embodiments, the data analysis circuit 8108 and/or the
response circuit 8110 may periodically store certain detection
values and/or the output of the multiplexers and/or the data
corresponding to the logic control of the MUX in the data storage
circuit 8136 to enable the tracking of component performance over
time. In embodiments, based on sensor status, as described
elsewhere herein, recently measured sensor data and related
operating conditions such as RPMs, component loads, temperatures,
pressures, vibrations, or other sensor data of the types described
throughout this disclosure in the data storage circuit 8136 enable
the backing out of overloaded/failed sensor data. The signal
evaluation circuit 8108 may store data at a higher data rate for
greater granularity in future processing, the ability to reprocess
at different sampling rates, and/or to enable diagnosing or
post-processing of system information where operational data of
interest is flagged, and the like.
[0398] In embodiments, as shown in FIGS. 23, 24, 25, and 26, a data
monitoring system 8138 may include at least one data monitoring
device 8140. The at least one data monitoring device 8140 may
include sensors 8106 and a controller 8142 comprising a data
acquisition circuit 8104, a data analysis circuit 8108, a data
storage circuit 8136, and a communication circuit 8146 to allow
data and analysis to be transmitted to a monitoring application
8150 on a remote server 8148. The signal evaluation circuit 8108
may include at least an overload detection circuit (e.g., reference
FIGS. 69 and 70) and/or a sensor fault detection circuit (e.g.,
reference FIGS. 69 and 70). The signal evaluation circuit 8108 may
periodically share data with the communication circuit 8146 for
transmittal to the remote server 8148 to enable the tracking of
component and equipment performance over time and under varying
conditions by a monitoring application 8150. Based on the sensor
status, the signal evaluation circuit 8108 and/or response circuit
8110 may share data with the communication circuit 8146 for
transmittal to the remote server 8148 based on the fit of data
relative to one or more criteria. Data may include recent sensor
data and additional data such as RPMs, component loads,
temperatures, pressures, vibrations, and the like for transmittal.
The signal evaluation circuit 8108 may share data at a higher data
rate for transmittal to enable greater granularity in processing on
the remote server.
[0399] In embodiments, as shown in FIG. 23, the communication
circuit 8146 may communicate data directly to a remote server 8148.
In embodiments, as shown in FIG. 24, the communication circuit 8146
may communicate data to an intermediate computer 8152 which may
include a processor 8154 running an operating system 8156 and a
data storage circuit 8158.
[0400] In embodiments as illustrated in FIGS. 25 and 26, a data
collection system 8160 may have a plurality of monitoring devices
8144 collecting data on multiple components in a single piece of
equipment, collecting data on the same component across a plurality
of pieces of equipment, (both the same and different types of
equipment) in the same facility, as well as collecting data from
monitoring devices in multiple facilities. A monitoring application
8150 on a remote server 8148 may receive and store one or more of
detection values, timing signals, and data coming from a plurality
of the various monitoring devices 8144.
[0401] In embodiments, as shown in FIG. 25, the communication
circuit 8146 may communicate data directly to a remote server 8148.
In embodiments, as shown in FIG. 26, the communication circuit 8146
may communicate data to an intermediate computer 8152 which may
include a processor 8154 running an operating system 8156 and a
data storage circuit 8158. There may be an individual intermediate
computer 8152 associated with each monitoring device 8140 or an
individual intermediate computer 8152 may be associated with a
plurality of monitoring devices 8144 where the intermediate
computer 8152 may collect data from a plurality of data monitoring
devices and send the cumulative data to the remote server 8148.
Communication to the remote server 8148 may be streaming, batch
(e.g., when a connection is available), or opportunistic.
[0402] The monitoring application 8150 may select subsets of the
detection values to be jointly analyzed. Subsets for analysis may
be selected based on a single type of sensor, component, or a
single type of equipment in which a component is operating. Subsets
for analysis may be selected or grouped based on common operating
conditions such as size of load, operational condition (e.g.,
intermittent or continuous), operating speed or tachometer output,
common ambient environmental conditions such as humidity,
temperature, air or fluid particulate, and the like. Subsets for
analysis may be selected based on the effects of other nearby
equipment such as nearby machines rotating at similar frequencies,
nearby equipment producing electromagnetic fields, nearby equipment
producing heat, nearby equipment inducing movement or vibration,
nearby equipment emitting vapors, chemicals or particulates, or
other potentially interfering or intervening effects.
[0403] In embodiments, the monitoring application 8150 may analyze
the selected subset. In an example, data from a single sensor may
be analyzed over different time periods such as one operating
cycle, several operating cycles, a month, a year, the life of the
component, or the like. Data from multiple sensors of a common type
measuring a common component type may also be analyzed over
different time periods. Trends in the data such as changing rates
of change associated with start-up or different points in the
process may be identified. Correlation of trends and values for
different sensors may be analyzed to identify those parameters
whose short-term analysis might provide the best prediction
regarding expected sensor performance. This information may be
transmitted back to the monitoring device to update sensor models,
sensor selection, sensor range, sensor scaling, sensor sampling
frequency, types of data collected, and the like, and be analyzed
locally or to influence the design of future monitoring
devices.
[0404] In embodiments, the monitoring application 8150 may have
access to equipment specifications, equipment geometry, component
specifications, component materials, anticipated state information
for a plurality of sensors, operational history, historical
detection values, sensor life models, and the like for use
analyzing the selected subset using rule-based or model-based
analysis. The monitoring application 8150 may provide
recommendations regarding sensor selection, additional data to
collect, data to store with sensor data, and the like. The
monitoring application 8150 may provide recommendations regarding
scheduling repairs and/or maintenance. The monitoring application
8150 may provide recommendations regarding replacing a sensor. The
replacement sensor may match the sensor being replaced or the
replacement sensor may have a different range, sensitivity,
sampling frequency, and the like.
[0405] In embodiments, the monitoring application 8150 may include
a remote learning circuit structured to analyze sensor status data
(e.g., sensor overload or sensor failure) together with data from
other sensors, failure data on components being monitored,
equipment being monitored, output being produced, and the like. The
remote learning system may identify correlations between sensor
overload and data from other sensors.
[0406] An example monitoring system for data collection in an
industrial environment includes a data acquisition circuit that
interprets a number of detection values, each of the detection
values corresponding to input received from at least one of a
number of input sensors, a MUX having inputs corresponding to a
subset of the detection values, a MUX control circuit that
interprets a subset of the number of detection values and provides
the logical control of the MUX and the correspondence of MUX input
and detected values as a result, where the logic control of the MUX
includes adaptive scheduling of the select lines, a data analysis
circuit that receives an output from the MUX and data corresponding
to the logic control of the MUX resulting in a component health
status, an analysis response circuit that performs an operation in
response to the component health status, where the number of
sensors includes at least two sensors such as a temperature sensor,
a load sensor, a vibration sensor, an acoustic wave sensor, a heat
flux sensor, an infrared sensor, an accelerometer, a tri-axial
vibration sensor, and/or a tachometer. In certain further
embodiments, an example system includes: where at least one of the
number of detection values may correspond to a fusion of two or
more input sensors representing a virtual sensor; where the system
further includes a data storage circuit that stores at least one of
component specifications and anticipated component state
information and buffers a subset of the number of detection values
for a predetermined length of time; where the system further
includes a data storage circuit that stores at least one of a
component specification and anticipated component state information
and buffers the output of the MUX and data corresponding to the
logic control of the MUX for a predetermined length of time; where
the data analysis circuit includes a peak detection circuit, a
phase detection circuit, a bandpass filter circuit, a frequency
transformation circuit, a frequency analysis circuit, a PLL
circuit, a torsional analysis circuit, and/or a bearing analysis
circuit; where operation further includes storing additional data
in the data storage circuit; where the operation includes at least
one of enabling or disabling one or more portions of the MUX
circuit; and/or where the operation includes causing the MUX
control circuit to alter the logical control of the MUX and the
correspondence of MUX input and detected values. In certain
embodiments, the system includes at least two multiplexers; control
of the correspondence of the multiplexer input and the detected
values further includes controlling the connection of the output of
a first multiplexer to an input of a second multiplexer; control of
the correspondence of the multiplexer input and the detected values
further comprises powering down at least a portion of one of the at
least two multiplexers; and/or control of the correspondence of MUX
input and detected values includes adaptive scheduling of the
select lines. In certain embodiments, a data response circuit
analyzes the stream of data from one or both MUXes, and recommends
an action in response to the analysis.
[0407] An example testing system includes the testing system in
communication with a number of analog and digital input sensors, a
monitoring device including a data acquisition circuit that
interprets a number of detection values, each of the number of
detection values corresponding to at least one of the input
sensors, a MUX having inputs corresponding to a subset of the
detection values, a MUX control circuit that interprets a subset of
the number of detection values and provides the logical control of
the MUX and control of the correspondence of MUX input and detected
values as a result, where the logic control of the MUX includes
adaptive scheduling of the select lines, and a user interface
enabled to accept scheduling input for select lines and display
output of MUX and select line data.
[0408] In embodiments, information about the health or other status
or state information of or regarding a component or piece of
industrial equipment may be obtained by looking at both the
amplitude and phase or timing of data signals relative to related
data signals, timers, reference signals or data measurements. An
embodiment of a data monitoring device 8500 is shown in FIG. 27 and
may include a plurality of sensors 8506 communicatively coupled to
a controller 8502. The controller 8502 may include a data
acquisition circuit 8504, a signal evaluation circuit 8508 and a
response circuit 8510. The plurality of sensors 8506 may be wired
to ports on the data acquisition circuit 8504 or wirelessly in
communication with the data acquisition circuit 8504. The plurality
of sensors 8506 may be wirelessly connected to the data acquisition
circuit 8504. The data acquisition circuit 8504 may be able to
access detection values corresponding to the output of at least one
of the plurality of sensors 8506 where the sensors 8506 may be
capturing data on different operational aspects of a piece of
equipment or an operating component.
[0409] The selection of the plurality of sensors 8506 for a data
monitoring device 8500 designed for a specific component or piece
of equipment may depend on a variety of considerations such as
accessibility for installing new sensors, incorporation of sensors
in the initial design, anticipated operational and failure
conditions, reliability of the sensors, and the like. The impact of
failure may drive the extent to which a component or piece of
equipment is monitored with more sensors and/or higher capability
sensors being dedicated to systems where unexpected or undetected
failure would be costly or have severe consequences.
[0410] Depending on the type of equipment, the component being
measured, the environment in which the equipment is operating and
the like, sensors 8506 may comprise one or more of, without
limitation, a vibration sensor, a thermometer, a hygrometer, a
voltage sensor, a current sensor, an accelerometer, a velocity
detector, a light or electromagnetic sensor (e.g., determining
temperature, composition and/or spectral analysis, and/or object
position or movement), an image sensor, a structured light sensor,
a laser-based image sensor, an acoustic wave sensor, a displacement
sensor, a turbidity meter, a viscosity meter, a load sensor, a
tri-axial sensor, an accelerometer, a tachometer, a fluid pressure
meter, an air flow meter, a horsepower meter, a flow rate meter, a
fluid particle detector, an acoustical sensor, a pH sensor, and the
like, including, without limitation, any of the sensors described
throughout this disclosure and the documents incorporated by
reference.
[0411] The sensors 8506 may provide a stream of data over time that
has a phase component, such as relating to acceleration or
vibration, allowing for the evaluation of phase or frequency
analysis of different operational aspects of a piece of equipment
or an operating component. The sensors 8506 may provide a stream of
data that is not conventionally phase-based, such as temperature,
humidity, load, and the like. The sensors 8506 may provide a
continuous or near continuous stream of data over time, periodic
readings, event-driven readings, and/or readings according to a
selected interval or schedule.
[0412] In embodiments, as illustrated in FIG. 27, the sensors 8506
may be part of the data monitoring device 8500, referred to herein
in some cases as a data collector, which in some cases may comprise
a mobile or portable data collector. In embodiments, as illustrated
in FIGS. 28 and 29, sensors 8518, either new or previously attached
to or integrated into the equipment or component, may be
opportunistically connected to or accessed by a monitoring device
8512. The sensors 8518 may be directly connected to input ports
8520 on the data acquisition circuit 8516 of a controller 8514 or
may be accessed by the data acquisition circuit 8516 wirelessly,
such as by a reader, interrogator, or other wireless connection,
such as over a short-distance wireless protocol. In embodiments, a
data acquisition circuit 8516 may access detection values
corresponding to the sensors 8518 wirelessly or via a separate
source or some combination of these methods. In embodiments, the
data acquisition circuit 8504 may include a wireless communications
circuit 8522 able to wirelessly receive data opportunistically from
sensors 8518 in the vicinity and route the data to the input ports
8520 on the data acquisition circuit 8516.
[0413] In an embodiment, as illustrated in FIGS. 30 and 31, the
signal evaluation circuit 8508 may then process the detection
values to obtain information about the component or piece of
equipment being monitored. Information extracted by the signal
evaluation circuit 8508 may comprise rotational speed, vibrational
data including amplitudes, frequencies, phase, and/or acoustical
data, and/or non-phase sensor data such as temperature, humidity,
image data, and the like.
[0414] The signal evaluation circuit 8508 may include one or more
components such as a phase detection circuit 8528 to determine a
phase difference between two time-based signals, a phase lock loop
circuit 8530 to adjust the relative phase of a signal such that it
is aligned with a second signal, timer or reference signal, and/or
a band pass filter circuit 8532 which may be used to separate out
signals occurring at different frequencies. An example band pass
filter circuit 8532 includes any filtering operations understood in
the art, including at least a low-pass filter, a high-pass filter,
and/or a band pass filter--for example to exclude or reduce
frequencies that are not of interest for a particular
determination, and/or to enhance the signal for frequencies of
interest. Additionally, or alternatively, a band pass filter
circuit 8532 includes one or more notch filters or other filtering
mechanism to narrow ranges of frequencies (e.g., frequencies from a
known source of noise). This may be used to filter out dominant
frequency signals such as the overall rotation, and may help enable
the evaluation of low amplitude signals at frequencies associated
with torsion, bearing failure and the like.
[0415] In embodiments, understanding the relative differences may
be enabled by a phase detection circuit 8528 to determine a phase
difference between two signals. It may be of value to understand a
relative phase offset, if any, between signals such as when a
periodic vibration occurs relative to a relative rotation of a
piece of equipment. In embodiments, there may be value in
understanding where in a cycle shaft vibrations occur relative to a
motor control input to better balance the control of the motor.
This may be particularly true for systems and components that are
operating at relative slow RPMs. Understanding of the phase
difference between two signals or between those signals and a timer
may enable establishing a relationship between a signal value and
where it occurs in a process or rotation. Understanding relative
phase differences may help in evaluating the relationship between
different components of a system such as in the creation of a
vibrational model for an Operational Deflection Shape (ODS).
[0416] The signal evaluation circuit 8544 may perform frequency
analysis using techniques such as a digital Fast Fourier transform
(FFT), Laplace transform, Z-transform, wavelet transform, other
frequency domain transform, or other digital or analog signal
analysis techniques, including, without limitation, complex
analysis, including complex phase evolution analysis. An overall
rotational speed or tachometer may be derived from data from
sensors such as rotational velocity meters, accelerometers,
displacement meters and the like. Additional frequencies of
interest may also be identified. These may include frequencies near
the overall rotational speed as well as frequencies higher than
that of the rotational speed. These may include frequencies that
are nonsynchronous with an overall rotational speed. Signals
observed at frequencies that are multiples of the rotational speed
may be due to bearing induced vibrations or other behaviors or
situations involving bearings. In some instances, these frequencies
may be in the range of one times the rotational speed, two times
the rotational speed, three times the rotational speed, and the
like, up to 3.15 to 15 times the rotational speed, or higher. In
some embodiments, the signal evaluation circuit 8544 may select RC
components for a band pass filter circuit 8532 based on overall
rotational speed to create a band pass filter circuit 8532 to
remove signals at expected frequencies such as the overall
rotational speed, to facilitate identification of small amplitude
signals at other frequencies. In embodiments, variable components
may be selected, such that adjustments may be made in keeping with
changes in the rotational speed, so that the band pass filter may
be a variable band pass filter. This may occur under control of
automatically self-adjusting circuit elements, or under control of
a processor, including automated control based on a model of the
circuit behavior, where a rotational speed indicator or other data
is provided as a basis for control.
[0417] In embodiments, rather than performing frequency analysis,
the signal evaluation circuit 8544 may utilize the time-based
detection values to perform transitory signal analysis. These may
include identifying abrupt changes in signal amplitude including
changes where the change in amplitude exceeds a predetermined value
or exists for a certain duration. In embodiments, the time-based
sensor data may be aligned with a timer or reference signal
allowing the time-based sensor data to be aligned with, for
example, a time or location in a cycle. Additional processing to
look at frequency changes over time may include the use of
Short-Time Fourier Transforms (STFT) or a wavelet transform.
[0418] In embodiments, frequency-based techniques and time-based
techniques may be combined, such as using time-based techniques to
determine discrete time periods during which given operational
modes or states are occurring and using frequency-based techniques
to determine behavior within one or more of the discrete time
periods.
[0419] In embodiments, the signal evaluation circuit may utilize
demodulation techniques for signals obtained from equipment running
at slow speeds such as paper and pulp machines, mining equipment,
and the like. A signal evaluation circuit employing a demodulation
technique may comprise a band-pass filter circuit, a rectifier
circuit, and/or a low pass circuit prior to transforming the data
to the frequency domain
[0420] The response circuit 8510 8710 may further comprise
evaluating the results of the signal evaluation circuit 8508 8544
and, based on certain criteria, initiating an action. Criteria may
include a predetermined maximum or minimum value for a detection
value from a specific sensor, a value of a sensor's corresponding
detection value over time, a change in value, a rate of change in
value, and/or an accumulated value (e.g., a time spent above/below
a threshold value, a weighted time spent above/below one or more
threshold values, and/or an area of the detected value above/below
one or more threshold values). The criteria may include a sensor's
detection values at certain frequencies or phases where the
frequencies or phases may be based on the equipment geometry,
equipment control schemes, system input, historical data, current
operating conditions, and/or an anticipated response. The criteria
may comprise combinations of data from different sensors such as
relative values, relative changes in value, relative rates of
change in value, relative values over time, and the like. The
relative criteria may change with other data or information such as
process stage, type of product being processed, type of equipment,
ambient temperature and humidity, external vibrations from other
equipment, and the like. The relative criteria may include level of
synchronicity with an overall rotational speed, such as to
differentiate between vibration induced by bearings and vibrations
resulting from the equipment design. In embodiments, the criteria
may be reflected in one or more calculated statistics or metrics
(including ones generated by further calculations on multiple
criteria or statistics), which in turn may be used for processing
(such as on board a data collector or by an external system), such
as to be provided as an input to one or more of the machine
learning capabilities described in this disclosure, to a control
system (which may be an on-board data collector or remote, such as
to control selection of data inputs, multiplexing of sensor data,
storage, or the like), or as a data element that is an input to
another system, such as a data stream or data package that may be
available to a data marketplace, a SCADA system, a remote control
system, a maintenance system, an analytic system, or other
system.
[0421] In an illustrative and non-limiting example, an alert may be
issued if the vibrational amplitude and/or frequency exceeds a
predetermined maximum value, if there is a change or rate of change
that exceeds a predetermined acceptable range, and/or if an
accumulated value based on vibrational amplitude and/or frequency
exceeds a threshold. Certain embodiments are described herein as
detected values exceeding thresholds or predetermined values, but
detected values may also fall below thresholds or predetermined
values--for example where an amount of change in the detected value
is expected to occur, but detected values indicate that the change
may not have occurred. For example, and without limitation,
vibrational data may indicate system agitation levels, properly
operating equipment, or the like, and vibrational data below
amplitude and/or frequency thresholds may be an indication of a
process that is not operating according to expectations. Except
where the context clearly indicates otherwise, any description
herein describing a determination of a value above a threshold
and/or exceeding a predetermined or expected value is understood to
include determination of a value below a threshold and/or falling
below a predetermined or expected value.
[0422] The predetermined acceptable range may be based on
anticipated system response or vibration based on the equipment
geometry and control scheme such as number of bearings, relative
rotational speed, influx of power to the system at a certain
frequency, and the like. The predetermined acceptable range may
also be based on long term analysis of detection values across a
plurality of similar equipment and components and correlation of
data with equipment failure. Based on vibration phase information,
a physical location of a problem may be identified. Based on the
vibration phase information system design flaws, off-nominal
operation, and/or component or process failures may be identified.
In some embodiments, an alert may be issued based on changes or
rates of change in the data over time such as increasing amplitude
or shifts in the frequencies or phases at which a vibration occurs.
In some embodiments, an alert may be issued based on accumulated
values such as time spent over a threshold, weighted time spent
over one or more thresholds, and/or an area of a curve of the
detected value over one or more thresholds. In embodiments, an
alert may be issued based on a combination of data from different
sensors such as relative changes in value, or relative rates of
change in amplitude, frequency of phase in addition to values of
non-phase sensors such as temperature, humidity and the like. For
example, an increase in temperature and energy at certain
frequencies may indicate a hot bearing that is starting to fail. In
embodiments, the relative criteria for an alarm may change with
other data or information such as process stage, type of product
being processed on equipment, ambient temperature and humidity,
external vibrations from other equipment and the like.
[0423] In embodiments, response circuit 8510 may cause the data
acquisition circuit 8504 to enable or disable the processing of
detection values corresponding to certain sensors based on the some
of the criteria discussed above. This may include switching to
sensors having different response rates, sensitivity, ranges, and
the like; accessing new sensors or types of sensors, and the like.
Switching may be undertaken based on a model, a set of rules, or
the like. In embodiments, switching may be under control of a
machine learning system, such that switching is controlled based on
one or more metrics of success, combined with input data, over a
set of trials, which may occur under supervision of a human
supervisor or under control of an automated system. Switching may
involve switching from one input port to another (such as to switch
from one sensor to another). Switching may involve altering the
multiplexing of data, such as combining different streams under
different circumstances. Switching may involve activating a system
to obtain additional data, such as moving a mobile system (such as
a robotic or drone system), to a location where different or
additional data is available (such as positioning an image sensor
for a different view or positioning a sonar sensor for a different
direction of collection) or to a location where different sensors
can be accessed (such as moving a collector to connect up to a
sensor that is disposed at a location in an environment by a wired
or wireless connection). The response circuit 8510 may make
recommendations for the replacement of certain sensors in the
future with sensors having different response rates, sensitivity,
ranges, and the like. The response circuit 8510 may recommend
design alterations for future embodiments of the component, the
piece of equipment, the operating conditions, the process, and the
like.
[0424] In embodiments, the response circuit 8510 may recommend
maintenance at an upcoming process stop or initiate a maintenance
call. The response circuit 8510 may recommend changes in process or
operating parameters to remotely balance the piece of equipment. In
embodiments, the response circuit 8510 may implement or recommend
process changes--for example to lower the utilization of a
component that is near a maintenance interval, operating
off-nominally, or failed for purpose but still at least partially
operational, to change the operating speed of a component (such as
to put it in a lower-demand mode), to initiate amelioration of an
issue (such as to signal for additional lubrication of a roller
bearing set, or to signal for an alignment process for a system
that is out of balance), and the like.
[0425] In embodiments, as shown in FIG. 32, the data monitoring
device 8540 may further comprise a data storage circuit 8542,
memory, and the like. The signal evaluation circuit 8544 may
periodically store certain detection values to enable the tracking
of component performance over time.
[0426] In embodiments, based on relevant operating conditions
and/or failure modes which may occur in as sensor values approach
one or more criteria, the signal evaluation circuit 8544 may store
data in the data storage circuit 8542 based on the fit of data
relative to one or more criteria, such as those described
throughout this disclosure. Based on one sensor input meeting or
approaching specified criteria or range, the signal evaluation
circuit 8544 may store additional data such as RPMs, component
loads, temperatures, pressures, vibrations or other sensor data of
the types described throughout this disclosure. The signal
evaluation circuit 8544 may store data at a higher data rate for
greater granularity in future processing, the ability to reprocess
at different sampling rates, and/or to enable diagnosing or
post-processing of system information where operational data of
interest is flagged, and the like.
[0427] In embodiments, as shown in FIG. 33, a data monitoring
system 8546 may comprise at least one data monitoring device 8548.
The at least one data monitoring device 8548 comprising sensors
8506, a controller 8550 comprising a data acquisition circuit 8504,
a signal evaluation circuit 8538, a data storage circuit 8542, and
a communications circuit 8552 to allow data and analysis to be
transmitted to a monitoring application 8556 on a remote server
8554. The signal evaluation circuit 8538 may comprise at least one
of a phase detection circuit 8528, a phase lock loop circuit 8530,
and/or a band pass circuit 8532. The signal evaluation circuit 8538
may periodically share data with the communication circuit 8552 for
transmittal to the remote server 8554 to enable the tracking of
component and equipment performance over time and under varying
conditions by a monitoring application 8556. Because relevant
operating conditions and/or failure modes may occur as sensor
values approach one or more criteria, the signal evaluation circuit
8538 may share data with the communication circuit 8552 for
transmittal to the remote server 8554 based on the fit of data
relative to one or more criteria. Based on one sensor input meeting
or approaching specified criteria or range, the signal evaluation
circuit 8538 may share additional data such as RPMs, component
loads, temperatures, pressures, vibrations, and the like for
transmittal. The signal evaluation circuit 8538 may share data at a
higher data rate for transmittal to enable greater granularity in
processing on the remote server.
[0428] In embodiments, as illustrated in FIG. 34, a data collection
system 8560 may have a plurality of monitoring devices 8558
collecting data on multiple components in a single piece of
equipment, collecting data on the same component across a plurality
of pieces of equipment (both the same and different types of
equipment) in the same facility, as well as collecting data from
monitoring devices in multiple facilities. A monitoring application
on a remote server may receive and store the data coming from a
plurality of the various monitoring devices. The monitoring
application may then select subsets of data which may be jointly
analyzed. Subsets of monitoring data may be selected based on data
from a single type of component or data from a single type of
equipment in which the component is operating. Monitoring data may
be selected or grouped based on common operating conditions such as
size of load, operational condition (e.g., intermittent,
continuous), operating speed or tachometer, common ambient
environmental conditions such as humidity, temperature, air or
fluid particulate, and the like. Monitoring data may be selected
based on the effects of other nearby equipment, such as nearby
machines rotating at similar frequencies, nearby equipment
producing electromagnetic fields, nearby equipment producing heat,
nearby equipment inducing movement or vibration, nearby equipment
emitting vapors, chemicals or particulates, or other potentially
interfering or intervening effects.
[0429] The monitoring application may then analyze the selected
data set. For example, data from a single component may be analyzed
over different time periods such as one operating cycle, several
operating cycles, a month, a year, or the like. Data from multiple
components of the same type may also be analyzed over different
time periods. Trends in the data such as changes in frequency or
amplitude may be correlated with failure and maintenance records
associated with the same component or piece of equipment. Trends in
the data such as changing rates of change associated with start-up
or different points in the process may be identified. Additional
data may be introduced into the analysis such as output product
quality, output quantity (such as per unit of time), indicated
success or failure of a process, and the like. Correlation of
trends and values for different types of data may be analyzed to
identify those parameters whose short-term analysis might provide
the best prediction regarding expected performance. This
information may be transmitted back to the monitoring device to
update types of data collected and analyzed locally or to influence
the design of future monitoring devices.
[0430] In an illustrative and non-limiting example, the monitoring
device may be used to collect and process sensor data to measure
mechanical torque. The monitoring device may be in communication
with or include a high resolution, high speed vibration sensor to
collect data over an extended period of time, enough to measure
multiple cycles of rotation. For gear driven equipment, the
sampling resolution should be such that the number of samples taken
per cycle is at least equal to the number of gear teeth driving the
component. It will be understood that a lower sampling resolution
may also be utilized, which may result in a lower confidence
determination and/or taking data over a longer period of time to
develop sufficient statistical confidence. This data may then be
used in the generation of a phase reference (relative probe) or
tachometer signal for a piece of equipment. This phase reference
may be used to align phase data such as vibrational data or
acceleration data from multiple sensors located at different
positions on a component or on different components within a
system. This information may facilitate the determination of torque
for different components or the generation of an Operational
Deflection Shape (ODS), indicating the extent of mechanical
deflection of one or more components during an operational mode,
which in turn may be used to measure mechanical torque in the
component.
[0431] The higher resolution data stream may provide additional
data for the detection of transitory signals in low speed
operations. The identification of transitory signals may enable the
identification of defects in a piece of equipment or component
[0432] In an illustrative and non-limiting example, the monitoring
device may be used to identify mechanical jitter for use in failure
prediction models. The monitoring device may begin acquiring data
when the piece of equipment starts up through ramping up to
operating speed and then during operation. Once at operating speed,
it is anticipated that the torsional jitter should be minimal and
changes in torsion during this phase may be indicative of cracks,
bearing faults and the like. Additionally, known torsions may be
removed from the signal to facilitate in the identification of
unanticipated torsions resulting from system design flaws or
component wear. Having phase information associated with the data
collected at operating speed may facilitate identification of a
location of vibration and potential component wear. Relative phase
information for a plurality of sensors located throughout a machine
may facilitate the evaluation of torsion as it is propagated
through a piece of equipment.
[0433] An example system data collection in an industrial
environment includes a data acquisition circuit that interprets a
number of detection values from a number of input sensors
communicatively coupled to the data acquisition circuit, each of
the number of detection values corresponding to at least one of the
input sensors, a signal evaluation circuit that obtains at least
one of a vibration amplitude, a vibration frequency and a vibration
phase location corresponding to at least one of the input sensors
in response to the number of detection values, and a response
circuit that performs at least one operation in response to at the
at least one of the vibration amplitude, the vibration frequency
and the vibration phase location. Certain further embodiments of an
example system include: where the signal evaluation circuit
includes a phase detection circuit, or a phase detection circuit
and a phase lock loop circuit and/or a band pass filter; where the
number of input sensors includes at least two input sensors
providing phase information and at least one input sensor providing
non-phase sensor information; the signal evaluation circuit further
aligning the phase information provided by the at least two of the
input sensors; where the at least one operation is further in
response to at least one of: a change in magnitude of the vibration
amplitude; a change in frequency or phase of vibration; a rate of
change in at least one of vibration amplitude, vibration frequency
and vibration phase; a relative change in value between at least
two of vibration amplitude, vibration frequency and vibration
phase; and/or a relative rate of change between at least two of
vibration amplitude, vibration frequency, and vibration phase; the
system further including an alert circuit, where the at least one
operation includes providing an alert and where the alert may be
one of haptic, audible and visual; a data storage circuit, where at
least one of the vibration amplitude, vibration frequency, and
vibration phase is stored periodically to create a vibration
history, and where the at least one operation includes storing
additional data in the data storage circuit (e.g., as a vibration
fingerprint for a component); where the storing additional data in
the data storage circuit is further in response to at least one of:
a change in magnitude of the vibration amplitude; a change in
frequency or phase of vibration; a rate of change in the vibration
amplitude, frequency or phase; a relative change in value between
at least two of vibration amplitude, frequency and phase; and a
relative rate of change between at least two of vibration
amplitude, frequency and phase; the system further comprising at
least one of a multiplexing (MUX) circuit whereby alternative
combinations of detection values may be selected based on at least
one of user input, a detected state, and a selected operating
parameter for a machine; where each of the number of detection
values corresponds to at least one of the input sensors; where the
at least one operation includes enabling or disabling the
connection of one or more portions of the multiplexing circuit; a
MUX control circuit that interprets a subset of the number of
detection values and provides the logical control of the MUX and
the correspondence of MUX input and detected values as a result;
and/or where the logic control of the MUX includes adaptive
scheduling of the select lines.
[0434] An example method of monitoring a component, includes
receiving time-based data from at least one sensor, phase-locking
the received data with a reference signal, transforming the
received time-based data to frequency data, filtering the frequency
data to remove tachometer frequencies, identifying low amplitude
signals occurring at high frequencies, and activating an alarm if a
low amplitude signal exceeds a threshold.
[0435] An example system for data collection, processing, and
utilization of signals in an industrial environment includes a
plurality of monitoring devices, each monitoring device comprising
a data acquisition circuit structured to interpret a plurality of
detection values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of
detection values corresponding to at least one of the input
sensors; a signal evaluation circuit structured to obtain at least
one of vibration amplitude, vibration frequency and a vibration
phase location corresponding to at least one of the input sensors
in response to the corresponding at least one of the plurality of
detection values; a data storage facility for storing a subset of
the plurality of detection values; a communication circuit
structured to communicate at least one selected detection value to
a remote server; and a monitoring application on the remote server
structured to: receive the at least one selected detection value;
jointly analyze a subset of the detection values received from the
plurality of monitoring devices; and recommend an action.
[0436] In certain further embodiments, an example system includes:
for each monitoring device, the plurality of input sensors include
at least one input sensor providing phase information and at least
one input sensor providing non-phase input sensor information and
where joint analysis includes using the phase information from the
plurality of monitoring devices to align the information from the
plurality of monitoring devices; where the subset of detection
values is selected based on data associated with a detection value
including at least one: common type of component, common type of
equipment, and common operating conditions and further selected
based on one of anticipated life of a component associated with
detection values, type of the equipment associated with detection
values, and operational conditions under which detection values
were measured; and/or where the analysis of the subset of detection
values includes feeding a neural net with the subset of detection
values and supplemental information to learn to recognize various
operating states, health states, life expectancies and fault states
utilizing deep learning techniques, wherein the supplemental
information comprises one of component specification, component
performance, equipment specification, equipment performance,
maintenance records, repair records and an anticipated state
model.
[0437] An example system for data collection in an industrial
environment includes a data acquisition circuit structured to
interpret a plurality of detection values from a plurality of input
sensors communicatively coupled to the data acquisition circuit,
each of the plurality of detection values corresponding to at least
one of the input sensors; a signal evaluation circuit structured to
obtain at least one of vibration amplitude, vibration frequency and
vibration phase location corresponding to at least one of the input
sensors in response to the corresponding at least one of a
plurality of detection values; a multiplexing circuit whereby
alternative combinations of the detection values may be selected
based on at least one of user input, a detected state and a
selected operating parameter for a machine, each of the plurality
of detection values corresponding to at least one of the input
sensors; and a response circuit structured to perform at least one
operation in response to at the at least one of the vibration
amplitude, vibration frequency and vibration phase location.
[0438] An example system for data collection in a piece of
equipment, includes a data acquisition circuit structured to
interpret a plurality of detection values from a plurality of input
sensors communicatively coupled to the data acquisition circuit,
each of the plurality of detection values corresponding to at least
one of the input sensors; a timer circuit structured to generate a
timing signal based on a first detected value of the plurality of
detection values; a signal evaluation circuit structured to obtain
at least one of vibration amplitude, vibration frequency and
vibration phase location corresponding to a second detected value
comprising: a phase detection circuit structured to determine a
relative phase difference between a second detection value of the
plurality of detection values and the timing signal; and a response
circuit structured to perform at least one operation in response to
at the at least one of the vibration amplitude, vibration frequency
and vibration phase location.
[0439] An example system for bearing analysis in an industrial
environment, includes a data acquisition circuit structured to
interpret a plurality of detection values from a plurality of input
sensors communicatively coupled to the data acquisition circuit,
each of the plurality of detection values corresponding to at least
one of the input sensors; a data storage for storing specifications
and anticipated state information for a plurality of bearing types
and buffering the plurality of detection values for a predetermined
length of time; a timer circuit structured to generate a timing
signal based on a first detected value of the plurality of
detection values; a bearing analysis circuit structured to analyze
buffered detection values relative to specifications and
anticipated state information resulting in a life prediction
comprising: a phase detection circuit structured to determine a
relative phase difference between a second detection value of the
plurality of detection values and the timing signal; and a signal
evaluation circuit structured to obtain at least one of vibration
amplitude, vibration frequency and vibration phase location
corresponding to a second detected value: and a response circuit
structured to perform at least one operation in response to at the
at least one of the vibration amplitude, vibration frequency and
vibration phase location.
[0440] An example motor monitoring system includes: a data
acquisition circuit structured to interpret a plurality of
detection values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of
detection values corresponding to at least one of the input
sensors; a data storage circuit structured to store specifications,
system geometry, and anticipated state information for the motor
and motor components, store historical motor performance and buffer
the plurality of detection values for a predetermined length of
time; a timer circuit structured to generate a timing signal based
on a first detected value of the plurality of detection values; a
motor analysis circuit structured to analyze buffered detection
values relative to specifications and anticipated state information
resulting in a motor performance parameter comprising: a phase
detection circuit structured to determine a relative phase
difference between a second detection value of the plurality of
detection values and the timing signal; and a signal evaluation
circuit structured to obtain at least one of vibration amplitude,
vibration frequency and vibration phase location corresponding to a
second detected value and analyze the at least one of vibration
amplitude, vibration frequency and vibration phase location
relative to buffered detection values, specifications and
anticipated state information resulting in a motor performance
parameter; and a response circuit structured to perform at least
one operation in response to at the at least one of vibration
amplitude, vibration frequency and vibration phase location and
motor performance parameter.
[0441] An example system for estimating a vehicle steering system
performance parameter, includes: a data acquisition circuit
structured to interpret a plurality of detection values from a
plurality of input sensors communicatively coupled to the data
acquisition circuit, each of the plurality of detection values
corresponding to at least one of the input sensors; a data storage
circuit structured to store specifications, system geometry, and
anticipated state information for the vehicle steering system, the
rack, the pinion, and the steering column, store historical
steering system performance and buffer the plurality of detection
values for a predetermined length of time; a timer circuit
structured to generate a timing signal based on a first detected
value of the plurality of detection values; a steering system
analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a steering system performance parameter comprising: a
phase detection circuit structured to determine a relative phase
difference between a second detection value of the plurality of
detection values and the timing signal; and a signal evaluation
circuit structured to obtain at least one of vibration amplitude,
vibration frequency and vibration phase location corresponding to a
second detected value and analyze the at least one of vibration
amplitude, vibration frequency and vibration phase location
relative to buffered detection values, specifications and
anticipated state information resulting in a steering system
performance parameter; and a response circuit structured to perform
at least one operation in response to at the at least one of
vibration amplitude, vibration frequency and vibration phase
location and the steering system performance parameter.
[0442] An example system for estimating a health parameter a pump
performance parameter includes a data acquisition circuit
structured to interpret a plurality of detection values from a
plurality of input sensors communicatively coupled to the data
acquisition circuit, each of the plurality of detection values
corresponding to at least one of the input sensors; a data storage
circuit structured to store specifications, system geometry, and
anticipated state information for the pump and pump components
associated with the detection values, store historical pump
performance and buffer the plurality of detection values for a
predetermined length of time; a timer circuit structured to
generate a timing signal based on a first detected value of the
plurality of detection values; a pump analysis circuit structured
to analyze buffered detection values relative to specifications and
anticipated state information resulting in a pump performance
parameter comprising: a phase detection circuit structured to
determine a relative phase difference between a second detection
value of the plurality of detection values and the timing signal;
and a signal evaluation circuit structured to obtain at least one
of vibration amplitude, vibration frequency and vibration phase
location corresponding to a second detected value and analyze the
at least one of vibration amplitude, vibration frequency and
vibration phase location relative to buffered detection values,
specifications and anticipated state information resulting in a
pump performance parameter; and a response circuit structured to
perform at least one operation in response to at the at least one
of vibration amplitude, vibration frequency and vibration phase
location and the pump performance parameter, wherein the pump is
one of a water pump in a car and a mineral pump.
[0443] An example system for estimating a drill performance
parameter for a drilling machine, includes: a data acquisition
circuit structured to interpret a plurality of detection values
from a plurality of input sensors communicatively coupled to the
data acquisition circuit, each of the plurality of detection values
corresponding to at least one of the input sensors; a data storage
circuit structured to store specifications, system geometry, and
anticipated state information for the drill and drill components
associated with the detection values, store historical drill
performance and buffer the plurality of detection values for a
predetermined length of time; a timer circuit structured to
generate a timing signal based on a first detected value of the
plurality of detection values; a drill analysis circuit structured
to analyze buffered detection values relative to specifications and
anticipated state information resulting in a drill performance
parameter comprising: a phase detection circuit structured to
determine a relative phase difference between a second detection
value of the plurality of detection values and the timing signal;
and a signal evaluation circuit structured to obtain at least one
of vibration amplitude, vibration frequency and vibration phase
location corresponding to a second detected value and analyze the
at least one of vibration amplitude, vibration frequency and
vibration phase location relative to buffered detection values,
specifications and anticipated state information resulting in a
drill performance parameter; and a response circuit structured to
perform at least one operation in response to at the at least one
of vibration amplitude, vibration frequency and vibration phase
location and the drill performance parameter, wherein the drilling
machine is one of an oil drilling machine and a gas drilling
machine.
[0444] An example system for estimating a conveyor health
parameter, includes: a data acquisition circuit structured to
interpret a plurality of detection values from a plurality of input
sensors communicatively coupled to the data acquisition circuit,
each of the plurality of detection values corresponding to at least
one of the input sensors; a data storage circuit structured to
store specifications, system geometry, and anticipated state
information for a conveyor and conveyor components associated with
the detection values, store historical conveyor performance and
buffer the plurality of detection values for a predetermined length
of time; a timer circuit structured to generate a timing signal
based on a first detected value of the plurality of detection
values; a conveyor analysis circuit structured to analyze buffered
detection values relative to specifications and anticipated state
information resulting in a conveyor performance parameter
comprising: a phase detection circuit structured to determine a
relative phase difference between a second detection value of the
plurality of detection values and the timing signal; and a signal
evaluation circuit structured to obtain at least one of vibration
amplitude, vibration frequency and vibration phase location
corresponding to a second detected value and analyze the at least
one of vibration amplitude, vibration frequency and vibration phase
location relative to buffered detection values, specifications and
anticipated state information resulting in a conveyor performance
parameter; and a response circuit structured to perform at least
one operation in response to at the at least one of vibration
amplitude, vibration frequency and vibration phase location and the
conveyor performance parameter.
[0445] An example system for estimating an agitator health
parameter, includes: a data acquisition circuit structured to
interpret a plurality of detection values from a plurality of input
sensors communicatively coupled to the data acquisition circuit,
each of the plurality of detection values corresponding to at least
one of the input sensors; a data storage circuit structured to
store specifications, system geometry, and anticipated state
information for an agitator and agitator components associated with
the detection values, store historical agitator performance and
buffer the plurality of detection values for a predetermined length
of time; a timer circuit structured to generate a timing signal
based on a first detected value of the plurality of detection
values; an agitator analysis circuit structured to analyze buffered
detection values relative to specifications and anticipated state
information resulting in an agitator performance parameter
comprising: a phase detection circuit structured to determine a
relative phase difference between a second detection value of the
plurality of detection values and the timing signal; and a signal
evaluation circuit structured to obtain at least one of vibration
amplitude, vibration frequency and vibration phase location
corresponding to a second detected value and analyze the at least
one of vibration amplitude, vibration frequency and vibration phase
location relative to buffered detection values, specifications and
anticipated state information resulting in an agitator performance
parameter; and a response circuit structured to perform at least
one operation in response to at the at least one of vibration
amplitude, vibration frequency and vibration phase location and the
agitator performance parameter, wherein the agitator is one of a
rotating tank mixer, a large tank mixer, a portable tank mixers, a
tote tank mixer, a drum mixer, a mounted mixer and a propeller
mixer.
[0446] An example system for estimating a compressor health
parameter, includes: a data acquisition circuit structured to
interpret a plurality of detection values from a plurality of input
sensors communicatively coupled to the data acquisition circuit,
each of the plurality of detection values corresponding to at least
one of the input sensors; a data storage circuit structured to
store specifications, system geometry, and anticipated state
information for a compressor and compressor components associated
with the detection values, store historical compressor performance
and buffer the plurality of detection values for a predetermined
length of time; a timer circuit structured to generate a timing
signal based on a first detected value of the plurality of
detection values; a compressor analysis circuit structured to
analyze buffered detection values relative to specifications and
anticipated state information resulting in a compressor performance
parameter comprising: a phase detection circuit structured to
determine a relative phase difference between a second detection
value of the plurality of detection values and the timing signal;
and a signal evaluation circuit structured to obtain at least one
of vibration amplitude, vibration frequency and vibration phase
location corresponding to a second detected value and analyze the
at least one of vibration amplitude, vibration frequency and
vibration phase location relative to buffered detection values,
specifications and anticipated state information resulting in a
compressor performance parameter; and a response circuit structured
to perform at least one operation in response to at the at least
one of vibration amplitude, vibration frequency and vibration phase
location and the compressor performance parameter.
[0447] An example system for estimating an air conditioner health
parameter, includes: a data acquisition circuit structured to
interpret a plurality of detection values from a plurality of input
sensors communicatively coupled to the data acquisition circuit,
each of the plurality of detection values corresponding to at least
one of the input sensors; a data storage circuit structured to
store specifications, system geometry, and anticipated state
information for an air conditioner and air conditioner components
associated with the detection values, store historical air
conditioner performance and buffer the plurality of detection
values for a predetermined length of time; a timer circuit
structured to generate a timing signal based on a first detected
value of the plurality of detection values; an air conditioner
analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in an air conditioner performance parameter comprising: a
phase detection circuit structured to determine a relative phase
difference between a second detection value of the plurality of
detection values and the timing signal; and a signal evaluation
circuit structured to obtain at least one of vibration amplitude,
vibration frequency and vibration phase location corresponding to a
second detected value and analyze the at least one of vibration
amplitude, vibration frequency and vibration phase location
relative to buffered detection values, specifications and
anticipated state information resulting in an air conditioner
performance parameter; and a response circuit structured to perform
at least one operation in response to at the at least one of
vibration amplitude, vibration frequency and vibration phase
location and the air conditioner performance parameter.
[0448] An example system for estimating a centrifuge health
parameter, includes: a data acquisition circuit structured to
interpret a plurality of detection values from a plurality of input
sensors communicatively coupled to the data acquisition circuit,
each of the plurality of detection values corresponding to at least
one of the input sensors; a data storage circuit structured to
store specifications, system geometry, and anticipated state
information for a centrifuge and centrifuge components associated
with the detection values, store historical centrifuge performance
and buffer the plurality of detection values for a predetermined
length of time; a timer circuit structured to generate a timing
signal based on a first detected value of the plurality of
detection values; a centrifuge analysis circuit structured to
analyze buffered detection values relative to specifications and
anticipated state information resulting in a centrifuge performance
parameter comprising: a phase detection circuit structured to
determine a relative phase difference between a second detection
value of the plurality of detection values and the timing signal;
and a signal evaluation circuit structured to obtain at least one
of vibration amplitude, vibration frequency and vibration phase
location corresponding to a second detected value and analyze the
at least one of vibration amplitude, vibration frequency and
vibration phase location relative to buffered detection values,
specifications and anticipated state information resulting in a
centrifuge performance parameter; and a response circuit structured
to perform at least one operation in response to at the at least
one of vibration amplitude, vibration frequency and vibration phase
location and the centrifuge performance parameter.
[0449] In embodiments, information about the health of a component
or piece of industrial equipment may be obtained by comparing the
values of multiple signals at the same point in a process. This may
be accomplished by aligning a signal relative to other related data
signals, timers, or reference signals An embodiment of a data
monitoring device 8700, 8718 is shown in FIGS. 35-37 and may
include a controller 8702, 8720. The controller may include a data
acquisition circuit 8704, 8722, a signal evaluation circuit 8708, a
data storage circuit 8716 and an optional response circuit 8710.
The signal evaluation circuit 8708 may comprise a timer circuit
8714 and, optionally, a phase detection circuit 8712.
[0450] The data monitoring device may include a plurality of
sensors 8706 communicatively coupled to a controller 8702. The
plurality of sensors 8706 may be wired to ports on the data
acquisition circuit 8704. The plurality of sensors 8706 may be
wirelessly connected to the data acquisition circuit 8704 which may
be able to access detection values corresponding to the output of
at least one of the plurality of sensors 8706 where the sensors
8706 may be capturing data on different operational aspects of a
piece of equipment or an operating component. In embodiments, as
illustrated in FIGS. 36 and 37, one or more external sensors 8724
which are not explicitly part of a monitoring device 8718 may be
opportunistically connected to or accessed by the monitoring device
8718. The data acquisition circuit 8722 may include one or more
input ports 8726. The one or more external sensors 8724 may be
directly connected to the one or more input ports 8726 on the data
acquisition circuit 8722 of the controller 8720. In embodiments, as
shown in FIG. 37, a data acquisition circuit 8722 may further
comprise a wireless communications circuit 8728 to access detection
values corresponding to the one or more external sensors 8724
wirelessly or via a separate source or some combination of these
methods.
[0451] The selection of the plurality of sensors 8706 8724 for
connection to a data monitoring device 8700 8718 designed for a
specific component or piece of equipment may depend on a variety of
considerations such as accessibility for installing new sensors,
incorporation of sensors in the initial design, anticipated
operational and failure conditions, resolution desired at various
positions in a process or plant, reliability of the sensors, and
the like. The impact of a failure, time response of a failure
(e.g., warning time and/or off-nominal modes occurring before
failure), likelihood of failure, and/or sensitivity required and/or
difficulty to detect failed conditions may drive the extent to
which a component or piece of equipment is monitored with more
sensors and/or higher capability sensors being dedicated to systems
where unexpected or undetected failure would be costly or have
severe consequences.
[0452] The signal evaluation circuit 8708 may process the detection
values to obtain information about a component or piece of
equipment being monitored. Information extracted by the signal
evaluation circuit 8708 may comprise information regarding what
point or time in a process corresponds with a detection value where
the point in time is based on a timing signal generated by the
timer circuit 8714. The start of the timing signal may be generated
by detecting an edge of a control signal such as a rising edge,
falling edge or both where the control signal may be associated
with the start of a process. The start of the timing signal may be
triggered by an initial movement of a component or piece of
equipment. The start of the timing signal may be triggered by an
initial flow through a pipe or opening or by a flow achieving a
predetermined rate. The start of the timing signal may be triggered
by a state value indicating a process has commenced--for example
the state of a switch, button, data value provided to indicate the
process has commenced, or the like. Information extracted may
comprise information regarding a difference in phase, determined by
the phase detection circuit 8712, between a stream of detection
value and the time signal generated by the timer circuit 8714.
Information extracted may comprise information regarding a
difference in phase between one stream of detection values and a
second stream of detection values where the first stream of
detection values is used as a basis or trigger for a timing signal
generated by the timer circuit.
[0453] Depending on the type of equipment, the component being
measured, the environment in which the equipment is operating and
the like, sensors 8706 8724 may comprise one or more of, without
limitation, a thermometer, a hygrometer, a voltage sensor, a
current sensor, an accelerometer, a velocity detector, a light or
electromagnetic sensor (e.g., determining temperature, composition
and/or spectral analysis, and/or object position or movement), an
image sensor, a displacement sensor, a turbidity meter, a viscosity
meter, a load sensor, a tri-axial sensor, a tachometer, a fluid
pressure meter, an air flow meter, a horsepower meter, a flow rate
meter, a fluid particle detector, an acoustical sensor, a pH
sensor, and the like.
[0454] The sensors 8706 8724 may provide a stream of data over time
that has a phase component, such as acceleration or vibration,
allowing for the evaluation of phase or frequency analysis of
different operational aspects of a piece of equipment or an
operating component. The sensors 8706 8724 may provide a stream of
data that is not phase based such as temperature, humidity, load,
and the like. The sensors 8706 8724 may provide a continuous or
near continuous stream of data over time, periodic readings,
event-driven readings, and/or readings according to a selected
interval or schedule.
[0455] In embodiments, as illustrated in FIGS. 38 and 39, the data
acquisition circuit 8734 may further comprise a multiplexer circuit
8736 as described elsewhere herein. Outputs from the multiplexer
circuit 8736 may be utilized by the signal evaluation circuit 8708.
The response circuit 8710 may have the ability to turn on and off
portions of the multiplexer circuit 8736. The response circuit 8710
may have the ability to control the control channels of the
multiplexer circuit 8736
[0456] The response circuit 8710 may further comprise evaluating
the results of the signal evaluation circuit 8708 and, based on
certain criteria, initiating an action. The criteria may include a
sensor's detection values at certain frequencies or phases relative
to the timer signal where the frequencies or phases of interest may
be based on the equipment geometry, equipment control schemes,
system input, historical data, current operating conditions, and/or
an anticipated response. Criteria may include a predetermined
maximum or minimum value for a detection value from a specific
sensor, a cumulative value of a sensor's corresponding detection
value over time, a change in value, a rate of change in value,
and/or an accumulated value (e.g., a time spent above/below a
threshold value, a weighted time spent above/below one or more
threshold values, and/or an area of the detected value above/below
one or more threshold values). The criteria may comprise
combinations of data from different sensors such as relative
values, relative changes in value, relative rates of change in
value, relative values over time, and the like. The relative
criteria may change with other data or information such as process
stage, type of product being processed, type of equipment, ambient
temperature and humidity, external vibrations from other equipment,
and the like.
[0457] Certain embodiments are described herein as detected values
exceeding thresholds or predetermined values, but detected values
may also fall below thresholds or predetermined values--for example
where an amount of change in the detected value is expected to
occur, but detected values indicate that the change may not have
occurred. For example, and without limitation, vibrational data may
indicate system agitation levels, properly operating equipment, or
the like, and vibrational data below amplitude and/or frequency
thresholds may be an indication of a process that is not operating
according to expectations. Except where the context clearly
indicates otherwise, any description herein describing a
determination of a value above a threshold and/or exceeding a
predetermined or expected value is understood to include
determination of a value below a threshold and/or falling below a
predetermined or expected value.
[0458] The predetermined acceptable range may be based on
anticipated system response or vibration based on the equipment
geometry and control scheme such as number of bearings, relative
rotational speed, influx of power to the system at a certain
frequency, and the like. The predetermined acceptable range may
also be based on long term analysis of detection values across a
plurality of similar equipment and components and correlation of
data with equipment failure.
[0459] In some embodiments, an alert may be issued based on the
some of the criteria discussed above. In an illustrative example,
an increase in temperature and energy at certain frequencies may
indicate a hot bearing that is starting to fail. In embodiments,
the relative criteria for an alarm may change with other data or
information such as process stage, type of product being processed
on equipment, ambient temperature and humidity, external vibrations
from other equipment and the like. In an illustrative and
non-limiting example, the response circuit 8710 may initiate an
alert if a vibrational amplitude and/or frequency exceeds a
predetermined maximum value, if there is a change or rate of change
that exceeds a predetermined acceptable range, and/or if an
accumulated value based on vibrational amplitude and/or frequency
exceeds a threshold.
[0460] In embodiments, response circuit 8710 may cause the data
acquisition circuit 8704 to enable or disable the processing of
detection values corresponding to certain sensors based on the some
of the criteria discussed above. This may include switching to
sensors having different response rates, sensitivity, ranges, and
the like; accessing new sensors or types of sensors, and the like.
This switching may be implemented by changing the control signals
for a multiplexer circuit 8736 and/or by turning on or off certain
input sections of the multiplexer circuit 8736. The response
circuit 8710 may make recommendations for the replacement of
certain sensors in the future with sensors having different
response rates, sensitivity, ranges, and the like. The response
circuit 8710 may recommend design alterations for future
embodiments of the component, the piece of equipment, the operating
conditions, the process, and the like.
[0461] In embodiments, the response circuit 8710 may recommend
maintenance at an upcoming process stop or initiate a maintenance
call. The response circuit 8710 may recommend changes in process or
operating parameters to remotely balance the piece of equipment. In
embodiments, the response circuit 8710 may implement or recommend
process changes--for example to lower the utilization of a
component that is near a maintenance interval, operating
off-nominally, or failed for purpose but still at least partially
operational. In an illustrative example, vibration phase
information, derived by the phase detection circuit 8712 relative
to a timer signal from the timer circuit 8714, may be indicative of
a physical location of a problem. Based on the vibration phase
information, system design flaws, off-nominal operation, and/or
component or process failures may be identified.
[0462] In embodiments, based on relevant operating conditions
and/or failure modes which may occur in as sensor values approach
one or more criteria, the signal evaluation circuit 8708 may store
data in the data storage circuit 8716 based on the fit of data
relative to one or more criteria. Based on one sensor input meeting
or approaching specified criteria or range, the signal evaluation
circuit 8708 may store additional data such as RPMs, component
loads, temperatures, pressures, vibrations in the data storage
circuit 8716. The signal evaluation circuit 8708 may store data at
a higher data rate for greater granularity in future processing,
the ability to reprocess at different sampling rates, and/or to
enable diagnosing or post-processing of system information where
operational data of interest is flagged, and the like.
[0463] In embodiments, as shown in FIGS. 40 and 41 and 42 and 43, a
data monitoring system 8762 may include at least one data
monitoring device 8768. The at least one data monitoring device
8768 may include sensors 8706 and a controller 8770 comprising a
data acquisition circuit 8704, a signal evaluation circuit 8772, a
data storage circuit 8742, and a communications circuit 8752 to
allow data and analysis to be transmitted to a monitoring
application 8776 on a remote server 8774. The signal evaluation
circuit 8772 may include at least one of a phase detection circuit
8712 and a timer circuit 8714. The signal evaluation circuit 8772
may periodically share data with the communication circuit 8752 for
transmittal to the remote server 8774 to enable the tracking of
component and equipment performance over time and under varying
conditions by a monitoring application 8776. Because relevant
operating conditions and/or failure modes may occur as sensor
values approach one or more criteria, the signal evaluation circuit
8708 may share data with the communication circuit 8752 for
transmittal to the remote server 8774 based on the fit of data
relative to one or more criteria. Based on one sensor input meeting
or approaching specified criteria or range, the signal evaluation
circuit 8708 may share additional data such as RPMs, component
loads, temperatures, pressures, vibrations, and the like for
transmittal. The signal evaluation circuit 8772 may share data at a
higher data rate for transmittal to enable greater granularity in
processing on the remote server.
[0464] In embodiments, as shown in FIG. 40, the communications
circuit 8752 may communicated data directly to a remote server
8774. In embodiments, as shown in FIG. 41, the communications
circuit 8752 may communicate data to an intermediate computer 8754
which may include a processor 8756 running an operating system 8758
and a data storage circuit 8760. The intermediate computer 8754 may
collect data from a plurality of data monitoring devices and send
the cumulative data to the remote server 8774.
[0465] In embodiments as illustrated in FIGS. 42 and 43, a data
collection system 8762 may have a plurality of monitoring devices
8768 collecting data on multiple components in a single piece of
equipment, collecting data on the same component across a plurality
of pieces of equipment, (both the same and different types of
equipment) in the same facility as well as collecting data from
monitoring devices in multiple facilities. In embodiments, as show
in in FIG. 42 the communications circuit 8752 may communicated data
directly to a remote server 8774. In embodiments, as shown in FIG.
43, the communications circuit 8752 may communicate data to an
intermediate computer 8754 which may include a processor 8756
running an operating system 8758 and a data storage circuit 8760.
The intermediate computer 8754 may collect data from a plurality of
data monitoring devices and send the cumulative data to the remote
server 8774.
[0466] In embodiments, a monitoring application 8776 on a remote
server 8774 may receive and store one or more of detection values,
timing signals and data coming from a plurality of the various
monitoring devices 8768. The monitoring application 8776 may then
select subsets of the detection values, timing signals and data to
be jointly analyzed. Subsets for analysis may be selected based on
a single type of component or a single type of equipment in which a
component is operating. Subsets for analysis may be selected or
grouped based on common operating conditions such as size of load,
operational condition (e.g., intermittent, continuous, process
stage), operating speed or tachometer, common ambient environmental
conditions such as humidity, temperature, air or fluid particulate,
and the like. Subsets for analysis may be selected based on the
effects of other nearby equipment such as nearby machines rotating
at similar frequencies.
[0467] The monitoring application 8776 may then analyze the
selected subset. In an illustrative example, data from a single
component may be analyzed over different time periods such as one
operating cycle, several operating cycles, a month, a year, the
life of the component or the like. Data from multiple components of
the same type may also be analyzed over different time periods.
Trends in the data such as changes in frequency or amplitude may be
correlated with failure and maintenance records associated with the
same or a related component or piece of equipment. Trends in the
data such as changing rates of change associated with start-up or
different points in the process may be identified. Additional data
may be introduced into the analysis such as output product quality,
indicated success or failure of a process, and the like.
Correlation of trends and values for different types of data may be
analyzed to identify those parameters whose short-term analysis
might provide the best prediction regarding expected performance.
This information may be transmitted back to the monitoring device
to update types of data collected and analyzed locally or to
influence the design of future monitoring devices.
[0468] In an illustrative and non-limiting example, a monitoring
device 8768 may be used to collect and process sensor data to
measure mechanical torque. The monitoring device 8768 may be in
communication with or include a high resolution, high speed
vibration sensor to collect data over a period of time sufficient
to measure multiple cycles of rotation. For gear driven components,
the sampling resolution of the sensor should be such that the
number of samples taken per cycle is at least equal to the number
of gear teeth driving the component. It will be understood that a
lower sampling resolution may also be utilized, which may result in
a lower confidence determination and/or taking data over a longer
period of time to develop sufficient statistical confidence. This
data may then be used in the generation of a phase reference
(relative probe) or tachometer signal for a piece of equipment.
This phase reference may be used directly or used by the timer
circuit 8714 to generate a timing signal to align phase data such
as vibrational data or acceleration data from multiple sensors
located at different positions on a component or on different
components within a system. This information may facilitate the
determination of torque for different components or the generation
of an Operational Deflection Shape (ODS).
[0469] A higher resolution data stream may also provide additional
data for the detection of transitory signals in low speed
operations. The identification of transitory signals may enable the
identification of defects in a piece of equipment or component
operating a low RPMs.
[0470] In an illustrative and non-limiting example, the monitoring
device may be used to identify mechanical jitter for use in failure
prediction models. The monitoring device may begin acquiring data
when the piece of equipment starts up, through ramping up to
operating speed, and then during operation. Once at operating
speed, it is anticipated that the torsional jitter should be
minimal or within expected ranges, and changes in torsion during
this phase may be indicative of cracks, bearing faults, and the
like. Additionally, known torsions may be removed from the signal
to facilitate in the identification of unanticipated torsions
resulting from system design flaws, component wear, or unexpected
process events. Having phase information associated with the data
collected at operating speed may facilitate identification of a
location of vibration and potential component wear, and/or may be
further correlated to a type of failure for a component. Relative
phase information for a plurality of sensors located throughout a
machine may facilitate the evaluation of torsion as it is
propagated through a piece of equipment.
[0471] In embodiments, the monitoring application 8776 may have
access to equipment specifications, equipment geometry, component
specifications, component materials, anticipated state information
for plurality of component types, operational history, historical
detection values, component life models, and the like for use in
analyzing the selected subset using rule-based or model-based
analysis. In embodiments, the monitoring application 8776 may feed
a neural net with the selected subset to learn to recognize various
operating state, health states (e.g., lifetime predictions) and
fault states utilizing deep learning techniques. In embodiments, a
hybrid of the two techniques (model-based learning and deep
learning) may be used.
[0472] In an illustrative and non-limiting example, component
health of: conveyors and lifters in an assembly line; water pumps
on industrial vehicles; factory air conditioning units; drilling
machines, screw drivers, compressors, pumps, gearboxes, vibrating
conveyors, mixers and motors situated in the oil and gas fields;
factory mineral pumps; centrifuges, and refining tanks situated in
oil and gas refineries; and compressors in gas handling systems may
be monitored using the phase detection and alignment techniques,
data monitoring devices and data collection systems described
herein.
[0473] In an illustrative and non-limiting example, the component
health of equipment to promote chemical reactions deployed in
chemical and pharmaceutical production lines (e.g. rotating
tank/mixer agitators, mechanical/rotating agitators, and propeller
agitators) may be evaluated using the phase detection and alignment
techniques, data monitoring devices and data collection systems
described herein.
[0474] In an illustrative and non-limiting example, the component
health of vehicle steering mechanisms and/or vehicle engines may be
evaluated using the phase detection and alignment techniques, data
monitoring devices and data collection systems described
herein.
[0475] An example monitoring system for data collection, includes a
data acquisition circuit structured to interpret a plurality of
detection values, each of the plurality of detection values
corresponding to at least one of a plurality of input sensors
communicatively coupled to the data acquisition circuit; a signal
evaluation circuit comprising: a timer circuit structured to
generate at least one timing signal; and a phase detection circuit
structured to determine a relative phase difference between at
least one of the plurality of detection values and at least one of
the timing signals from the timer circuit; and a response circuit
structured to perform at least one operation in response to the
relative phase difference. In certain further embodiments, an
example system includes:
[0476] wherein the at least one operation is further in response to
at least one of: a change in amplitude of at least one of the
plurality of detection values; a change in frequency or relative
phase of at least one of the plurality of detection values; a rate
of change in both amplitude and relative phase of at least one the
plurality of detection values; and a relative rate of change in
amplitude and relative phase of at least one the plurality of
detection values; wherein the at least one operation comprises
issuing an alert; wherein the alert may be one of haptic, audible
and visual; a data storage circuit, wherein the relative phase
difference and at least one of the detection values and the timing
signal are stored; wherein the at least one operation further
comprises storing additional data in the data storage circuit;
wherein the storing additional data in the data storage circuit is
further in response to at least one of: a change in the relative
phase difference and a relative rate of change in the relative
phase difference; wherein the data acquisition circuit further
comprises at least one multiplexer circuit (MUX) whereby
alternative combinations of detection values may be selected based
on at least one of user input and a selected operating parameter
for a machine, wherein each of the plurality of detection values
corresponds to at least one of the input sensors; wherein the at
least one operation comprises enabling or disabling one or more
portions of the multiplexer circuit, or altering the multiplexer
control lines; wherein the data acquisition circuit comprises at
least two multiplexer circuits and the at least one operation
comprises changing connections between the at least two multiplexer
circuits; and/or the system further comprising a MUX control
circuit structured to interpret a subset of the plurality of
detection values and provide the logical control of the MUX and the
correspondence of MUX input and detected values as a result,
wherein the logic control of the MUX comprises adaptive scheduling
of the select lines.
[0477] An example system for data collection, includes: a data
acquisition circuit structured to interpret a plurality of
detection values, each of the plurality of detection values
corresponding to at least one of a plurality of input sensors
communicatively coupled to the data acquisition circuit; a signal
evaluation circuit comprising: a timer circuit structured to
generate a timing signal based on a first detected value of the
plurality of detection values; and a phase detection circuit
structured to determine a relative phase difference between a
second detection value of the plurality of detection values and the
timing signal; and a phase response circuit structured to perform
at least one operation in response to the phase difference. In
certain further embodiments, an example system includes wherein the
at least one operation is further in response to at least one of: a
change in amplitude of at least one of the plurality of detection
values; a change in frequency or relative phase of at least one of
the plurality of detection values; a rate of change in both
amplitude and relative phase of at least one the plurality of
detection values and a relative rate of change in amplitude and
relative phase of at least one the plurality of detection values;
wherein the at least one operation comprises issuing an alert;
wherein the alert may be one of haptic, audible and visual; where
the system, further includes a data storage circuit; wherein the
relative phase difference and at least one of the detection values
and the timing signal are stored; wherein the at least one
operation further includes storing additional data in the data
storage circuit; wherein the storing additional data in the data
storage circuit is further in response to at least one of: a change
in the relative phase difference and a relative rate of change in
the relative phase difference; wherein the data acquisition circuit
further includes at least one multiplexer (MUX) circuit whereby
alternative combinations of detection values may be selected based
on at least one of user input and a selected operating parameter
for a machine; wherein each of the plurality of detection values
corresponds to at least one of the input sensors; wherein the at
least one operation comprises enabling or disabling one or more
portions of the multiplexer circuit, or altering the multiplexer
control lines; wherein the data acquisition circuit comprises at
least two multiplexer circuits and the at least one operation
comprises changing connections between the at least two multiplexer
circuits; where the system further comprising a MUX control circuit
structured to interpret a subset of the plurality of detection
values and provide the logical control of the MUX and the
correspondence of MUX input and detected values as a result; and/or
wherein the logic control of the MUX comprises adaptive scheduling
of the select lines.
[0478] An example system for data collection, processing, and
utilization of signals in an industrial environment includes a data
acquisition circuit structured to interpret a plurality of
detection values, each of the plurality of detection values
corresponding to at least one of a plurality of input sensors
communicatively coupled to the data acquisition circuit; a signal
evaluation circuit comprising: a timer circuit structured to
generate a timing signal based on a first detected value of the
plurality of detection values; and a phase detection circuit
structured to determine a relative phase difference between a
second detection value of the plurality of detection values and the
timing signal; a data storage facility for storing a subset of the
plurality of detection values and the timing signal; a
communication circuit structured to communicate at least one
selected detection value and the timing signal to a remote server;
and a monitoring application on the remote server structured to
receive the at least one selected detection value and the timing
signal; jointly analyze a subset of the detection values received
from the plurality of monitoring devices; and recommend an action.
In certain embodiments, the example system further includes wherein
joint analysis comprises using the timing signal from each of the
plurality of monitoring devices to align the detection values from
the plurality of monitoring devices and/or wherein the subset of
detection values is selected based on data associated with a
detection value comprising at least one: common type of component,
common type of equipment, and common operating conditions.
[0479] An example system for data collection in an industrial
environment, includes: a data acquisition circuit structured to
interpret a plurality of detection values, each of the plurality of
detection values corresponding to at least one of a plurality of
input sensors communicatively coupled to the data acquisition
circuit, the data acquisition circuit comprising a multiplexer
circuit whereby alternative combinations of the detection values
may be selected based on at least one of user input, a detected
state and a selected operating parameter for a machine, each of the
plurality of detection values corresponding to at least one of the
input sensors; a signal evaluation circuit comprising: a timer
circuit structured to generate a timing signal; and a phase
detection circuit structured to determine a relative phase
difference between at least one of the plurality of detection
values and a signal from the timer circuit; and a response circuit
structured to perform at least one operation in response to the
phase difference.
[0480] An example monitoring system for data collection in a piece
of equipment, includes a data acquisition circuit structured to
interpret a plurality of detection values, each of the plurality of
detection values corresponding to at least one of a plurality of
input sensors communicatively coupled to the data acquisition
circuit; a timer circuit structured to generate a timing signal
based on a first detected value of the plurality of detection
values; a signal evaluation circuit structured to obtain at least
one of vibration amplitude, vibration frequency and vibration phase
location corresponding to a second detected value comprising: a
phase detection circuit structured to determine a relative phase
difference between a second detection value of the plurality of
detection values and the timing signal; and a response circuit
structured to perform at least one operation in response to at the
at least one of the vibration amplitude, vibration frequency and
vibration phase location.
[0481] A monitoring system for bearing analysis in an industrial
environment, the monitoring device includes: a data acquisition
circuit structured to interpret a plurality of detection values,
each of the plurality of detection values corresponding to at least
one of a plurality of input sensors communicatively coupled to the
data acquisition circuit; a timer circuit structured to generate a
timing signal a data storage for storing specifications and
anticipated state information for a plurality of bearing types and
buffering the plurality of detection values for a predetermined
length of time; a timer circuit structured to generate a timing
signal based on a first detected value of the plurality of
detection values; a bearing analysis circuit structured to analyze
buffered detection values relative to specifications and
anticipated state information resulting in a life prediction
comprising: a phase detection circuit structured to determine a
relative phase difference between a second detection value of the
plurality of detection values and the timing signal; a signal
evaluation circuit structured to obtain at least one of vibration
amplitude, vibration frequency and vibration phase location
corresponding to a second detected value: and a response circuit
structured to perform at least one operation in response to at the
at least one of the vibration amplitude, vibration frequency and
vibration phase location.
[0482] In embodiments, information about the health or other status
or state information of or regarding a component or piece of
industrial equipment may be obtained by monitoring the condition of
various components throughout a process. Monitoring may include
monitoring the amplitude of a sensor signal measuring attributes
such as temperature, humidity, acceleration, displacement and the
like. An embodiment of a data monitoring device 9000 is shown in
FIG. 44 and may include a plurality of sensors 9006 communicatively
coupled to a controller 9002. The controller 9002, which may be
part of a data collection device, such as a mobile data collector,
or part of a system, such as a network-deployed or cloud-deployed
system, may include a data acquisition circuit 9004, a signal
evaluation circuit 9008 and a response circuit 9010. The signal
evaluation circuit 9008 may comprise a peak detection circuit 9012.
Additionally, the signal evaluation circuit 9008 may optionally
comprise one or more of a phase detection circuit 9016, a bandpass
filter circuit 9018, a phase lock loop circuit, a torsional
analysis circuit, a bearing analysis circuit, and the like. The
bandpass filter 9018 may be used to filter a stream of detection
values such that values, such as peaks and valleys, are detected
only at or within bands of interest, such as frequencies of
interest. The data acquisition circuit 9004 may include one or more
analog-to-digital converter circuits 9014. A peak amplitude
detected by the peak detection circuit 9012 may be input into one
or more analog-to-digital converter circuits 9014 to provide a
reference value for scaling output of the analog-to-digital
converter circuits 9014 appropriately.
[0483] The plurality of sensors 9006 may be wired to ports on the
data acquisition circuit 9004. The plurality of sensors 9006 may be
wirelessly connected to the data acquisition circuit 9004. The data
acquisition circuit 9004 may be able to access detection values
corresponding to the output of at least one of the plurality of
sensors 9006 where the sensors 9006 may be capturing data on
different operational aspects of a piece of equipment or an
operating component.
[0484] The selection of the plurality of sensors 9006 for a data
monitoring device 9000 designed for a specific component or piece
of equipment may depend on a variety of considerations such as
accessibility for installing new sensors, incorporation of sensors
in the initial design, anticipated operational and failure
conditions, resolution desired at various positions in a process or
plant, reliability of the sensors, power availability, power
utilization, storage utilization, and the like. The impact of a
failure, time response of a failure (e.g., warning time and/or
off-optimal modes occurring before failure), likelihood of failure,
extent of impact of failure, and/or sensitivity required and/or
difficulty to detection failure conditions may drive the extent to
which a component or piece of equipment is monitored with more
sensors and/or higher capability sensors being dedicated to systems
where unexpected or undetected failure would be costly or have
severe consequences.
[0485] The signal evaluation circuit 9008 may process the detection
values to obtain information about a component or piece of
equipment being monitored. Information extracted by the signal
evaluation circuit 9008 may comprise information regarding a peak
value of a signal such as a peak temperature, peak acceleration,
peak velocity, peak pressure, peak weight bearing, peak strain,
peak bending, or peak displacement. The peak detection may be done
using analog or digital circuits. In embodiments, the peak
detection circuit 9012 may be able to distinguish between "local"
or short term peaks in a stream of detection values and a "global"
or longer term peak. In embodiments, the peak detection circuit
9012 may be able to identify peak shapes (not just a single peak
value) such as flat tops, asymptotic approaches, discrete jumps in
the peak value or rapid/steep climbs in peak value, sinusoidal
behavior within ranges and the like. Flat topped peaks may indicate
saturation at of a sensor. Asymptotic approaches to a peak may
indicate linear system behavior. Discrete jumps in value or steep
changes in peak value may indicate quantized or nonlinear behavior
of either the sensor doing the measurement or the behavior of the
component. In embodiments, the system may be able to identify
sinusoidal variations in the peak value within an envelope, such as
an envelope established by line or curve connecting a series of
peak values. It should be noted that references to "peaks" should
be understood to encompass one or more "valleys," representing a
series of low points in measurement, except where context indicates
otherwise.
[0486] In embodiments, a peak value may be used as a reference for
an analog-to-digital conversion circuit 9014.
[0487] In an illustrative and non-limiting example, a temperature
probe may measure the temperature of a gear as it rotates in a
machine. The peak temperature may be detected by a peak detection
circuit 9012. The peak temperature may be fed into an
analog-to-digital converter circuit 9014 to appropriately scale a
stream of detection values corresponding to temperature readings of
the gear as it rotates in a machine. The phase of the stream of
detection values corresponding to temperature relative to an
orientation of the gear may be determined by the phase detection
circuit 9016. Knowing where in the rotation of the gear a peak
temperature is occurring may allow the identification of a bad gear
tooth.
[0488] In some embodiments, two or more sets of detection values
may be fused to create detection values for a virtual sensor. A
peak detection circuit may be used to verify consistency in timing
of peak values between at least one of the two or more sets of
detection values and the detection values for the virtual
sensor.
[0489] In embodiments, the signal evaluation circuit 9008 may be
able to reset the peak detection circuit 9012 upon start-up of the
monitoring device 9000, upon edge detection of a control signal of
the system being monitored, based on a user input, after a system
error and the like. In embodiments, the signal evaluation circuit
9008 may discard an initial portion of the output of the peak
detection circuit 9012 prior to using the peak value as a reference
value for an analog-to-digital conversion circuit to allow the
system to fully come on line.
[0490] Depending on the type of equipment, the component being
measured, the environment in which the equipment is operating and
the like, sensors 9006 may comprise one or more of, without
limitation, a vibration sensor, a thermometer, a hygrometer, a
voltage sensor, a current sensor, an accelerometer, a velocity
detector, a light or electromagnetic sensor (e.g., determining
temperature, composition and/or spectral analysis, and/or object
position or movement), an image sensor, a structured light sensor,
a laser-based image sensor, an acoustic wave sensor, a displacement
sensor, a turbidity meter, a viscosity meter, a load sensor, a
tri-axial sensor, an accelerometer, a tachometer, a fluid pressure
meter, an air flow meter, a horsepower meter, a flow rate meter, a
fluid particle detector, an acoustical sensor, a pH sensor, and the
like, including, without limitation, any of the sensors described
throughout this disclosure and the documents incorporated by
reference.
[0491] The sensors 9006 may provide a stream of data over time that
has a phase component, such as relating to acceleration or
vibration, allowing for the evaluation of phase or frequency
analysis of different operational aspects of a piece of equipment
or an operating component. The sensors 9006 may provide a stream of
data that is not conventionally phase-based, such as temperature,
humidity, load, and the like. The sensors 9006 may provide a
continuous or near continuous stream of data over time, periodic
readings, event-driven readings, and/or readings according to a
selected interval or schedule.
[0492] In embodiments, as illustrated in FIG. 44, the sensors 9006
may be part of the data monitoring device 9000, referred to herein
in some cases as a data collector, which in some cases may comprise
a mobile or portable data collector. In embodiments, as illustrated
in FIGS. 45 and 46, one or more external sensors 9026, which are
not explicitly part of a monitoring device 9020 but rather are new,
previously attached to or integrated into the equipment or
component, may be opportunistically connected to or accessed by the
monitoring device 9020. The monitoring device 9020 may include a
controller 9022. The controller 9022 may include a response circuit
9010, a signal evaluation circuit 9008 and a data acquisition
circuit 9024. The signal evaluation circuit 9008 may include a peak
detection circuit 9012 and optionally a phase detection circuit
9016 and/or a bandpass filter circuit 9018. The data acquisition
circuit 9024 may include one or more input ports 9028. The one or
more external sensors 9026 may be directly connected to the one or
more input ports 9028 on the data acquisition circuit 9024 of the
controller 9022 or may be accessed by the data acquisition circuit
9004 wirelessly, such as by a reader, interrogator, or other
wireless connection, such as over a short-distance wireless
protocol. In embodiments as shown in FIG. 46, a data acquisition
circuit 9024 may further comprise a wireless communication circuit
9030. The data acquisition circuit 9024 may use the wireless
communication circuit 9030 to access detection values corresponding
to the one or more external sensors 9026 wirelessly or via a
separate source or some combination of these methods.
[0493] In embodiments as illustrated in FIG. 47, the data
acquisition circuit 9036 may further comprise a multiplexer circuit
9038 as described elsewhere herein. Outputs from the multiplexer
circuit 9038 may be utilized by the signal evaluation circuit 9008.
The response circuit 9010 may have the ability to turn on and off
portions of the multiplexor circuit 9038. The response circuit 9010
may have the ability to control the control channels of the
multiplexor circuit 9038
[0494] The response circuit 9010 may evaluate the results of the
signal evaluation circuit 9008 and, based on certain criteria,
initiate an action. The criteria may include a predetermined peak
value for a detection value from a specific sensor, a cumulative
value of a sensor's corresponding detection value over time, a
change in peak value, a rate of change in a peak value, and/or an
accumulated value (e.g., a time spent above/below a threshold
value, a weighted time spent above/below one or more threshold
values, and/or an area of the detected value above/below one or
more threshold values). The criteria may comprise combinations of
data from different sensors such as relative values, relative
changes in value, relative rates of change in value, relative
values over time, and the like. The relative criteria may change
with other data or information such as process stage, type of
product being processed, type of equipment, ambient temperature and
humidity, external vibrations from other equipment, and the like.
The relative criteria may be reflected in one or more calculated
statistics or metrics (including ones generated by further
calculations on multiple criteria or statistics), which in turn may
be used for processing (such as an on-board a data collector or by
an external system), such as to be provided as an input to one or
more of the machine learning capabilities described in this
disclosure, to a control system (which may be on-board a data
collector or remote, such as to control selection of data inputs,
multiplexing of sensor data, storage, or the like), or as a data
element that is an input to another system, such as a data stream
or data package that may be available to a data marketplace, a
SCADA system, a remote control system, a maintenance system, an
analytic system, or other system.
[0495] Certain embodiments are described herein as detected values
exceeding thresholds or predetermined values, but detected values
may also fall below thresholds or predetermined values--for example
where an amount of change in the detected value is expected to
occur, but detected values indicate that the change may not have
occurred. For example, and without limitation, vibrational data may
indicate system agitation levels, properly operating equipment, or
the like, and vibrational data below amplitude and/or frequency
thresholds may be an indication of a process that is not operating
according to expectations. For example, in a process involving a
blender, a mixer, an agitator or the like, the absence of vibration
may indicate that a blade, fin, vane or other working element is
unable to move adequately, such as, for example, as a result of a
working material being excessively viscous or as a result of a
problem in gears (e.g., stripped gears, seizing in gears, or the
like (a clutch, or the like). Except where the context clearly
indicates otherwise, any description herein describing a
determination of a value above a threshold and/or exceeding a
predetermined or expected value is understood to include
determination of a value below a threshold and/or falling below a
predetermined or expected value.
[0496] The predetermined acceptable range may be based on
anticipated system response or vibration based on the equipment
geometry and control scheme such as number of bearings, relative
rotational speed, influx of power to the system at a certain
frequency, and the like. The predetermined acceptable range may
also be based on long term analysis of detection values across a
plurality of similar equipment and components and correlation of
data with equipment failure.
[0497] In embodiments, the response circuit 9010 may issue an alert
based on one or more of the criteria discussed above. In an
illustrative example, an increase in peak temperature beyond a
predetermined value may indicate a hot bearing that is starting to
fail. In embodiments, the relative criteria for an alarm may change
with other data or information such as process stage, type of
product being processed on equipment, ambient temperature and
humidity, external vibrations from other equipment and the like. In
an illustrative and non-limiting example, the response circuit 9010
may initiate an alert if an amplitude, such as a vibrational
amplitude and/or frequency, exceeds a predetermined maximum value,
if there is a change or rate of change that exceeds a predetermined
acceptable range, and/or if an accumulated value based on such
amplitude and/or frequency exceeds a threshold.
[0498] In embodiments, the response circuit 9010 may cause the data
acquisition circuit 9004 to enable or disable the processing of
detection values corresponding to certain sensors based on one or
more of the criteria discussed above. This may include switching to
sensors having different response rates, sensitivity, ranges, and
the like; accessing new sensors or types of sensors, accessing data
from multiple sensors, and the like. Switching may be based on a
detected peak value for the sensor being switched or based on the
peak value of another sensor. Switching may be undertaken based on
a model, a set of rules, or the like. In embodiments, switching may
be under control of a machine learning system, such that switching
is controlled based on one or more metrics of success, combined
with input data, over a set of trials, which may occur under
supervision of a human supervisor or under control of an automated
system. Switching may involve switching from one input port to
another (such as to switch from one sensor to another). Switching
may involve altering the multiplexing of data, such as combining
different streams under different circumstances. Switching may
involve activating a system to obtain additional data, such as
moving a mobile system (such as a robotic or drone system), to a
location where different or additional data is available (such as
positioning an image sensor for a different view or positioning a
sonar sensor for a different direction of collection) or to a
location where different sensors can be accessed (such as moving a
collector to connect up to a sensor that is disposed at a location
in an environment by a wired or wireless connection). This
switching may be implemented by changing the control signals for a
multiplexor circuit 9038 and/or by turning on or off certain input
sections of the multiplexor circuit 9038.
[0499] In embodiments, the response circuit 9010 may adjust a
sensor scaling value using the detected peak as a reference
voltage. The response circuit 9010 may adjust a sensor sampling
rate such that the peak value is captured.
[0500] The response circuit 9010 may identify sensor overload. In
embodiments, the response circuit 9010 may make recommendations for
the replacement of certain sensors in the future with sensors
having different response rates, sensitivity, ranges, and the like.
The response circuit 9010 may recommend design alterations for
future embodiments of the component, the piece of equipment, the
operating conditions, the process, and the like.
[0501] In embodiments, the response circuit 9010 may recommend
maintenance at an upcoming process stop or initiate a maintenance
call where the maintenance may include the replacement of the
sensor with the same or an alternate type of sensor having a
different response rate, sensitivity, range and the like. In
embodiments, the response circuit 9010 may implement or recommend
process changes--for example, to lower the utilization of a
component that is near a maintenance interval, operating
off-nominally, or failed for purpose but still at least partially
operational, to change the operating speed of a component (such as
to put it in a lower-demand mode), to initiate amelioration of an
issue (such as to signal for additional lubrication of a roller
bearing set, or to signal for an alignment process for a system
that is out of balance), and the like.
[0502] In embodiments, as shown in FIG. 48, the data monitoring
device 9040 may include sensors 9006 and a controller 9042 which
may include a data acquisition circuit 9004, and a signal
evaluation circuit 9008. The signal evaluation circuit 9008 may
include a peak detection circuit 9012 and, optionally, a phased
detection circuit 9016 and/or a bandpass filter circuit 9018. The
controller 9042 may further include a data storage circuit 9044,
memory, and the like. The controller 9042 may further include a
response circuit 9010. The signal evaluation circuit 9008 may
periodically store certain detection values in the data storage
circuit 9044 to enable the tracking of component performance over
time.
[0503] In embodiments, based on relevant criteria as described
elsewhere herein, operating conditions and/or failure modes which
may occur as sensor values approach one or more criteria, the
signal evaluation circuit 9008 may store data in the data storage
circuit 9044 based on the fit of data relative to one or more
criteria, such as those described throughout this disclosure. Based
on one sensor input meeting or approaching specified criteria or
range, the signal evaluation circuit 9008 may store additional data
such as RPMs, component loads, temperatures, pressures, vibrations
or other sensor data of the types described throughout this
disclosure in the data storage circuit 9068. The signal evaluation
circuit 9008 may store data at a higher data rate for greater
granularity in future processing, the ability to reprocess at
different sampling rates, and/or to enable diagnosing or
post-processing of system information where operational data of
interest is flagged, and the like.
[0504] In embodiments, the signal evaluation circuit 9008 may store
new peaks that indicate changes in overall scaling over a long
duration (e.g., scaling a data stream based on historical peaks
over months of analysis). The signal evaluation circuit 9008 may
store data when historical peak values are approached (e.g., as
temperatures, pressures, vibrations, velocities, accelerations and
the like approach historical peaks).
[0505] In embodiments as shown in FIGS. 49 and 50 and 51 and 52, a
data monitoring system 9046 may include at least one data
monitoring device 9048. At least one data monitoring device 9048
may include sensors 9006 and a controller 9050 comprising a data
acquisition circuit 9004, a signal evaluation circuit 9008, a data
storage circuit 9044, and a communication circuit 9052 to allow
data and analysis to be transmitted to a monitoring application
9056 on a remote server 9054. The signal evaluation circuit 9008
may include at least one of a peak detection circuit 9012. The
signal evaluation circuit 9008 may periodically share data with the
communication circuit 9052 for transmittal to the remote server
9054 to enable the tracking of component and equipment performance
over time and under varying conditions by a monitoring application
9056. Because relevant operating conditions and/or failure modes
may occur as sensor values approach one or more criteria as
described elsewhere herein, the signal evaluation circuit 9008 may
share data with the communication circuit 9052 for transmittal to
the remote server 9054 based on the fit of data relative to one or
more criteria. Based on one sensor input meeting or approaching
specified criteria or range, the signal evaluation circuit 9008 may
share additional data such as RPMs, component loads, temperatures,
pressures, vibrations, and the like for transmittal. The signal
evaluation circuit 9008 may share data at a higher data rate for
transmittal to enable greater granularity in processing on the
remote server.
[0506] In embodiments, as shown in FIG. 49, the communication
circuit 9052 may communicate data directly to a remote server 9054.
In embodiments, as shown in FIG. 50, the communication circuit 9052
may communicate data to an intermediate computer 9058 which may
include a processor 9060 running an operating system 9062 and a
data storage circuit 9064.
[0507] In embodiments, as illustrated in FIGS. 51 and 52, a data
collection system 9066 may have a plurality of monitoring devices
9048 collecting data on multiple components in a single piece of
equipment, collecting data on the same component across a plurality
of pieces of equipment (both the same and different types of
equipment) in the same facility as well as collecting data from
monitoring devices in multiple facilities. A monitoring application
9056 on a remote server 9054 may receive and store one or more of
detection values, timing signals or data coming from a plurality of
the various monitoring devices 9048.
[0508] In embodiments, as shown in FIG. 49, the communication
circuit 9052 may communicate data directly to a remote server 9054.
In embodiments, as shown in FIG. 50, the communication circuit 9052
may communicate data to an intermediate computer 9058 which may
include a processor 9060 running an operating system 9062 and a
data storage circuit 9064. There may be an individual intermediate
computer 9058 associated with each monitoring device 9048 or an
individual intermediate computer 9058 may be associated with a
plurality of monitoring devices 9048 where the intermediate
computer 9058 may collect data from a plurality of data monitoring
devices and send the cumulative data to the remote server 9054.
[0509] The monitoring application 9056 may select subsets of the
detection values, timing signals and data to be jointly analyzed.
Subsets for analysis may be selected based on a single type of
component or a single type of equipment in which a component is
operating. Subsets for analysis may be selected or grouped based on
common operating conditions such as size of load, operational
condition (e.g., intermittent, continuous), operating speed or
tachometer, common ambient environmental conditions such as
humidity, temperature, air or fluid particulate, and the like.
Subsets for analysis may be selected based on the effects of other
nearby equipment such as nearby machines rotating at similar
frequencies, nearby equipment producing electromagnetic fields,
nearby equipment producing heat, nearby equipment inducing movement
or vibration, nearby equipment emitting vapors, chemicals or
particulates, or other potentially interfering or intervening
effects.
[0510] The monitoring application 9056 may then analyze the
selected subset. In an illustrative example, data from a single
component may be analyzed over different time periods such as one
operating cycle, several operating cycles, a month, a year, the
life of the component or the like. Data from multiple components of
the same type may also be analyzed over different time periods.
Trends in the data such as changes in frequency or amplitude may be
correlated with failure and maintenance records associated with the
same or a related component or piece of equipment. Trends in the
data, such as changing rates of change associated with start-up or
different points in the process, may be identified. Additional data
may be introduced into the analysis such as output product quality,
output quantity (such as per unit of time), indicated success or
failure of a process, and the like. Correlation of trends and
values for different types of data may be analyzed to identify
those parameters whose short-term analysis might provide the best
prediction regarding expected performance. This information may be
transmitted back to the monitoring device to update types of data
collected and analyzed locally or to influence the design of future
monitoring devices.
[0511] In embodiments, the monitoring application 9056 may have
access to equipment specifications, equipment geometry, component
specifications, component materials, anticipated state information
for a plurality of component types, operational history, historical
detection values, component life models and the like for use
analyzing the selected subset using rule-based or model-based
analysis. In embodiments, the monitoring application 9056 may feed
a neural net with the selected subset to learn to recognize peaks
in waveform patterns by feeding a large data set sample of waveform
behavior of a given type within which peaks are designated (such as
by human analysts).
[0512] A monitoring system for data collection in an industrial
environment, the monitoring system comprising: a data acquisition
circuit structured to interpret a plurality of detection values
from a plurality of input sensors communicatively coupled to the
data acquisition circuit, each of the plurality of detection values
corresponding to at least one of the input sensors; a peak
detection circuit structured to determine at least one peak value
in response to the plurality of detection values; and a peak
response circuit structured to perform at least one operation in
response to the at least one peak value.
[0513] An example monitoring system further includes: wherein the
at least one operation is further in response to at least one of: a
change in amplitude of at least one of the plurality of detection
values; a change in frequency or relative phase of at least one of
the plurality of detection values; a rate of change in both
amplitude and relative phase of at least one of the plurality of
detection values; and a relative rate of change in amplitude and
relative phase of at least one of the plurality of detection
values` wherein the at least one operation comprises issuing an
alert; wherein the alert may be one of haptic, audible or visual;
further comprising a data storage circuit, wherein the relative
phase difference and at least one of the detection values and the
timing signal are stored wherein the at least one operation further
comprises storing additional data in the data storage circuit
wherein the storing additional data in the data storage circuit is
further in response to at least one of: a change in the relative
phase difference and a relative rate of change in the relative
phase difference wherein the data acquisition circuit further
comprises at least one multiplexer circuit whereby alternative
combinations of detection values may be selected based on at least
one of user input and a selected operating parameter for a machine,
wherein each of the plurality of detection values corresponds to at
least one of the input sensors wherein the at least one operation
comprises enabling or disabling one or more portions of the
multiplexer circuit, or altering the multiplexer control lines
wherein the data acquisition circuit comprises at least two
multiplexer circuits and the at least one operation comprises
changing connections between the at least two multiplexer
circuits.
[0514] A monitoring system for data collection in an industrial
environment, the monitoring system structure to receive input
corresponding to a plurality of sensors, includes a data
acquisition circuit structured to interpret a plurality of
detection values, each of the plurality of detection values
corresponding to at least one of the input sensors; a peak
detection circuit structured to determine at least one peak value
in response to the plurality of detection values; and a peak
response circuit structured to perform at least one operation in
response to the at least one peak value.
[0515] An example monitoring system further includes: wherein the
at least one operation is further in response to at least one of: a
change in amplitude of at least one of the plurality of detection
values; a change in frequency or relative phase of at least one of
the plurality of detection values; a rate of change in both
amplitude and relative phase of at least one of the plurality of
detection values; and a relative rate of change in amplitude and
relative phase of at least one of the plurality of detection values
wherein the at least one operation comprises issuing an alert
wherein the alert may be one of haptic, audible or visual further
comprising a data storage circuit, wherein the relative phase
difference and at least one of the detection values and the timing
signal are stored wherein the at least one operation further
comprises storing additional data in the data storage circuit
wherein the storing additional data in the data storage circuit is
further in response to at least one of: a change in the relative
phase difference and a relative rate of change in the relative
phase difference wherein the data acquisition circuit further
comprises at least one multiplexer circuit whereby alternative
combinations of detection values may be selected based on at least
one of user input and a selected operating parameter for a machine,
wherein each of the plurality of detection values corresponds to at
least one of the input sensors wherein the at least one operation
comprises enabling or disabling one or more portions of the
multiplexer circuit, or altering the multiplexer control lines
wherein the data acquisition circuit comprises at least two
multiplexer circuits and the at least one operation comprises
changing connections between the at least two multiplexer
circuits.
[0516] An example system for data collection, processing, and
utilization of signals in an industrial environment includes: a
plurality of monitoring devices, each monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of
detection values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of
detection values corresponding to at least one of the input
sensors; a peak detection circuit structured to determine at least
one peak value in response to the plurality of detection values; a
peak response circuit structured to select at least one detection
value in response to the at least one peak value; a communication
circuit structured to communicate the at least one selected
detection value to a remote server; and a monitoring application on
the remote server structured to: receive the at least one selected
detection value; jointly analyze received detection values from a
subset of the plurality of monitoring devices; and recommend an
action.
[0517] An example system further includes: the system further
structured to subset detection values based on one of anticipated
life of a component associated with detection values, type of the
equipment associated with detection values, and operational
conditions under which detection values were measured; wherein the
analysis of the subset of detection values comprises feeding a
neural net with the subset of detection values and supplemental
information to learn to recognize various operating states, health
states, life expectancies and fault states utilizing deep learning
techniques; wherein the supplemental information comprises one of
component specification, component performance, equipment
specification, equipment performance, maintenance records, repair
records and an anticipated state model wherein the at least one
operation is further in response to at least one of: a change in
amplitude of at least one of the plurality of detection values; a
change in frequency or relative phase of at least one of the
plurality of detection values; a rate of change in both amplitude
and relative phase of at least one the plurality of detection
values; and a relative rate of change in amplitude and relative
phase of at least one the plurality of detection values wherein the
at least one operation comprises issuing an alert wherein the alert
may be one of haptic, audible and visual further comprising a data
storage circuit, wherein the relative phase difference and at least
one of the detection values and the timing signal are stored
wherein the at least one operation further comprises storing
additional data in the data storage circuit wherein the storing
additional data in the data storage circuit is further in response
to at least one of: a change in the relative phase difference and a
relative rate of change in the relative phase difference wherein
the data acquisition circuit further comprises at least one
multiplexer circuit whereby alternative combinations of detection
values may be selected based on at least one of user input and a
selected operating parameter for a machine, wherein each of the
plurality of detection values corresponds to at least one of the
input sensors wherein the at least one operation comprises enabling
or disabling one or more portions of the multiplexer circuit, or
altering the multiplexer control lines and/or wherein the data
acquisition circuit comprises at least two multiplexer circuits and
the at least one operation comprises changing connections between
the at least two multiplexer circuits.
[0518] An example motor monitoring system, includes: a data
acquisition circuit structured to interpret a plurality of
detection values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of
detection values corresponding to at least one of the input
sensors; a data storage circuit structured to store specifications,
system geometry, and anticipated state information for the motor
and motor components, store historical motor performance and buffer
the plurality of detection values for a predetermined length of
time; a peak detection circuit structured to determine a plurality
of peak values comprising at least a temperature peak value, a
speed peak value and a vibration peak value in response to the
plurality of detection values and analyze the peak values relative
to buffered detection values, specifications and anticipated state
information resulting in a motor performance parameter; and a peak
response circuit structured to perform at least one operation in
response to one of a peak value and a motor system performance
parameter.
[0519] An example system for estimating a vehicle steering system
performance parameter, the device includes: a data acquisition
circuit structured to interpret a plurality of detection values
from a plurality of input sensors communicatively coupled to the
data acquisition circuit, each of the plurality of detection values
corresponding to at least one of the input sensors; a data storage
circuit structured to store specifications, system geometry, and
anticipated state information for the vehicle steering system, the
rack, the pinion, and the steering column, store historical
steering system performance and buffer the plurality of detection
values for a predetermined length of time; a peak detection circuit
structured to determine a plurality of peak values comprising at
least a temperature peak value, a speed peak value and a vibration
peak value in response to the plurality of detection values and
analyze the peak values relative to buffered detection values,
specifications and anticipated state information resulting in a
vehicle steering system performance parameter; and a peak response
circuit structured to perform at least one operation in response to
one of a peak value and a vehicle steering system performance
parameter.
[0520] An example system for estimating a pump performance
parameter, includes: a data acquisition circuit structured to
interpret a plurality of detection values from a plurality of input
sensors communicatively coupled to the data acquisition circuit,
each of the plurality of detection values corresponding to at least
one of the input sensors; a data storage circuit structured to
store specifications, system geometry, and anticipated state
information for the pump and pump components associated with the
detection values, store historical pump performance and buffer the
plurality of detection values for a predetermined length of time; a
peak detection circuit structured to determine a plurality of peak
values comprising at least a temperature peak value, a speed peak
value and a vibration peak value in response to the plurality of
detection values and analyze the peak values relative to buffered
detection values, specifications and anticipated state information
resulting in a pump performance parameter; and a peak response
circuit structured to perform at least one operation in response to
one of a peak value and a pump performance parameter. In certain
further embodiments, the example system includes wherein the pump
is a water pump in a car and wherein the pump is a mineral
pump.
[0521] An example system for estimating a drill performance
parameter for a drilling machine, includes a data acquisition
circuit structured to interpret a plurality of detection values
from a plurality of input sensors communicatively coupled to the
data acquisition circuit, each of the plurality of detection values
corresponding to at least one of the input sensors; a data storage
circuit structured to store specifications, system geometry, and
anticipated state information for the drill and drill components
associated with the detection values, store historical drill
performance and buffer the plurality of detection values for a
predetermined length of time; a peak detection circuit structured
to determine a plurality of peak values comprising at least a
temperature peak value, a speed peak value and a vibration peak
value in response to the plurality of detection values and analyze
the peak values relative to buffered detection values,
specifications and anticipated state information resulting in a
drill performance parameter; and a peak response circuit structured
to perform at least one operation in response to one of a peak
value and a drill performance parameter. An example system further
includes wherein the drilling machine is one of an oil drilling
machine and a gas drilling machine.
[0522] An example system for estimating a conveyor health
parameter, the system includes: a data acquisition circuit
structured to interpret a plurality of detection values from a
plurality of input sensors communicatively coupled to the data
acquisition circuit, each of the plurality of detection values
corresponding to at least one of the input sensors; a data storage
circuit structured to store specifications, system geometry, and
anticipated state information for a conveyor and conveyor
components associated with the detection values, store historical
conveyor performance and buffer the plurality of detection values
for a predetermined length of time; a peak detection circuit
structured to determine a plurality of peak values comprising at
least a temperature peak value, a speed peak value and a vibration
peak value in response to the plurality of detection values and
analyze the peak values relative to buffered detection values,
specifications and anticipated state information resulting in a
conveyor performance parameter; and a peak response circuit
structured to perform at least one operation in response to one of
a peak value and a conveyor performance parameter.
[0523] An example system for estimating an agitator health
parameter, the system includes: a data acquisition circuit
structured to interpret a plurality of detection values from a
plurality of input sensors communicatively coupled to the data
acquisition circuit, each of the plurality of detection values
corresponding to at least one of the input sensors; a data storage
circuit structured to store specifications, system geometry, and
anticipated state information for an agitator and agitator
components associated with the detection values, store historical
agitator performance and buffer the plurality of detection values
for a predetermined length of time; a peak detection circuit
structured to determine a plurality of peak values comprising at
least a temperature peak value, a speed peak value and a vibration
peak value in response to the plurality of detection values and
analyze the peak values relative to buffered detection values,
specifications and anticipated state information resulting in an
agitator performance parameter; and a peak response circuit
structured to perform at least one operation in response to one of
a peak value and an agitator performance parameter. In certain
embodiments, a system further includes where the agitator is one of
a rotating tank mixer, a large tank mixer, a portable tank mixer, a
tote tank mixer, a drum mixer, a mounted mixer and a propeller
mixer.
[0524] An example system for estimating a compressor health
parameter, the system includes: a data acquisition circuit
structured to interpret a plurality of detection values from a
plurality of input sensors communicatively coupled to the data
acquisition circuit, each of the plurality of detection values
corresponding to at least one of the input sensors; a data storage
circuit structured to store specifications, system geometry, and
anticipated state information for a compressor and compressor
components associated with the detection values, store historical
compressor performance and buffer the plurality of detection values
for a predetermined length of time; a peak detection circuit
structured to determine a plurality of peak values comprising at
least a temperature peak value, a speed peak value and a vibration
peak value in response to the plurality of detection values and
analyze the peak values relative to buffered detection values,
specifications and anticipated state information resulting in a
compressor performance parameter; and a peak response circuit
structured to perform at least one operation in response to one of
a peak value and a compressor performance parameter.
[0525] An example system for estimating an air conditioner health
parameter, the system includes: a data acquisition circuit
structured to interpret a plurality of detection values from a
plurality of input sensors communicatively coupled to the data
acquisition circuit, each of the plurality of detection values
corresponding to at least one of the input sensors; a data storage
circuit structured to store specifications, system geometry, and
anticipated state information for an air conditioner and air
conditioner components associated with the detection values, store
historical air conditioner performance and buffer the plurality of
detection values for a predetermined length of time; a peak
detection circuit structured to determine a plurality of peak
values comprising at least a temperature peak value, a speed peak
value, a pressure value and a vibration peak value in response to
the plurality of detection values and analyze the peak values
relative to buffered detection values, specifications and
anticipated state information resulting in an air conditioner
performance parameter; and a peak response circuit structured to
perform at least one operation in response to one of a peak value
and an air conditioner performance parameter.
[0526] An example system for estimating a centrifuge health
parameter, the system includes: a data acquisition circuit
structured to interpret a plurality of detection values from a
plurality of input sensors communicatively coupled to the data
acquisition circuit, each of the plurality of detection values
corresponding to at least one of the input sensors; a data storage
circuit structured to store specifications, system geometry, and
anticipated state information for a centrifuge and centrifuge
components associated with the detection values, store historical
centrifuge performance and buffer the plurality of detection values
for a predetermined length of time; a peak detection circuit
structured to determine a plurality of peak values comprising at
least a temperature peak value, a speed peak value and a vibration
peak value in response to the plurality of detection values and
analyze the peak values relative to buffered detection values,
specifications and anticipated state information resulting in a
centrifuge performance parameter; and a peak response circuit
structured to perform at least one operation in response to one of
a peak value and a centrifuge performance parameter.
[0527] Bearings are used throughout many different types of
equipment and applications. Bearings may be present in or
supporting shafts, motors, rotors, stators, housings, frames,
suspension systems and components, gears, gear sets of various
types, other bearings, and other elements. Bearings may be used as
support for high speed vehicles such as maglev trains. Bearings are
used to support rotating shafts for engines, motors, generators,
fans, compressors, turbines and the like. Giant roller bearings may
be used to support buildings and physical infrastructure. Different
types of bearings may be used to support conventional, planetary
and other types of gears. Bearings may be used to support
transmissions and gear boxes such as roller thrust bearings, for
example. Bearings may be used to support wheels, wheel hubs and
other rolling parts using tapered roller bearings.
[0528] There are many different types of bearings such as roller
bearings, needle bearings, sleeve bearings, ball bearings, radial
bearings, thrust load bearings including ball thrust bearings used
in low speed applications and roller thrust bearings, taper
bearings and tapered roller bearings, specialized bearings,
magnetic bearings, giant roller bearings, jewel bearings (e.g.,
Sapphire), fluid bearings, flexure bearings to support bending
element loads, and the like. References to bearings throughout this
disclosure is intended to include, but not be limited by, the terms
listed above.
[0529] In embodiments, information about the health or other status
or state information of or regarding a bearing in a piece of
industrial equipment or in an industrial process may be obtained by
monitoring the condition of various components of the industrial
equipment or industrial process. Monitoring may include monitoring
the amplitude and/or frequency and/or phase of a sensor signal
measuring attributes such as temperature, humidity, acceleration,
displacement and the like.
[0530] An embodiment of a data monitoring device 9200 is shown in
FIG. 53 and may include a plurality of sensors 9206 communicatively
coupled to a controller 9202. The controller 9202 may include a
data acquisition circuit 9204, a data storage circuit 9216, a
signal evaluation circuit 9208 and, optionally, a response circuit
9210. The signal evaluation circuit 9208 may comprise a frequency
transformation circuit 9212 and a frequency evaluation circuit
9214.
[0531] The plurality of sensors 9206 may be wired to ports 9226
(reference FIG. 54) on the data acquisition circuit 9204. The
plurality of sensors 9206 may be wirelessly connected to the data
acquisition circuit 9204. The data acquisition circuit 9204 may be
able to access detection values corresponding to the output of at
least one of the plurality of sensors 9206 where the sensors 9206
may be capturing data on different operational aspects of a bearing
or piece of equipment or infrastructure.
[0532] The selection of the plurality of sensors 9206 for a data
monitoring device 9200 designed for a specific bearing or piece of
equipment may depend on a variety of considerations such as
accessibility for installing new sensors, incorporation of sensors
in the initial design, anticipated operational and failure
conditions, reliability of the sensors, and the like. The impact of
failure may drive the extent to which a bearing or piece of
equipment is monitored with more sensors and/or higher capability
sensors being dedicated to systems where unexpected or undetected
bearing failure would be costly or have severe consequences.
[0533] The signal evaluation circuit 9208 may process the detection
values to obtain information about a bearing being monitored. The
frequency transformation circuit 9212 may transform one or more
time-based detection values to frequency information. The
transformation may be accomplished using techniques such as a
digital Fast Fourier transform ("FFT"), Laplace transform,
Z-transform, wavelet transform, other frequency domain transform,
or other digital or analog signal analysis techniques, including,
without limitation, complex analysis, including complex phase
evolution analysis.
[0534] The frequency evaluation circuit 9214 (or frequency analysis
circuit) may be structured to detect signals at frequencies of
interest. Frequencies of interest may include frequencies higher
than the frequency at which the equipment rotates (as measured by a
tachometer, for instance), various harmonics and/or resonant
frequencies associated with the equipment design and operating
conditions such as multiples of shaft rotation velocities or other
rotating components for the equipment that is borne by the
bearings. Changes in energy at frequencies close to the operating
frequency may be an indicator of balance/imbalance in the system.
Changes in energy at frequencies on the order of twice the
operating frequency may be indicative of a system misalignment--for
example, on the coupling, or a looseness in the system, (e.g.,
rattling at harmonics of the operating frequency). Changes in
energy at frequencies close to three or four times the operating
frequency, corresponding to the number of bolts on a coupling, may
indicate wear of on one of the couplings. Changes in energy at
frequencies of four, five, or more times the operating frequency
may relate back to something that has a corresponding number of
elements, such as if there are energy peaks or activity around five
times the operating frequency there may be wear or an imbalance in
a five-vane pump or the like.
[0535] In an illustrative and non-limiting example, in the analysis
of roller bearings, frequencies of interest may include ball spin
frequencies, cage spin frequencies, inner race frequency (as
bearings often sit on a race inside a cage), outer race frequency
and the like. Bearings that are damaged or beginning to fail may
show humps of energy at the frequencies mentioned above and
elsewhere in this disclosure. The energy at these frequencies may
increase over time as the bearings wear more and become more
damaged due to more variations in rotational acceleration and
pings.
[0536] In an illustrative and non-limiting example, bad bearings
may show humps of energy and the intensity of high frequency
measurements may start to grow over time as bearings wear and
become imperfect (greater acceleration and pings may show up in
high frequency measurement domains) Those measurements may be
indicators of air gaps in the bearing system. As bearings begin to
wear, harder hits may cause the energy signal to move to higher
frequencies.
[0537] In embodiments, the signal evaluation circuit 9208 may also
include one or more of a phase detection circuit, a phase lock loop
circuit, a bandpass filter circuit, a peak detection circuit, and
the like.
[0538] In embodiments, the signal evaluation circuit 9208 may
include a transitory signal analysis circuit. Transient signals may
cause small amplitude vibrations. However, the challenge in bearing
analysis is that you may receive a signal associated with a single
or non-periodic impact and an exponential decay. Thus, the
oscillation of the bearing may not be represented by a single sine
wave, but rather by a spectrum of many high frequency sine waves.
For example, a signal from a failing bearing may only be seen, in a
time-based signal, as a low amplitude spike for a short amount of
time. A signal from a failing bearing may be lower in amplitude
than a signal associated with an imbalance even though the
consequences of a failed bearing may be more significant. It is
important to be able to identify these signals. This type of low
amplitude, transient signal may be best analyzed using transient
analysis rather than a conventional frequency transformation, such
as an FFT, which would treat the signal like a low frequency sine
wave. A higher resolution data stream may also provide additional
data for the detection of transitory signals in low speed
operations. The identification of transitory signals may enable the
identification of defects in a piece of equipment or component
operating at low RPMs.
[0539] In embodiments, the transitory signal analysis circuit for
bearing analysis may include envelope modulation analysis and other
transitory signal analysis techniques. The signal evaluation
circuit 9208 may store long stream of detection values to the data
storage circuit 9216. The transitory signal analysis circuit may
use envelope analysis techniques on those long streams of detection
values to identify transient effects (such as impacts) which may
not be identified by conventional sine wave analysis (such as
FFTs).
[0540] The signal evaluation circuit 9208 may utilize transitory
signal analysis models optimized for the type of component being
measured such as bearings, gears, variable speed machinery and the
like. In an illustrative and non-limiting example, a gear may
resonate close to its average rotational speed. In an illustrative
and non-limiting example, a bearing may resonate close to the
bearing rotation frequency and produce a ringing in amplitude
around that frequency. For example, if the shaft inner race is
wearing there may be chatter between the inner race and the shaft
resulting in amplitude modulation to the left and right of the
bearing frequency. The amplitude modulation may demonstrate its own
sine wave characteristics with its own side bands. Various signal
processing techniques may be used to eliminate the sinusoidal
component, resulting in a modulation envelope for analysis.
[0541] The signal evaluation circuit 9208 may be optimized for
variable speed machinery. Historically, variable speed machinery
was expensive to make, and it was common to use DC motors and
variable sheaves, such that flow could be controlled using vanes.
Variable speed motors became more common with solid-state drive
advances ("SCR devices"). The base operating frequency of equipment
may be varied from the 50-60 Hz provided by standard utility
companies and either and slowed down or sped up to run the
equipment at different speeds depending on the application. The
ability to run the equipment at varying speeds may result in energy
savings. However, depending on the equipment geometry, there may be
some speeds which create vibrations at resonant frequencies,
reducing the life of the components. Variable speed motors may also
emit electricity into bearings which may damage the bearings.
[0542] In embodiments, the analysis of long data streams for
envelope modulation analysis and other transitory signal analysis
techniques as described herein may be useful in identifying these
frequencies such that control schemes for the equipment may be
designed to avoid those speeds which result in unacceptable
vibrations and/or damage to the bearings.
[0543] In an illustrative and non-limiting example, heating,
ventilation and air conditioning ("HVAC") systems may be assembled
on site using variable speed motors, fans, belts, compressors and
the like where the operating speeds are not constant, and their
relative relationships are unknown. In an illustrative and
non-limiting example, variable speed motors may be used in fan
pumps for building air circulation. Variable speed motors may be
used to vary the speed of conveyors--for example, in manufacturing
assembly lines or steel mills Variable speed motors may be used for
fans in a pharmaceutical process, such as where it may be critical
to avoid vibration.
[0544] In an illustrative and non-limiting example, sleeve bearings
may be analyzed for defects. Sleeve bearings typically have an oil
system. If the oil flow stops or the oil becomes severely
contaminated, failure can occur very quickly. Therefore, a fluid
particulate sensor or fluid pressure sensors may be an important
source of detection values.
[0545] In an illustrative and non-limiting example, fan integrity
may be evaluated by measuring air pulsations related to blade pass
frequencies. For example, if a fan has 12 blades, 12 air pulsations
may be measured. Variations in the amplitude of the pulsations
associated with the different blades may be indicative of changes
in a fan blade. Changes in frequencies associated with the air
pulsations may be indicative of bearing problems.
[0546] In an illustrative and non-limiting example, compressors
used in in the gas and oil field or in gas handling equipment on an
assembly line may be evaluated by measuring the periodic increases
in energy/pressure in the storage vessel as gas is pumped into the
vessel. Periodic variations in the amplitude of the energy
increases may be associated with piston wear or damage to a portion
of a rotary screw. Phase evaluation of the energy signal relative
to timing signals may be helpful in identifying which piston or
portion of the rotary screw has damage. Changes in frequencies
associated with the energy pulsations may be indicative of bearing
problems.
[0547] In an illustrative and non-limiting example, cavitation/air
pockets in pumps may create shuttering in the pump housing and the
output flow which may be identified with the frequency
transformation and frequency analysis techniques described above
and elsewhere herein.
[0548] In an illustrative and non-limiting example, the frequency
transformation and frequency analysis techniques described above
and elsewhere herein may assist in the identification of problems
in components of building HVAC systems such as big fans. If the
dampers of the system are set poorly it may result in ducts pulsing
or vibrating as air is pushed through the system. Monitoring of
vibration sensors on the ducts may assist in the balancing of the
system. If there are defects in the blades of the big fan this may
also result in uneven air flow and resulting pulsation in the
buildings ductwork.
[0549] In an illustrative and non-limiting example, detection
values from acoustical sensors located close to the bearings may
assist in the identification of issues in the engagement between
gears or bad bearings. Based on a knowledge of gear ratios, such as
the "in" and "out" gear ratios, for a system and measurements of
the input and output rotational speed, detection values may be
evaluated for energy occurring at those ratios, which in turn may
be used to identify bad bearings. This could be done with simple
off the shelf motors rather than requiring extensive retrofitting
of the motor with sensors.
[0550] Based on the output of its various components, the signal
evaluation circuit 9208 may make a bearing life prediction,
identify a bearing health parameter, identify a bearing performance
parameter, determine a bearing health parameter (e.g., fault
conditions), and the like. The signal evaluation circuit 9208 may
identify wear on a bearing, identify the presence of foreign matter
(e.g., particulates) in the bearings, identify air gaps or a loss
of fluid in oil/fluid coated bearings, identify a loss of
lubrication in a set of bearings, identify a loss of power for
magnetic bearings and the like, identify strain/stress of flexure
bearings, and the like. The signal evaluation circuit 9208 may
identify optimal operation parameters for a piece of equipment to
extend bearing life. The signal evaluation circuit 9208 may
identify behavior (resonant wobble) at a selected operational
frequency (e.g., shaft rotation rate).
[0551] The signal evaluation circuit 9208 may communicate with the
data storage circuit 9216 to access equipment specifications,
equipment geometry, bearing specifications, bearing materials,
anticipated state information for a plurality of bearing types,
operational history, historical detection values, and the like for
use in assessing the output of its various components. The signal
evaluation circuit 9208 may buffer a subset of the plurality of
detection values, intermediate data such as time-based detection
values transformed to frequency information, filtered detection
values, identified frequencies of interest, and the like for a
predetermined length of time. The signal evaluation circuit 9208
may periodically store certain detection values in the data storage
circuit 9216 to enable the tracking of component performance over
time. In embodiments, based on relevant operating conditions and/or
failure modes that may occur as detection values approach one or
more criteria, the signal evaluation circuit 9208 may store data in
the data storage circuit 9216 based on the fit of data relative to
one or more criteria, such as those described throughout this
disclosure. Based on one sensor input meeting or approaching
specified criteria or range, the signal evaluation circuit 9208 may
store additional data such as RPMs, component loads, temperatures,
pressures, vibrations or other sensor data of the types described
throughout this disclosure in the data storage circuit 9216. The
signal evaluation circuit 9208 may store data at a higher data rate
for greater granularity in future processing, the ability to
reprocess at different sampling rates, and/or to enable diagnosing
or post-processing of system information where operational data of
interest is flagged, and the like.
[0552] Depending on the type of equipment, the component being
measured, the environment in which the equipment is operating and
the like, sensors 9206 may comprise one or more of, without
limitation, a vibration sensor, an optical vibration sensor, a
thermometer, a hygrometer, a voltage sensor, a current sensor, an
accelerometer, a velocity detector, a light or electromagnetic
sensor (e.g., determining temperature, composition and/or spectral
analysis, and/or object position or movement), an image sensor, a
structured light sensor, a laser-based image sensor, an infrared
sensor, an acoustic wave sensor, a heat flux sensor, a displacement
sensor, a turbidity meter, a viscosity meter, a load sensor, a
tri-axial vibration sensor, an accelerometer, a tachometer, a fluid
pressure meter, an air flow meter, a horsepower meter, a flow rate
meter, a fluid particle detector, an acoustical sensor, a pH
sensor, and the like, including, without limitation, any of the
sensors described throughout this disclosure and the documents
incorporated by reference. The sensors may typically comprise at
least a temperature sensor, a load sensor, a tri-axial sensor and a
tachometer.
[0553] The sensors 9206 may provide a stream of data over time that
has a phase component, such as relating to acceleration or
vibration, allowing for the evaluation of phase or frequency
analysis of different operational aspects of a piece of equipment
or an operating component. The sensors 9206 may provide a stream of
data that is not conventionally phase-based, such as temperature,
humidity, load, and the like. The sensors 9206 may provide a
continuous or near continuous stream of data over time, periodic
readings, event-driven readings, and/or readings according to a
selected interval or schedule.
[0554] In embodiments, as illustrated in FIG. 53, the sensors 9206
may be part of the data monitoring device 9200, referred to herein
in some cases as a data collector, which in some cases may comprise
a mobile or portable data collector. In embodiments, as illustrated
in FIGS. 54 and 55, one or more external sensors 9224, which are
not explicitly part of a monitoring device 9218 but rather are new,
previously attached to or integrated into the equipment or
component, may be opportunistically connected to or accessed by the
monitoring device 9218. The monitoring device 9218 may include a
controller 9220. The controller 9202 may include a data acquisition
circuit 9222, a data storage circuit 9216, a signal evaluation
circuit 9208 and, optionally, a response circuit 9210. The signal
evaluation circuit 9208 may comprise a frequency transformation
circuit 9212 and a frequency analysis circuit 9214. The data
acquisition circuit 9222 may include one or more input ports 9226.
The one or more external sensors 9224 may be directly connected to
the one or more input ports 9226 on the data acquisition circuit
9222 of the controller 9220 or may be accessed by the data
acquisition circuit 9222 wirelessly, such as by a reader,
interrogator, or other wireless connection, such as over a
short-distance wireless protocol. In embodiments as shown in FIG.
55, a data acquisition circuit 9222 may further comprise a wireless
communications circuit 9262. The data acquisition circuit 9222 may
use the wireless communications circuit 9262 to access detection
values corresponding to the one or more external sensors 9224
wirelessly or via a separate source or some combination of these
methods.
[0555] In embodiments, as illustrated in FIG. 56, the data
acquisition circuit 9222 may further comprise a multiplexer circuit
9236 as described elsewhere herein. Outputs from the multiplexer
circuit 9236 may be utilized by the signal evaluation circuit 9208.
The response circuit 9210 may have the ability to turn on and off
portions of the multiplexor circuit 9236. The response circuit 9210
may have the ability to control the control channels of the
multiplexor circuit 9236.
[0556] The response circuit 9210 may initiate actions based on a
bearing performance parameter, a bearing health value, a bearing
life prediction parameter, and the like. The response circuit 9210
may evaluate the results of the signal evaluation circuit 9208 and,
based on certain criteria or the output from various components of
the signal evaluation circuit 9208, initiate an action. The
criteria may include a sensor's detection values at certain
frequencies or phases relative to a timer signal where the
frequencies or phases of interest may be based on the equipment
geometry, equipment control schemes, system input, historical data,
current operating conditions, and/or an anticipated response. The
criteria may include a sensor's detection values at certain
frequencies or phases relative to detection values of a second
sensor. The criteria may include signal strength at certain
resonant frequencies/harmonics relative to detection values
associated with a system tachometer or anticipated based on
equipment geometry and operation conditions. Criteria may include a
predetermined peak value for a detection value from a specific
sensor, a cumulative value of a sensor's corresponding detection
value over time, a change in peak value, a rate of change in a peak
value, and/or an accumulated value (e.g., a time spent above/below
a threshold value, a weighted time spent above/below one or more
threshold values, and/or an area of the detected value above/below
one or more threshold values). The criteria may comprise
combinations of data from different sensors such as relative
values, relative changes in value, relative rates of change in
value, relative values over time, and the like. The relative
criteria may change with other data or information such as process
stage, type of product being processed, type of equipment, ambient
temperature and humidity, external vibrations from other equipment,
and the like. The relative criteria may be reflected in one or more
calculated statistics or metrics (including ones generated by
further calculations on multiple criteria or statistics), which in
turn may be used for processing (such as on-board a data collector
or by an external system), such as to be provided as an input to
one or more of the machine learning capabilities described in this
disclosure, to a control system (which may be on board a data
collector or remote, such as to control selection of data inputs,
multiplexing of sensor data, storage, or the like), or as a data
element that is an input to another system, such as a data stream
or data package that may be available to a data marketplace, a
SCADA system, a remote control system, a maintenance system, an
analytic system, or other system.
[0557] Certain embodiments are described herein as detected values
exceeding thresholds or predetermined values, but detected values
may also fall below thresholds or predetermined values--for
example, where an amount of change in the detected value is
expected to occur, but detected values indicate that the change may
not have occurred. For example, and without limitation, vibrational
data may indicate system agitation levels, properly operating
equipment, or the like, and vibrational data below amplitude and/or
frequency thresholds may be an indication of a process that is not
operating according to expectations. Except where the context
clearly indicates otherwise, any description herein describing a
determination of a value above a threshold and/or exceeding a
predetermined or expected value is understood to include
determination of a value below a threshold and/or falling below a
predetermined or expected value.
[0558] The predetermined acceptable range may be based on
anticipated system response or vibration based on the equipment
geometry and control scheme such as number of bearings, relative
rotational speed, influx of power to the system at a certain
frequency, and the like. The predetermined acceptable range may
also be based on long term analysis of detection values across a
plurality of similar equipment and components and correlation of
data with equipment failure.
[0559] In some embodiments, an alert may be issued based on some of
the criteria discussed above. In an illustrative example, an
increase in temperature and energy at certain frequencies may
indicate a hot bearing that is starting to fail. In embodiments,
the relative criteria for an alarm may change with other data or
information such as process stage, type of product being processed
on equipment, ambient temperature and humidity, external vibrations
from other equipment and the like. In an illustrative and
non-limiting example, the response circuit 9210 may initiate an
alert if a vibrational amplitude and/or frequency exceeds a
predetermined maximum value, if there is a change or rate of change
that exceeds a predetermined acceptable range, and/or if an
accumulated value based on vibrational amplitude and/or frequency
exceeds a threshold.
[0560] In embodiments, response circuit 9210 may cause the data
acquisition circuit 9204 to enable or disable the processing of
detection values corresponding to certain sensors based on some of
the criteria discussed above. This may include switching to sensors
having different response rates, sensitivity, ranges, and the like,
or accessing new sensors or types of sensors, and the like.
Switching may be undertaken based on a model, a set of rules, or
the like. In embodiments, switching may be under control of a
machine learning system, such that switching is controlled based on
one or more metrics of success, combined with input data, over a
set of trials, which may occur under supervision of a human
supervisor or under control of an automated system. Switching may
involve switching from one input port to another (such as to switch
from one sensor to another). Switching may involve altering the
multiplexing of data, such as combining different streams under
different circumstances. Switching may also involve activating a
system to obtain additional data, such as moving a mobile system
(such as a robotic or drone system), to a location where different
or additional data is available (such as positioning an image
sensor for a different view or positioning a sonar sensor for a
different direction of collection) or to a location where different
sensors can be accessed (such as moving a collector to connect up
to a sensor that is disposed at a location in an environment by a
wired or wireless connection). This switching may be implemented by
changing the control signals for a multiplexor circuit 9236 and/or
by turning on or off certain input sections of the multiplexor
circuit 9236. The response circuit 9210 may make recommendations
for the replacement of certain sensors in the future with sensors
having different response rates, sensitivity, ranges, and the like.
The response circuit 9210 may recommend design alterations for
future embodiments of the component, the piece of equipment, the
operating conditions, the process, and the like.
[0561] In embodiments, the response circuit 9210 may recommend
maintenance at an upcoming process stop or initiate a maintenance
call. The response circuit 9210 may recommend changes in process or
operating parameters to remotely balance the piece of equipment. In
embodiments, the response circuit 9210 may implement or recommend
process changes--for example to lower the utilization of a
component that is near a maintenance interval, operating
off-nominally, or failed for purpose but still at least partially
operational, to change the operating speed of a component (such as
to put it in a lower-demand mode), to initiate amelioration of an
issue (such as to signal for additional lubrication of a roller
bearing set, or to signal for an alignment process for a system
that is out of balance), and the like.
[0562] In embodiments as shown in FIGS. 57, 58, 59, and 60, a data
monitoring system 9240 may include at least one data monitoring
device 9250. The at least one data monitoring device 9250 may
include sensors 9206 and a controller 9242 comprising a data
acquisition circuit 9204, a signal evaluation circuit 9208, a data
storage circuit 9216, and a communications circuit 9246. The signal
evaluation circuit 9208 may include at least one of a frequency
detection circuit 9212 and a frequency analysis circuit 9214. There
may also be an optional response circuit as described above and
elsewhere herein. The signal evaluation circuit 9208 may
periodically share data with the communication circuit 9246 for
transmittal to a remote server 9244 to enable the tracking of
component and equipment performance over time and under varying
conditions by a monitoring application 9248. Because relevant
operating conditions and/or failure modes may occur as sensor
values approach one or more criteria, the signal evaluation circuit
9208 may share data with the communication circuit 9246 for
transmittal to the remote server 9244 based on the fit of data
relative to one or more criteria. Based on one sensor input meeting
or approaching specified criteria or range, the signal evaluation
circuit 9208 may share additional data such as RPMs, component
loads, temperatures, pressures, vibrations, and the like for
transmittal. The signal evaluation circuit 9208 may share data at a
higher data rate for transmittal to enable greater granularity in
processing on the remote server.
[0563] In embodiments, as shown in FIG. 57, the communications
circuit 9246 may communicate data directly to a remote server 9244.
In embodiments, as shown in FIG. 58, the communications circuit
9246 may communicate data to an intermediate computer 9252, which
may include a processor 9254 running an operating system 9256 and a
data storage circuit 9258. The intermediate computer 9252 may
collect data from a plurality of data monitoring devices and send
the cumulative data to the remote server 9244.
[0564] In embodiments, as illustrated in FIGS. 59 and 60, a data
collection system 9260 may have a plurality of monitoring devices
9250 collecting data on multiple components in a single piece of
equipment, collecting data on the same component across a plurality
of pieces of equipment, (both the same and different types of
equipment) in the same facility as well as collecting data from
monitoring devices in multiple facilities. A monitoring application
9248 on a remote server 9244 may receive and store one or more of
the following: detection values, timing signals and data coming
from a plurality of the various monitoring devices 9250. In
embodiments, as shown in FIG. 59, the communications circuit 9246
may communicate data directly to a remote server 9244. In
embodiments, as shown in FIG. 60, the communications circuit 9246
may communicate data to an intermediate computer 9252, which may
include a processor 9254 running an operating system 9256 and a
data storage circuit 9258. There may be an individual intermediate
computer 9252 associated with each monitoring device 9264 or an
individual intermediate computer 9252 may be associated with a
plurality of monitoring devices 9250 where the intermediate
computer 9252 may collect data from a plurality of data monitoring
devices and send the cumulative data to the remote server 9244.
[0565] The monitoring application 9248 may select subsets of the
detection values, timing signals and data to be jointly analyzed.
Subsets for analysis may be selected based on a bearing type,
bearing materials, or a single type of equipment in which a bearing
is operating. Subsets for analysis may be selected or grouped based
on common operating conditions or operational history such as size
of load, operational condition (e.g., intermittent, continuous),
operating speed or tachometer, common ambient environmental
conditions such as humidity, temperature, air or fluid particulate,
and the like. Subsets for analysis may be selected based on common
anticipated state information. Subsets for analysis may be selected
based on the effects of other nearby equipment such as nearby
machines rotating at similar frequencies, nearby equipment
producing electromagnetic fields, nearby equipment producing heat,
nearby equipment inducing movement or vibration, nearby equipment
emitting vapors, chemicals or particulates, or other potentially
interfering or intervening effects.
[0566] The monitoring application 9248 may analyze a selected
subset. In an illustrative example, data from a single component
may be analyzed over different time periods, such as one operating
cycle, cycle-to-cycle comparisons, trends over several operating
cycles/times such as a month, a year, the life of the component, or
the like. Data from multiple components of the same type may also
be analyzed over different time periods. Trends in the data such as
changes in frequency or amplitude may be correlated with failure
and maintenance records associated with the same component or piece
of equipment. Trends in the data such as changing rates of change
associated with start-up or different points in the process may be
identified. Additional data may be introduced into the analysis
such as output product quality, output quantity (such as per unit
of time), indicated success or failure of a process, and the like.
Correlation of trends and values for different types of data may be
analyzed to identify those parameters whose short-term analysis
might provide the best prediction regarding expected performance.
The analysis may identify model improvements to the model for
anticipated state information, recommendations around sensors to be
used, positioning of sensors and the like. The analysis may
identify additional data to collect and store. The analysis may
identify recommendations regarding needed maintenance and repair
and/or the scheduling of preventative maintenance. The analysis may
identify recommendations around purchasing replacement bearings and
the timing of the replacement of the bearings. The analysis may
result in warning regarding the dangers of catastrophic failure
conditions. This information may be transmitted back to the
monitoring device to update types of data collected and analyzed
locally or to influence the design of future monitoring
devices.
[0567] In embodiments, the monitoring application 9248 may have
access to equipment specifications, equipment geometry, bearing
specifications, bearing materials, anticipated state information
for a plurality of bearing types, operational history, historical
detection values, bearing life models and the like for use
analyzing the selected subset using rule-based or model-based
analysis. In embodiments, the monitoring application 9248 may feed
a neural net with the selected subset to learn to recognize various
operating state, health states (e.g., lifetime predictions) and
fault states utilizing deep learning techniques. In embodiments, a
hybrid of the two techniques (model-based learning and deep
learning) may be used.
[0568] In an illustrative and non-limiting example, the health of
bearings on conveyors and lifters in an assembly line, in water
pumps on industrial vehicles and in compressors in gas handling
systems, in compressors situated out in the gas and oil fields, in
factory air conditioning units and in factory mineral pumps may be
monitored using the frequency transformation and frequency analysis
techniques, data monitoring devices and data collection systems
described herein.
[0569] In an illustrative and non-limiting example, the health of
one or more of bearings, gears, blades, screws and associated
shafts, motors, rotors, stators, gears, and other components of
gear boxes, motors, pumps, vibrating conveyors, mixers,
centrifuges, drilling machines, screw drivers and refining tanks
situated in the oil and gas fields may be evaluated using the
frequency transformation and frequency analysis techniques, data
monitoring devices and data collection systems described
herein.
[0570] In an illustrative and non-limiting example, the health of
bearings and associated shafts, motors, rotors, stators, gears, and
other components of rotating tank/mixer agitators,
mechanical/rotating agitators, and propeller agitators, to promote
chemical reactions deployed in chemical and pharmaceutical
production lines may be evaluated using the frequency
transformation and frequency analysis techniques, data monitoring
devices and data collection systems described herein.
[0571] In an illustrative and non-limiting example, the health of
bearings and associated shafts, motors, rotors, stators, gears, and
other components of vehicle systems such as steering mechanisms or
engines may be evaluated using the frequency transformation and
frequency analysis techniques, data monitoring devices and data
collection systems described herein.
[0572] An example monitoring device for bearing analysis in an
industrial environment, includes: a data acquisition circuit
structured to interpret a plurality of detection values, each of
the plurality of detection values corresponding to at least one of
a plurality of input sensors communicatively coupled to the data
acquisition circuit; a data storage for storing specifications and
anticipated state information for a plurality of bearing types and
buffering the plurality of detection values for a predetermined
length of time; and a bearing analysis circuit structured to
analyze buffered detection values relative to specifications and
anticipated state information resulting in a bearing performance
parameter.
[0573] In certain further embodiments, an example monitoring device
includes one or more of: a response circuit to perform at least one
operation in response to the bearing performance parameter, wherein
the plurality of input sensors includes at least two sensors
selected from the group consisting of a temperature sensor, a load
sensor, an optical vibration sensor, an acoustic wave sensor, a
heat flux sensor, an infrared sensor, an accelerometer, a tri-axial
vibration sensor and a tachometer; wherein the at least one
operation is further in response to at least one of: a change in
amplitude of at least one of the plurality of detection values; a
change in frequency or relative phase of at least one of the
plurality of detection values; a rate of change in both amplitude
and relative phase of at least one of the plurality of detection
values; and a relative rate of change in amplitude and relative
phase of at least one of the plurality of detection values; wherein
the at least one operation comprises issuing an alert; wherein the
alert may be one of haptic, audible and visual; wherein the at
least one operation further comprises storing additional data in
the data storage circuit; wherein the storing additional data in
the data storage circuit is further in response to at least one of:
a change in the relative phase difference and a relative rate of
change in the relative phase difference.
[0574] An example monitoring device for bearing analysis in an
industrial environment, the monitoring device includes: a data
acquisition circuit structured to interpret a plurality of
detection values, each of the plurality of detection values
corresponding to at least one of a plurality of input sensors
communicatively coupled to the data acquisition circuit; a data
storage for storing specifications and anticipated state
information for a plurality of bearing types and buffering the
plurality of detection values for a predetermined length of time;
and a bearing analysis circuit structured to analyze buffered
detection values relative to specifications and anticipated state
information resulting in a bearing health value.
[0575] In certain embodiments, an example monitoring device further
includes one or more of: a response circuit to perform at least one
operation in response to the bearing health value, wherein the
plurality of input sensors includes at least two sensors selected
from the group consisting of a temperature sensor, a load sensor,
an optical vibration sensor, an acoustic wave sensor, a heat flux
sensor, an infrared sensor, an accelerometer, a tri-axial vibration
sensor and a tachometer; wherein the at least one operation is
further in response to at least one of: a change in amplitude of at
least one of the plurality of detection values; a change in
frequency or relative phase of at least one of the plurality of
detection values; a rate of change in both amplitude and relative
phase of at least one of the plurality of detection values; and a
relative rate of change in amplitude and relative phase of at least
one of the plurality of detection values; wherein the at least one
operation comprises issuing an alert; wherein the alert may be one
of haptic, audible and visual; wherein the at least one operation
further comprises storing additional data in the data storage
circuit; wherein the storing additional data in the data storage
circuit is further in response to at least one of: a change in the
relative phase difference and a relative rate of change in the
relative phase difference.
[0576] An example monitoring device for bearing analysis in an
industrial environment, includes: a data acquisition circuit
structured to interpret a plurality of detection values, each of
the plurality of detection values corresponding to at least one of
a plurality of input sensors communicatively coupled to the data
acquisition circuit; a data storage for storing specifications and
anticipated state information for a plurality of bearing types and
buffering the plurality of detection values for a predetermined
length of time; and a bearing analysis circuit structured to
analyze buffered detection values relative to specifications and
anticipated state information resulting in a bearing life
prediction parameter.
[0577] In certain embodiments, a monitoring device further includes
one or more of: a response circuit to perform at least one
operation in response to the bearing life prediction parameter,
wherein the plurality of input sensors includes at least two
sensors selected from the group consisting of a temperature sensor,
a load sensor, an optical vibration sensor, an acoustic wave
sensor, a heat flux sensor, an infrared sensor, an accelerometer, a
tri-axial vibration sensor and a tachometer; wherein the at least
one operation is further in response to at least one of: a change
in amplitude of at least one of the plurality of detection values;
a change in frequency or relative phase of at least one of the
plurality of detection values; a rate of change in both amplitude
and relative phase of at least one of the plurality of detection
values; and a relative rate of change in amplitude and relative
phase of at least one of the plurality of detection values; wherein
the at least one operation comprises issuing an alert; wherein the
alert may be one of haptic, audible and visual; wherein the at
least one operation further comprises storing additional data in
the data storage circuit; wherein the storing additional data in
the data storage circuit is further in response to at least one of:
a change in the relative phase difference and a relative rate of
change in the relative phase difference.
[0578] An example monitoring device for bearing analysis in an
industrial environment, includes: a data acquisition circuit
structured to interpret a plurality of detection values, each of
the plurality of detection values corresponding to at least one of
a plurality of input sensors communicatively coupled to the data
acquisition circuit; a data storage for storing specifications and
anticipated state information for a plurality of bearing types and
buffering the plurality of detection values for a predetermined
length of time; and a bearing analysis circuit structured to
analyze buffered detection values relative to specifications and
anticipated state information resulting in a bearing performance
parameter, wherein the data acquisition circuit comprises a
multiplexer circuit whereby alternative combinations of the
detection values may be selected based on at least one of user
input, a detected state and a selected operating parameter for a
machine.
[0579] In certain further embodiments, an example monitoring device
further includes one or more of: a response circuit to perform at
least one operation in response to the bearing performance
parameter, wherein the plurality of input sensors includes at least
two sensors selected from the group consisting of a temperature
sensor, a load sensor, an optical vibration sensor, an acoustic
wave sensor, a heat flux sensor, an infrared sensor, an
accelerometer, a tri-axial vibration sensor and a tachometer; a
change in amplitude of at least one of the plurality of detection
values; a change in frequency or relative phase of at least one of
the plurality of detection values; a rate of change in both
amplitude and relative phase of at least one of the plurality of
detection values; and a relative rate of change in amplitude and
relative phase of at least one of the plurality of detection
values; wherein the at least one operation comprises issuing an
alert; wherein the alert may be one of haptic, audible and visual;
wherein the at least one operation further comprises storing
additional data in the data storage circuit; wherein the storing
additional data in the data storage circuit is further in response
to at least one of: a change in the relative phase difference and a
relative rate of change in the relative phase difference; wherein
the at least one operation comprises enabling or disabling one or
more portions of the multiplexer circuit, or altering the
multiplexer control lines; wherein the data acquisition circuit
comprises at least two multiplexer circuits and the at least one
operation comprises changing connections between the at least two
multiplexer circuits.
[0580] An example system for data collection, processing, and
bearing analysis in an industrial environment includes: a plurality
of monitoring devices, each monitoring device comprising: a data
acquisition circuit structured to interpret a plurality of
detection values, each of the plurality of detection values
corresponding to at least one of a plurality of input sensors
communicatively coupled to the data acquisition circuit; a data
storage for storing specifications and anticipated state
information for a plurality of bearing types and buffering the
plurality of detection values for a predetermined length of time;
[0581] a bearing analysis circuit structured to analyze buffered
detection values relative to specifications and anticipated state
information resulting in a bearing life prediction; a communication
circuit structured to communicate with a remote server providing
the bearing life prediction and a portion of the buffered detection
values to the remote server; and a monitoring application on the
remote server structured to receive, store and jointly analyze a
subset of the detection values from the plurality of monitoring
devices.
[0582] In certain further embodiments, an example monitoring device
includes one or more of: a response circuit to perform at least one
operation in response to the bearing life prediction, wherein the
plurality of input sensors includes at least two sensors selected
from the group consisting of a temperature sensor, a load sensor,
an optical vibration sensor, an acoustic wave sensor, a heat flux
sensor, an infrared sensor, an accelerometer, a tri-axial vibration
sensor and a tachometer; wherein the at least one operation is
further in response to at least one of: a change in amplitude of at
least one of the plurality of detection values; a change in
frequency or relative phase of at least one of the plurality of
detection values; a rate of change in both amplitude and relative
phase of at least one of the plurality of detection values; and a
relative rate of change in amplitude and relative phase of at least
one of the plurality of detection values; wherein the at least one
operation comprises issuing an alert; wherein the alert may be one
of haptic, audible and visual; wherein the at least one operation
further comprises storing additional data in the data storage
circuit; wherein the storing additional data in the data storage
circuit is further in response to at least one of: a change in the
relative phase difference and a relative rate of change in the
relative phase difference.
[0583] An example system for data collection, processing, and
bearing analysis in an industrial environment comprising: a
plurality of monitoring devices, each comprising: a data
acquisition circuit structured to interpret a plurality of
detection values, each of the plurality of detection values
corresponding to at least one of a plurality of input sensors
communicatively coupled to the data acquisition circuit; a data
storage for storing specifications and anticipated state
information for a plurality of bearing types and buffering the
plurality of detection values for a predetermined length of time;
[0584] a bearing analysis circuit structured to analyze buffered
detection values relative to specifications and anticipated state
information resulting in a bearing performance parameter; a
communication circuit structured to communicate with a remote
server providing the life prediction and a portion of the buffered
detection values to the remote server; and a monitoring application
on the remote server structured to receive, store and jointly
analyze a subset of the detection values from the plurality of
monitoring devices.
[0585] In certain further embodiments, an example monitoring device
further includes one or more of: a response circuit to perform at
least one operation in response to the bearing performance
parameter, wherein the plurality of input sensors includes at least
two sensors selected from the group consisting of a temperature
sensor, a load sensor, an optical vibration sensor, an acoustic
wave sensor, a heat flux sensor, an infrared sensor, an
accelerometer, a tri-axial vibration sensor and a tachometer;
wherein the at least one operation is further in response to at
least one of: a change in amplitude of at least one of the
plurality of detection values; a change in frequency or relative
phase of at least one of the plurality of detection values; a rate
of change in both amplitude and relative phase of at least one the
plurality of detection values; and a relative rate of change in
amplitude and relative phase of at least one the plurality of
detection values; wherein the at least one operation comprises
issuing an alert; wherein the alert may be one of haptic, audible
and visual; wherein the at least one operation further comprises
storing additional data in the data storage circuit; wherein
storing additional data in the data storage circuit is further in
response to at least one of: a change in the relative phase
difference and a relative rate of change in the relative phase
difference.
[0586] An example system for data collection, processing, and
bearing analysis in an industrial environment includes: a plurality
of monitoring devices, each monitoring device comprising: a data
acquisition circuit structured to interpret a plurality of
detection values, each of the plurality of detection values
corresponding to at least one of a plurality of input sensors
communicatively coupled to the data acquisition circuit; a
streaming circuit for streaming at least a subset of the acquired
detection values to a remote learning system; and a remote learning
system including a bearing analysis circuit structured to analyze
the detection values relative to a machine-based understanding of
the state of the at least one bearing.
[0587] In certain further embodiments, an example system further
includes one or more of: wherein the machine-based understanding is
developed based on a model of the bearing that determines a state
of the at least one bearing based at least in part on the
relationship of the behavior of the bearing to an operating
frequency of a component of the industrial machine; wherein the
state of the at least one bearing is at least one of an operating
state, a health state, a predicted lifetime state and a fault
state; wherein the machine-based understanding is developed based
by providing inputs to a deep learning machine, wherein the inputs
comprise a plurality of streams of detection values for a plurality
of bearings and a plurality of measured state values for the
plurality of bearings; wherein the state of the at least one
bearing is at least one of an operating state, a health state, a
predicted lifetime state and a fault state.
[0588] An example method of analyzing bearings and sets of
bearings, includes: receiving a plurality of detection values
corresponding to data from a temperature sensor, a vibration sensor
positioned near the bearing or set of bearings and a tachometer to
measure rotation of a shaft associated with the bearing or set of
bearings; comparing the detection values corresponding to the
temperature sensor to a predetermined maximum level; filtering the
detection values corresponding to the vibration sensor through a
high pass filter where the filter is selected to eliminate
vibrations associated with detection values associated with the
tachometer; identifying rapid changes in at least one of a
temperature peak and a vibration peak; identifying frequencies at
which spikes in the filtered detection values corresponding to the
vibration sensor occur and comparing frequencies and spikes in
amplitude relative to an anticipated state information and
specification associated with the bearing or set of bearings; and
determining a bearing health parameter.
[0589] An example device for monitoring roller bearings in an
industrial environment, includes: a data acquisition circuit
structured to interpret a plurality of detection values, each of
the plurality of detection values corresponding to at least one of
a plurality of input sensors communicatively coupled to the data
acquisition circuit; a data storage circuit structured to store
specifications and anticipated state information for a plurality of
types of roller bearings and buffering the plurality of detection
values for a predetermined length of time; a bearing analysis
circuit structured to analyze buffered detection values relative to
specifications and anticipated state information resulting in a
bearing performance parameter; and [0590] a response circuit to
perform at least one operation in response to the bearing
performance prediction, wherein the plurality of input sensors
includes at least two sensors selected from the group consisting of
a temperature sensor, a load sensor, an optical vibration sensor,
an acoustic wave sensor, a heat flux sensor, an infrared sensor, an
accelerometer, a tri-axial vibration sensor and a tachometer.
[0591] An example device for monitoring sleeve bearings in an
industrial environment, includes: a data acquisition circuit
structured to interpret a plurality of detection values, each of
the plurality of detection values corresponding to at least one of
a plurality of input sensors communicatively coupled to the data
acquisition circuit; a data storage for storing sleeve bearing
specifications and anticipated state information for types of
sleeve bearings and buffering the plurality of detection values for
a predetermined length of time; a bearing analysis circuit
structured to analyze buffered detection values relative to
specifications and anticipated state information resulting in a
bearing performance parameter; and [0592] a response circuit to
perform at least one operation in response to the bearing
performance parameter, wherein the plurality of input sensors
includes at least two sensors selected from the group consisting of
a temperature sensor, a load sensor, an optical vibration sensor,
an acoustic wave sensor, a heat flux sensor, an infrared sensor, an
accelerometer, a tri-axial vibration sensor and a tachometer.
[0593] An example system for monitoring pump bearings in an
industrial environment, includes: a data acquisition circuit
structured to interpret a plurality of detection values, each of
the plurality of detection values corresponding to at least one of
a plurality of input sensors communicatively coupled to the data
acquisition circuit; a data storage for storing pump
specifications, bearing specifications, anticipated state
information for pump bearings and buffering the plurality of
detection values for a predetermined length of time; a bearing
analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a bearing performance parameter; and [0594] a response
circuit to perform at least one operation in response to the
bearing performance parameter, wherein the plurality of input
sensors includes at least two sensors selected from the group
consisting of a temperature sensor, a load sensor, an optical
vibration sensor, an acoustic wave sensor, a heat flux sensor, an
infrared sensor, an accelerometer, a tri-axial vibration sensor and
a tachometer.
[0595] An example system for collection, processing, and analyzing
pump bearings in an industrial environment includes: a plurality of
monitoring devices, each comprising: a data acquisition circuit
structured to interpret a plurality of detection values, each of
the plurality of detection values corresponding to at least one of
a plurality of input sensors communicatively coupled to the data
acquisition circuit; a data storage for storing pump
specifications, bearing specifications, anticipated state
information for pump bearings and buffering the plurality of
detection values for a predetermined length of time; a bearing
analysis circuit structured to analyze buffered detection values
relative to the pump and bearing specifications and anticipated
state information resulting in a bearing performance parameter; a
communication circuit structured to communicate with a remote
server providing the bearing performance parameter and a portion of
the buffered detection values to the remote server; and a
monitoring application on the remote server structured to receive,
store and jointly analyze a subset of the detection values from the
plurality of monitoring devices.
[0596] An example system for estimating a conveyor health
parameter, includes: a data acquisition circuit structured to
interpret a plurality of detection values, each of the plurality of
detection values corresponding to at least one of a plurality of
input sensors, wherein the plurality of input sensors comprises at
least one of an angular position sensor, an angular velocity sensor
and an angular acceleration sensor positioned to measure the
rotating component; a data storage circuit structured to store
specifications, system geometry, and anticipated state information
for the conveyor and associated rotating components, store
historical conveyor and component performance and buffer the
plurality of detection values for a predetermined length of time; a
bearing analysis circuit structured to analyze buffered detection
values relative to specifications and anticipated state information
resulting in a bearing performance parameter; and [0597] a system
analysis circuit structured to utilize the bearing performance and
at least one of an anticipated state, historical data and a system
geometry to estimate a conveyor health performance. [0598] An
example system for estimating an agitator health parameter,
includes: a data acquisition circuit structured to interpret a
plurality of detection values, each of the plurality of detection
values corresponding to at least one of a plurality of input
sensors, wherein the plurality of input sensors comprises at least
one of an angular position sensor, an angular velocity sensor and
an angular acceleration sensor positioned to measure the rotating
component; a data storage circuit structured to store
specifications, system geometry, and anticipated state information
for the agitator and associated components, store historical
agitator and component performance and buffer the plurality of
detection values for a predetermined length of time; a bearing
analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a bearing performance parameter; and a system analysis
circuit structured to utilize the bearing performance and at least
one of an anticipated state, historical data and a system geometry
to estimate an agitation health parameter. In certain further
embodiments, an example device further includes where the agitator
is one of a rotating tank mixer, a large tank mixer, a portable
tank mixers, a tote tank mixer, a drum mixer, a mounted mixer and a
propeller mixer.
[0599] An example system for estimating a vehicle steering system
performance parameter, includes: a data acquisition circuit
structured to interpret a plurality of detection values, each of
the plurality of detection values corresponding to at least one of
a plurality of input sensors, wherein the plurality of input
sensors comprises at least one of an angular position sensor, an
angular velocity sensor and an angular acceleration sensor
positioned to measure the rotating component; a data storage
circuit structured to store specifications, system geometry, and
anticipated state information for the vehicle steering system, the
rack, the pinion, and the steering column, store historical
steering system performance and buffer the plurality of detection
values for a predetermined length of time; a bearing analysis
circuit structured to analyze buffered detection values relative to
specifications and anticipated state information resulting in a
bearing performance parameter; and [0600] a system analysis circuit
structured to utilize the bearing performance and at least one of
an anticipated state, historical data and a system geometry to
estimate a vehicle steering system performance parameter.
[0601] An example system for estimating a pump performance
parameter, includes: a data acquisition circuit structured to
interpret a plurality of detection values, each of the plurality of
detection values corresponding to at least one of a plurality of
input sensors, wherein the plurality of input sensors comprises at
least one of an angular position sensor, an angular velocity sensor
and an angular acceleration sensor positioned to measure the
rotating component; a data storage circuit structured to store
specifications, system geometry, and anticipated state information
for the pump and pump components, store historical steering system
performance and buffer the plurality of detection values for a
predetermined length of time; a bearing analysis circuit structured
to analyze buffered detection values relative to specifications and
anticipated state information resulting in a bearing performance
parameter; a system analysis circuit structured to utilize the
bearing performance and at least one of an anticipated state,
historical data and a system geometry to estimate a pump
performance parameter. In certain embodiments, and example system
further includes wherein the pump is a water pump in a car, and/or
wherein the pump is a mineral pump.
[0602] An example system for estimating a performance parameter for
a drilling machine, includes: a data acquisition circuit structured
to interpret a plurality of detection values, each of the plurality
of detection values corresponding to at least one of a plurality of
input sensors, wherein the plurality of input sensors comprises at
least one of an angular position sensor, an angular velocity sensor
and an angular acceleration sensor positioned to measure the
rotating component; a data storage circuit structured to store
specifications, system geometry, and anticipated state information
for the drilling machine and drilling machine components, store
historical drilling machine performance and buffer the plurality of
detection values for a predetermined length of time; a bearing
analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a bearing performance parameter; and [0603] a system
analysis circuit structured to utilize the bearing performance and
at least one of an anticipated state, historical data and a system
geometry to estimate a performance parameter for the drilling
machine. In certain further embodiments, the drilling machine is
one of an oil drilling machine and a gas drilling machine.
[0604] An example system for estimating a performance parameter for
a drilling machine, includes: a data acquisition circuit structured
to interpret a plurality of detection values, each of the plurality
of detection values corresponding to at least one of a plurality of
input sensors, wherein the plurality of input sensors comprises at
least one of an angular position sensor, an angular velocity sensor
and an angular acceleration sensor positioned to measure the
rotating component; a data storage circuit structured to store
specifications, system geometry, and anticipated state information
for the drilling machine and drilling machine components, store
historical drilling machine performance and buffer the plurality of
detection values for a predetermined length of time; a bearing
analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a bearing performance parameter; and a system analysis
circuit structured to utilize bearing performance and at least one
of an anticipated state, historical data and a system geometry to
estimate a performance parameter for the drilling machine.
[0605] Rotating components are used throughout many different types
of equipment and applications. Rotating components may include
shafts, motors, rotors, stators, bearings, fins, vanes, wings,
blades, fans, bearings, wheels, hubs, spokes, balls, rollers, pins,
gears and the like. In embodiments, information about the health or
other status or state information of or regarding a rotating
component in a piece of industrial equipment or in an industrial
process may be obtained by monitoring the condition of the
component or various other components of the industrial equipment
or industrial process and identifying torsion on the component.
Monitoring may include monitoring the amplitude and phase of a
sensor signal, such as one measuring attributes such as angular
position, angular velocity, angular acceleration, and the like.
[0606] An embodiment of a data monitoring device 9400 is shown in
FIG. 61 and may include a plurality of sensors 9406 communicatively
coupled to a controller 9402. The controller 9402 may include a
data acquisition circuit 9404, a data storage circuit 9414, a
system evaluation circuit 9408 and, optionally, a response circuit
9410. The system evaluation circuit 9408 may comprise a torsion
analysis circuit 9412.
[0607] The plurality of sensors 9406 may be wired to ports on the
data acquisition circuit 9404. The plurality of sensors 9406 may be
wirelessly connected to the data acquisition circuit 9404. The data
acquisition circuit 9404 may be able to access detection values
corresponding to the output of at least one of the plurality of
sensors 9406 where the sensors 9406 may be capturing data on
different operational aspects of a bearing or piece of equipment or
infrastructure.
[0608] The selection of the plurality of sensors 9406 for a data
monitoring device 9400 designed to assess torsion on a component,
such as a shaft, motor, rotor, stator, bearing or gear, or other
component described herein, or a combination of components, such as
within or comprising a drive train or piece of equipment or system,
may depend on a variety of considerations such as accessibility for
installing new sensors, incorporation of sensors in the initial
design, anticipated operational and failure conditions, reliability
of the sensors, and the like. The impact of failure may drive the
extent to which a bearing or piece of equipment is monitored with
more sensors and/or higher capability sensors being dedicated to
systems where unexpected or undetected bearing failure would be
costly or have severe consequences. To assess torsion the sensors
may include, among other options, an angular position sensor and/or
an angular velocity sensor and/or an angular acceleration
sensor.
[0609] The system evaluation circuit 9408 may process the detection
values to obtain information about one or more rotating components
being monitored. The torsional analysis circuit 9412 may be
structured to identify torsion in a component or system, such as
based on anticipated state, historical state, system geometry and
the like, such as that which is available from the data storage
circuit 9414. The torsional analysis circuit 9412 may be structured
to identify torsion using a variety of techniques such as
amplitude, phase and frequency differences in the detection values
from two linear accelerometers positioned at different locations on
a shaft. The torsional analysis circuit 9412 may identify torsion
using the difference in amplitude and phase between an angular
accelerometer on a shaft and an angular accelerometer on a slip
ring on the end of the shaft. The torsional analysis circuit 9412
may identify shear stress/elongation on a component using two
strain gauges in a half bridge configuration or four strain gauges
in a full bridge configuration. The torsional analysis circuit 9412
may use coder based techniques such as markers to identify the
rotation of a shaft, bearing, rotor, stator, gear or other rotating
component. The markers being assessed may include visual markers
such as gear teeth or stripes on a shaft captured by an image
sensor, light detector or the like. The markers being assessed may
include magnetic components located on the rotating component and
sensed by an electromagnetic pickup. The sensor may be a Hall
Effect sensor.
[0610] Additional input sensors may include a thermometer, a heat
flux sensor, a magnetometer, an axial load sensor, a radial load
sensor, an accelerometer, a shear-stress torque sensor, a twist
angle sensor and the like. Twist angle may include rotational
information at two positions on shaft or an angular velocity or
angular acceleration at two positions on a shaft. In embodiments,
the sensors may be positioned at different ends of the shaft.
[0611] The torsional analysis circuit 9412 may include one or more
of a transient signal analysis circuit and/or a frequency
transformation circuit and/or a frequency analysis circuit as
described elsewhere herein.
[0612] In embodiments, the transitory signal analysis circuit for
torsional analysis may include envelope modulation analysis, and
other transitory signal analysis techniques. The system evaluation
circuit 9408 may store long stream of detection values to the data
storage circuit 9414. The transitory signal analysis circuit may
use envelope analysis techniques on those long streams of detection
values to identify transient effects (such as impacts) which may
not be identified by conventional sine wave analysis (such as
FFTs).
[0613] In embodiments, the frequencies of interest may include
identifying energy at relation-order bandwidths for rotating
equipment. The maximum order observed may comprise a function of
the bandwidth of the system and the rotational speed of the
component. For varying speeds (run-ups, run-downs, etc.), the
minimum RPM may determine the maximum-observed order. In
embodiments, there may be torsional resonance at harmonics of the
forcing frequency/frequency at which a component is being
driven.
[0614] In an illustrative and non-limiting example, the monitoring
device may be used to collect and process sensor data to measure
torsion on a component. The monitoring device may be in
communication with or include a high resolution, high speed
vibration sensor to collect data over an extended period of time,
enough to measure multiple cycles of rotation. For gear driven
equipment, the sampling resolution should be such that the number
of samples taken per cycle is at least equal to the number of gear
teeth driving the component. It will be understood that a lower
sampling resolution may also be utilized, which may result in a
lower confidence determination and/or taking data over a longer
period of time to develop sufficient statistical confidence. This
data may then be used in the generation of a phase reference
(relative probe) or tachometer signal for a piece of equipment.
This phase reference may be used to align phase data such as
velocity and/or positional and/or acceleration data from multiple
sensors located at different positions on a component or on
different components within a system. This information may
facilitate the determination of torsion for different components or
the generation of an Operational Deflection Shape ("ODS"),
indicating the extent of torsion on one or more components during
an operational mode.
[0615] The higher resolution data stream may provide additional
data for the detection of transitory signals in low speed
operations. The identification of transitory signals may enable the
identification of defects in a piece of equipment or component.
[0616] In an illustrative and non-limiting example, the monitoring
device may be used to identify mechanical jitter for use in failure
prediction models. The monitoring device may begin acquiring data
when the piece of equipment starts up through ramping up to
operating speed or during operation. Once at operating speed, it is
anticipated that the torsional jitter should be minimal and changes
in torsion during this phase may be indicative of cracks, bearing
faults and the like. Additionally, known torsions may be removed
from the signal to facilitate the identification of unanticipated
torsions resulting from system design flaws or component wear.
Having phase information associated with the data collected at
operating speed may facilitate identification of a location of
vibration and potential component wear. Relative phase information
for a plurality of sensors located throughout a machine may
facilitate the evaluation of torsion as it is propagated through a
piece of equipment.
[0617] Based on the output of its various components, the system
evaluation circuit 9408 may make a component life prediction,
identify a component health parameter, identify a component
performance parameter, and the like. The system evaluation circuit
9408 may identify unexpected torsion on a rotating component,
identify strain/stress of flexure bearings, and the like. The
system evaluation circuit 9408 may identify optimal operation
parameters for a piece of equipment to reduce torsion and extend
component life. The system evaluation circuit 9408 may identify
torsion at selected operational frequencies (e.g., shaft rotation
rates). Information about operational frequencies causing torsion
may facilitate equipment operational balance in the future.
[0618] The system evaluation circuit 9408 may communicate with the
data storage circuit 9414 to access equipment specifications,
equipment geometry, bearing specifications, component materials,
anticipated state information for a plurality of component types,
operational history, historical detection values, and the like for
use in assessing the output of its various components. The system
evaluation circuit 9408 may buffer a subset of the plurality of
detection values, intermediate data such as time-based detection
values, time-based detection values transformed to frequency
information, filtered detection values, identified frequencies of
interest, and the like for a predetermined length of time. The
system evaluation circuit 9408 may periodically store certain
detection values in the data storage circuit 9414 to enable the
tracking of component performance over time. In embodiments, based
on relevant operating conditions and/or failure modes, which may
occur as detection values approach one or more criteria, the system
evaluation circuit 9408 may store data in the data storage circuit
9414 based on the fit of data relative to one or more criteria,
such as those described throughout this disclosure. Based on one
sensor input meeting or approaching specified criteria or range,
the system evaluation circuit 9408 may store additional data such
as RPM information, component loads, temperatures, pressures,
vibrations or other sensor data of the types described throughout
this disclosure in the data storage circuit 9414. The system
evaluation circuit 9408 may store data in the data storage circuit
at a higher data rate for greater granularity in future processing,
the ability to reprocess at different sampling rates, and/or to
enable diagnosing or post-processing of system information where
operational data of interest is flagged, and the like.
[0619] Depending on the type of equipment, the component being
measured, the environment in which the equipment is operating and
the like, sensors 9406 may comprise, without limitation, one or
more of the following: a displacement sensor, an angular velocity
sensor, an angular accelerometer, a vibration sensor, an optical
vibration sensor, a thermometer, a hygrometer, a voltage sensor, a
current sensor, an accelerometer, a velocity detector, a light or
electromagnetic sensor (e.g., determining temperature, composition
and/or spectral analysis, and/or object position or movement), an
image sensor, a structured light sensor, a laser-based image
sensor, an infrared sensor, an acoustic wave sensor, a heat flux
sensor, a displacement sensor, a turbidity meter, a viscosity
meter, a load sensor, a tri-axial vibration sensor, an
accelerometer, a tachometer, a fluid pressure meter, an air flow
meter, a horsepower meter, a flow rate meter, a fluid particle
detector, an acoustical sensor, a pH sensor, and the like,
including, without limitation, any of the sensors described
throughout this disclosure and the documents incorporated by
reference.
[0620] The sensors 9406 may provide a stream of data over time that
has a phase component, such as relating to angular velocity,
angular acceleration or vibration, allowing for the evaluation of
phase or frequency analysis of different operational aspects of a
piece of equipment or an operating component. The sensors 9406 may
provide a stream of data that is not conventionally phase-based,
such as temperature, humidity, load, and the like. The sensors 9406
may provide a continuous or near continuous stream of data over
time, periodic readings, event-driven readings, and/or readings
according to a selected interval or schedule.
[0621] In an illustrative and non-limiting example, when assessing
engine components it may be desirable to remove vibrations due to
the timing of piston vibrations or anticipated vibrational input
due to crankshaft geometry to assist in identifying other torsional
forces on a component. This may assist in assessing the health of
such diverse components as a water pump in a vehicle or positive
displacement pumps.
[0622] In an illustrative and non-limiting example, torsional
analysis and the identification of variations in torsion may assist
in the identification of stick-slip in a gear or transfer system.
In some cases, this may only occur once per cycle, and phase
information may be as important as or more important than the
amplitude of the signal in determining system state or
behavior.
[0623] In an illustrative and non-limiting example, torsional
analysis may assist in the identification, prediction (e.g.,
timing) and evaluation of lash in a drive train and the follow-on
torsion resulting from a change in direction or start up, which in
turn may be used for controlling a system, assessing needs for
maintenance, assessing needs for balancing or otherwise re-setting
components, or the like.
[0624] In an illustrative and non-limiting example, when assessing
compressors, it may be desirable to remove vibrations due to the
timing of piston vibrations or anticipated vibrational input
associated with the techniques and geometry used for positive
displacement compressors to assist in identifying other torsional
forces on a component. This may assist in assessing the health of
compressors in such diverse environments as air conditioning units
in factories, compressors in gas handling systems in an industrial
environment, compressors in oil fields, and other environments as
described elsewhere herein.
[0625] In an illustrative and non-limiting example, torsional
analysis may facilitate the understanding of the health and
expected life of various components associated with the drive
trains of vehicles, such as cranes, bulldozers, tractors, haulers,
backhoes, forklifts, agricultural equipment, mining equipment,
boring and drilling machines, digging machines, lifting machines,
mixers (e.g., cement mixers), tank trucks, refrigeration trucks,
security vehicles (e.g., including safes and similar facilities for
preserving valuables), underwater vehicles, watercraft, aircraft,
automobiles, trucks, trains and the like, as well as drive trains
of moving apparatus, such as assembly lines, lifts, cranes,
conveyors, hauling systems, and others. The evaluation of the
sensor data with the model of the system geometry and operating
conditions may be useful in identifying unexpected torsion and the
transmission of that torsion from the motor and drive shaft, from
the drive shaft to the universal joint and from the universal joint
to one or more wheel axles.
[0626] In an illustrative and non-limiting example, torsional
analysis may facilitate in the understanding of the health and
expected life of various components associated with train/tram
wheels and wheel sets. As discussed above, torsional analysis may
facilitate in the identification of stick-slip between the wheels
or wheel sets and the rail. The torsional analysis in view of the
system geometry may facilitate the identification of torsional
vibration due to stick-slip as opposed to the torsional vibration
due to the driving geometry connecting the engine to the drive
shaft to the wheel axle.
[0627] In embodiments, as illustrated in FIG. 61, the sensors 9406
may be part of the data monitoring device 9400, referred to herein
in some cases as a data collector, which in some cases may comprise
a mobile or portable data collector. In embodiments, as illustrated
in FIGS. 62 and 63, one or more external sensors 9422, which are
not explicitly part of a monitoring device 9416 but rather are new,
previously attached to or integrated into the equipment or
component, may be opportunistically connected to or accessed by the
monitoring device 9416. The monitoring device 9416 may include a
controller 9418. The controller 9418 may include a data acquisition
circuit 9420, a data storage circuit 9414, a system evaluation
circuit 9408 and, optionally, a response circuit 9410. The system
evaluation circuit 9408 may comprise a torsional analysis circuit
9412. The data acquisition circuit 9420 may include one or more
input ports 9424. In embodiments as shown in FIG. 63, a data
acquisition circuit 9420 may further comprise a wireless
communications circuit 9426. The one or more external sensors 9422
may be directly connected to the one or more input ports 9424 on
the data acquisition circuit 9420 of the controller 9418 or may be
accessed by the data acquisition circuit 9420 wirelessly using the
wireless communications circuit 9426, such as by a reader,
interrogator, or other wireless connection, such as over a
short-distance wireless protocol. The data acquisition circuit 9420
may use the wireless communications circuit 9426 to access
detection values corresponding to the one or more external sensors
9422 wirelessly or via a separate source or some combination of
these methods.
[0628] In embodiments, as illustrated in FIG. 64, the data
acquisition circuit 9432 may further comprise a multiplexer circuit
9434 as described elsewhere herein. Outputs from the multiplexer
circuit 9434 may be utilized by the system evaluation circuit 9408.
The response circuit 9410 may have the ability to turn on or off
portions of the multiplexor circuit 9434. The response circuit 9410
may have the ability to control the control channels of the
multiplexor circuit 9434
[0629] The response circuit 9410 may initiate actions based on a
component performance parameter, a component health value, a
component life prediction parameter, and the like. The response
circuit 9410 may evaluate the results of the system evaluation
circuit 9408 and, based on certain criteria or the output from
various components of the system evaluation circuit 9408, may
initiate an action. The criteria may include identification of
torsion on a component by the torsional analysis circuit. The
criteria may include a sensor's detection values at certain
frequencies or phases relative to a timer signal where the
frequencies or phases of interest may be based on the equipment
geometry, equipment control schemes, system input, historical data,
current operating conditions, and/or an anticipated response. The
criteria may include a sensor's detection values at certain
frequencies or phases relative to detection values of a second
sensor. The criteria may include signal strength at certain
resonant frequencies/harmonics relative to detection values
associated with a system tachometer or anticipated based on
equipment geometry and operation conditions. Criteria may include a
predetermined peak value for a detection value from a specific
sensor, a cumulative value of a sensor's corresponding detection
value over time, a change in peak value, a rate of change in a peak
value, and/or an accumulated value (e.g., a time spent above/below
a threshold value, a weighted time spent above/below one or more
threshold values, and/or an area of the detected value above/below
one or more threshold values). The criteria may comprise
combinations of data from different sensors such as relative
values, relative changes in value, relative rates of change in
value, relative values over time, and the like. The relative
criteria may change with other data or information such as process
stage, type of product being processed, type of equipment, ambient
temperature and humidity, external vibrations from other equipment,
and the like. The relative criteria may be reflected in one or more
calculated statistics or metrics (including ones generated by
further calculations on multiple criteria or statistics), which in
turn may be used for processing (such as on board a data collector
or by an external system), such as to be provided as an input to
one or more of the machine learning capabilities described in this
disclosure, to a control system (which may be on board a data
collector or remote, such as to control selection of data inputs,
multiplexing of sensor data, storage, or the like), or as a data
element that is an input to another system, such as a data stream
or data package that may be available to a data marketplace, a
SCADA system, a remote control system, a maintenance system, an
analytic system, or other system.
[0630] Certain embodiments are described herein as detected values
exceeding thresholds or predetermined values, but detected values
may also fall below thresholds or predetermined values--for example
where an amount of change in the detected value is expected to
occur, but detected values indicate that the change may not have
occurred. Except where the context clearly indicates otherwise, any
description herein describing a determination of a value above a
threshold and/or exceeding a predetermined or expected value is
understood to include determination of a value below a threshold
and/or falling below a predetermined or expected value.
[0631] The predetermined acceptable range may be based on
anticipated torsion based on equipment geometry, the geometry of a
transfer system, an equipment configuration or control scheme, such
as a piston firing sequence, and the like. The predetermined
acceptable range may also be based on historical performance or
predicted performance, such as long term analysis of signals and
performance both from the past run and from the past several runs.
The predetermined acceptable range may also be based on historical
performance or predicted performance, or based on long term
analysis of signals and performance across a plurality of similar
equipment and components (both within a specific environment,
within an individual company, within multiple companies in the same
industry and across industries). The predetermined acceptable range
may also be based on a correlation of sensor data with actual
equipment and component performance.
[0632] In some embodiments, an alert may be issued based on some of
the criteria discussed above. In embodiments, the relative criteria
for an alarm may change with other data or information, such as
process stage, type of product being processed on equipment,
ambient temperature and humidity, external vibrations from other
equipment and the like. In an illustrative and non-limiting
example, the response circuit 9410 may initiate an alert if a
torsion in a component across a plurality of components exceeds a
predetermined maximum value, if there is a change or rate of change
that exceeds a predetermined acceptable range, and/or if an
accumulated value based on torsion amplitude and/or frequency
exceeds a threshold.
[0633] In embodiments, response circuit 9410 may cause the data
acquisition circuit 9432 to enable or disable the processing of
detection values corresponding to certain sensors based on some of
the criteria discussed above. This may include switching to sensors
having different response rates, sensitivity, ranges, and the like;
accessing new sensors or types of sensors, and the like. Switching
may be undertaken based on a model, a set of rules, or the like. In
embodiments, switching may be under control of a machine learning
system, such that switching is controlled based on one or more
metrics of success, combined with input data, over a set of trials,
which may occur under supervision of a human supervisor or under
control of an automated system. Switching may involve switching
from one input port to another (such as to switch from one sensor
to another). Switching may involve altering the multiplexing of
data, such as combining different streams under different
circumstances. Switching may involve activating a system to obtain
additional data, such as moving a mobile system (such as a robotic
or drone system), to a location where different or additional data
is available (such as positioning an image sensor for a different
view or positioning a sonar sensor for a different direction of
collection) or to a location where different sensors can be
accessed (such as moving a collector to connect up to a sensor that
is disposed at a location in an environment by a wired or wireless
connection). This switching may be implemented by changing the
control signals for a multiplexor circuit 9434 and/or by turning on
or off certain input sections of the multiplexor circuit 9434.
[0634] The response circuit 9410 may calculate transmission
effectiveness based on differences between a measured and
theoretical angular position and velocity of an output shaft after
accounting for the gear ration and any phase differential between
input and output.
[0635] The response circuit 9410 may identify equipment or
components that are due for maintenance. The response circuit 9410
may make recommendations for the replacement of certain sensors in
the future with sensors having different response rates,
sensitivity, ranges, and the like. The response circuit 9410 may
recommend design alterations for future embodiments of the
component, the piece of equipment, the operating conditions, the
process, and the like.
[0636] In embodiments, the response circuit 9410 may recommend
maintenance at an upcoming process stop or initiate a maintenance
call. The response circuit 9410 may recommend changes in process or
operating parameters to remotely balance the piece of equipment. In
embodiments, the response circuit 9410 may implement or recommend
process changes--for example to lower the utilization of a
component that is near a maintenance interval, operating
off-nominally, or failed for purpose but still at least partially
operational, to change the operating speed of a component (such as
to put it in a lower-demand mode), to initiate amelioration of an
issue (such as to signal for additional lubrication of a roller
bearing set, or to signal for an alignment process for a system
that is out of balance), and the like.
[0637] In embodiments as shown in FIGS. 65, 66, 67, and 68, a data
monitoring system 9460 may include at least one data monitoring
device 9448. At least one data monitoring device 9448 may include
sensors 9406 and a controller 9438 comprising a data acquisition
circuit 9404, a system evaluation circuit 9408, a data storage
circuit 9414, and a communications circuit 9442. The system
evaluation circuit 9408 may include a torsional analysis circuit
9412. There may also be an optional response circuit as described
above and elsewhere herein. The system evaluation circuit 9408 may
periodically share data with the communication circuit 9442 for
transmittal to the remote server 9440 to enable the tracking of
component and equipment performance over time and under varying
conditions by a monitoring application 9446. Because relevant
operating conditions and/or failure modes may occur as sensor
values approach one or more criteria, the system evaluation circuit
9408 may share data with the communication circuit 9462 for
transmittal to the remote server 9440 based on the fit of data
relative to one or more criteria. Based on one sensor input meeting
or approaching specified criteria or range, the system evaluation
circuit 9408 may share additional data such as RPMs, component
loads, temperatures, pressures, vibrations, and the like for
transmittal. The system evaluation circuit 9408 may share data at a
higher data rate for transmittal to enable greater granularity in
processing on the remote server. In embodiments, as shown in FIG.
65, the communications circuit 9442 may communicate data directly
to a remote server 9440. In embodiments, as shown in FIG. 66, the
communications circuit 9442 may communicate data to an intermediate
computer 9450 which may include a processor 9452 running an
operating system 9454 and a data storage circuit 9456.
[0638] In embodiments, as illustrated in FIGS. 67 and 68, a data
collection system 9458 may have a plurality of monitoring devices
9448 collecting data on multiple components in a single piece of
equipment, collecting data on the same component across a plurality
of pieces of equipment (both the same and different types of
equipment) in the same facility as well as collecting data from
monitoring devices in multiple facilities. A monitoring application
9446 on a remote server 9440 may receive and store one or more of
detection values, timing signals and data coming from a plurality
of the various monitoring devices 9448. In embodiments, as shown in
FIG. 67, the communications circuit 9442 may communicate data
directly to a remote server 9440. In embodiments, as shown in FIG.
68, the communications circuit 9442 may communicate data to an
intermediate computer 9450, which may include a processor 9452
running an operating system 9454 and a data storage circuit 9456.
There may be an individual intermediate computer 9450 associated
with each monitoring device 9264 or an individual intermediate
computer 9450 may be associated with a plurality of monitoring
devices 9448 where the intermediate computer 9450 may collect data
from a plurality of data monitoring devices and send the cumulative
data to the remote server 9440.
[0639] The monitoring application 9446 may select subsets of
detection values, timing signals, data, product performance and the
like to be jointly analyzed. Subsets for analysis may be selected
based on component type, component materials, or a single type of
equipment in which a component is operating. Subsets for analysis
may be selected or grouped based on common operating conditions or
operational history such as size of load, operational condition
(e.g., intermittent, continuous), operating speed or tachometer,
common ambient environmental conditions such as humidity,
temperature, air or fluid particulate, and the like. Subsets for
analysis may be selected based on common anticipated state
information. Subsets for analysis may be selected based on the
effects of other nearby equipment such as nearby machines rotating
at similar frequencies, nearby equipment producing electromagnetic
fields, nearby equipment producing heat, nearby equipment inducing
movement or vibration, nearby equipment emitting vapors, chemicals
or particulates, or other potentially interfering or intervening
effects.
[0640] The monitoring application 9446 may analyze a selected
subset. In an illustrative example, data from a single component
may be analyzed over different time periods such as one operating
cycle, cycle to cycle comparisons, trends over several operating
cycles/time such as a month, a year, the life of the component or
the like. Data from multiple components of the same type may also
be analyzed over different time periods. Trends in the data such as
changes in frequency or amplitude may be correlated with failure
and maintenance records associated with the same component or piece
of equipment. Trends in the data such as changing rates of change
associated with start-up or different points in the process may be
identified. Additional data may be introduced into the analysis
such as output product quality, output quantity (such as per unit
of time), indicated success or failure of a process, and the like.
Correlation of trends and values for different types of data may be
analyzed to identify those parameters whose short-term analysis
might provide the best prediction regarding expected performance.
The analysis may identify model improvements to the model for
anticipated state information, recommendations around sensors to be
used, positioning of sensors and the like. The analysis may
identify additional data to collect and store. The analysis may
identify recommendations regarding needed maintenance and repair
and/or the scheduling of preventative maintenance. The analysis may
identify recommendations around purchasing replacement components
and the timing of the replacement of the components. The analysis
may identify recommendations regarding future geometry changes to
reduce torsion on components. The analysis may result in warning
regarding dangers of catastrophic failure conditions. This
information may be transmitted back to the monitoring device to
update types of data collected and analyzed locally or to influence
the design of future monitoring devices.
[0641] In embodiments, the monitoring application 9446 may have
access to equipment specifications, equipment geometry, component
specifications, component materials, anticipated state information
for a plurality of component types, operational history, historical
detection values, component life models and the like for use
analyzing the selected subset using rule-based or model-based
analysis. In embodiments, the monitoring application 9446 may feed
a neural net with the selected subset to learn to recognize various
operating states, health states (e.g., lifetime predictions) and
fault states utilizing deep learning techniques. In embodiments, a
hybrid of the two techniques (model-based learning and deep
learning) may be used.
[0642] In an illustrative and non-limiting example, the health of
the rotating components on conveyors and lifters in an assembly
line may be monitored using the torsional analysis techniques, data
monitoring devices and data collection systems described
herein.
[0643] In an illustrative and non-limiting example, the health the
rotating components in water pumps on industrial vehicles may be
monitored using the torsional analysis techniques, data monitoring
devices and data collection systems described herein.
[0644] In an illustrative and non-limiting example, the health of
rotating components in compressors in gas handling systems may be
monitored using the data monitoring devices and data collection
systems described herein.
[0645] In an illustrative and non-limiting example, the health of
the rotating components in compressors situated in the gas and oil
fields may be monitored using the data monitoring devices and data
collection systems described herein.
[0646] In an illustrative and non-limiting example, the health of
the rotating components in factory air conditioning units may be
evaluated using the techniques, data monitoring devices and data
collection systems described herein.
[0647] In an illustrative and non-limiting example, the health of
the rotating components in factory mineral pumps may be evaluated
using the techniques, data monitoring devices and data collection
systems described herein.
[0648] In an illustrative and non-limiting example, the health of
the rotating components such as shafts, bearings, and gears in
drilling machines and screw drivers situated in the oil and gas
fields may be evaluated using the torsional analysis techniques,
data monitoring devices and data collection systems described
herein.
[0649] In an illustrative and non-limiting example, the health of
rotating components such as shafts, bearings, gears, and rotors of
motors situated in the oil and gas fields may be evaluated using
the torsional analysis techniques, data monitoring devices and data
collection systems described herein.
[0650] In an illustrative and non-limiting example, the health of
rotating components such as blades, screws and other components of
pumps situated in the oil and gas fields may be evaluated using the
torsional analysis techniques, data monitoring devices and data
collection systems described herein.
[0651] In an illustrative and non-limiting example, the health of
rotating components such as shafts, bearings, motors, rotors,
stators, gears, and other components of vibrating conveyors
situated in the oil and gas fields may be evaluated using the
torsional analysis techniques, data monitoring devices and data
collection systems described herein.
[0652] In an illustrative and non-limiting example, the health of
rotating components such as bearings, shafts, motors, rotors,
stators, gears, and other components of mixers situated in the oil
and gas fields may be evaluated using the torsional analysis
techniques, data monitoring devices and data collection systems
described herein.
[0653] In an illustrative and non-limiting example, the health of
rotating components such as bearings, shafts, motors, rotors,
stators, gears, and other components of centrifuges situated in oil
and gas refineries may be evaluated using the torsional analysis
techniques, data monitoring devices and data collection systems
described herein.
[0654] In an illustrative and non-limiting example, the health of
rotating components such as bearings, shafts, motors, rotors,
stators, gears, and other components of refining tanks situated in
oil and gas refineries may be evaluated using the torsional
analysis techniques, data monitoring devices and data collection
systems described herein.
[0655] In an illustrative and non-limiting example, the health of
rotating components such as bearings, shafts, motors, rotors,
stators, gears, and other components of rotating tank/mixer
agitators to promote chemical reactions deployed in chemical and
pharmaceutical production lines may be evaluated using the
torsional analysis techniques, data monitoring devices and data
collection systems described herein.
[0656] In an illustrative and non-limiting example, the health of
rotating components such as bearings, shafts, motors, rotors,
stators, gears, and other components of mechanical/rotating
agitators to promote chemical reactions deployed in chemical and
pharmaceutical production lines may be evaluated using the
torsional analysis techniques, data monitoring devices and data
collection systems described herein.
[0657] In an illustrative and non-limiting example, the health of
rotating components such as bearings, shafts, motors, rotors,
stators, gears, and other components of propeller agitators to
promote chemical reactions deployed in chemical and pharmaceutical
production lines may be evaluated using the torsional analysis
techniques, data monitoring devices and data collection systems
described herein.
[0658] In an illustrative and non-limiting example, the health of
bearings and associated shafts, motors, rotors, stators, gears, and
other components of vehicle steering mechanisms may be evaluated
using the torsional analysis techniques, data monitoring devices
and data collection systems described herein.
[0659] In an illustrative and non-limiting example, the health of
bearings and associated shafts, motors, rotors, stators, gears, and
other components of vehicle engines may be evaluated using the
torsional analysis techniques, data monitoring devices and data
collection systems described herein.
[0660] In embodiments, a monitoring device for estimating an
anticipated lifetime of a rotating component in an industrial
machine may comprise a data acquisition circuit structured to
interpret a plurality of detection values, each of the plurality of
detection values corresponding to at least one of a plurality of
input sensors, wherein the plurality of input sensors comprises at
least one of an angular position sensor, an angular velocity sensor
and an angular acceleration sensor positioned to measure the
rotating component; a data storage circuit structured to store
specifications, system geometry, and anticipated state information
for a plurality of rotating components, store historical component
performance and buffer the plurality of detection values for a
predetermined length of time; and a torsional analysis circuit
structured to utilize transitory signal analysis to analyze the
buffered detection values relative to the rotating component
specifications and anticipated state information resulting in the
identification of torsional vibration; and a system analysis
circuit structured to utilize the identified torsional vibration
and at least one of an anticipated state, historical data and a
system geometry to identify an anticipated lifetime of the rotating
component. In embodiments, the monitoring device may further
comprise a response circuit to perform at least one operation in
response to the anticipated lifetime of the rotating component,
wherein the plurality of input sensors includes at least two
sensors selected from the group consisting of a temperature sensor,
a load sensor, an optical vibration sensor, an acoustic wave
sensor, a heat flux sensor, an infrared sensor, an accelerometer, a
tri-axial vibration sensor, a tachometer, and the like. At least
one operation may comprise issuing at least one of an alert and a
warning, storing additional data in the data storage circuit,
ordering a replacement of the rotating component, scheduling
replacement of the rotating component, recommending alternatives to
the rotating component, and the like.
[0661] In embodiments, a monitoring device for evaluating the
health of a rotating component in an industrial machine may
comprise a data acquisition circuit structured to interpret a
plurality of detection values, each of the plurality of detection
values corresponding to at least one of a plurality of input
sensors, wherein the plurality of input sensors comprises at least
one of an angular position sensor, an angular velocity sensor and
an angular acceleration sensor positioned to measure the rotating
component; a data storage circuit structured to store
specifications, system geometry, and anticipated state information
for a plurality of rotating components, store historical component
performance and buffer the plurality of detection values for a
predetermined length of time; and a torsional analysis circuit
structured to utilize transitory signal analysis to analyze the
buffered detection values relative to the rotating component
specifications and anticipated state information resulting in the
identification of torsional vibration; and a system analysis
circuit structured to utilize the identified torsional vibration
and at least one of an anticipated state, historical data and a
system geometry to identify the health of the rotating component.
In embodiments, the monitoring device may further comprise a
response circuit to perform at least one operation in response to
the health of the rotating component. The plurality of input
sensors may include at least two sensors selected from the group
consisting of a temperature sensor, a load sensor, an optical
vibration sensor, an acoustic wave sensor, a heat flux sensor, an
infrared sensor, an accelerometer, a tri-axial vibration sensor a
tachometer, and the like. The monitoring device may issue an alert
and an alarm, such as the at least one operation storing additional
data in the data storage circuit, ordering a replacement of the
rotating component, scheduling replacement of the rotating
component, recommending alternatives to the rotating component, and
the like.
[0662] In embodiments, a monitoring device for evaluating the
operational state of a rotating component in an industrial machine
may comprise a data acquisition circuit structured to interpret a
plurality of detection values, each of the plurality of detection
values corresponding to at least one of a plurality of input
sensors, wherein the plurality of input sensors comprises at least
one of an angular position sensor, an angular velocity sensor and
an angular acceleration sensor positioned to measure the rotating
component; a data storage circuit structured to store
specifications, system geometry, and anticipated state information
for a plurality of rotating components, store historical component
performance and buffer the plurality of detection values for a
predetermined length of time; and a torsional analysis circuit
structured to utilize transitory signal analysis to analyze the
buffered detection values relative to the rotating component
specifications and anticipated state information resulting in the
identification of torsional vibration; and a system analysis
circuit structured to utilize the identified torsional vibration
and at least one of an anticipated state, historical data and a
system geometry to identify the operational state of the rotating
component. In embodiments, the operational state may be a current
or future operational state. A response circuit may perform at
least one operation in response to the operational state of the
rotating component. The at least one operation may store additional
data in the data storage circuit, order a replacement of the
rotating component, schedule a replacement of the rotating
component, recommending alternatives to the rotating component, and
the like.
[0663] In embodiments, s monitoring device for evaluating the
operational state of a rotating component in an industrial machine
may include a data acquisition circuit structured to interpret a
plurality of detection values, each of the plurality of detection
values corresponding to at least one of a plurality of input
sensors, wherein the plurality of input sensors comprises at least
one of an angular position sensor, an angular velocity sensor and
an angular acceleration sensor positioned to measure the rotating
component; a data storage circuit structured to store
specifications, system geometry, and anticipated state information
for a plurality of rotating components, store historical component
performance and buffer the plurality of detection values for a
predetermined length of time; and a torsional analysis circuit
structured to utilize transitory signal analysis to analyze the
buffered detection values relative to the rotating component
specifications and anticipated state information resulting in the
identification of torsional vibration; and a system analysis
circuit structured to utilize the identified torsional vibration
and at least one of an anticipated state, historical data and a
system geometry to identify the operational state of the rotating
component, wherein the data acquisition circuit comprises a
multiplexer circuit whereby alternative combinations of the
detection values may be selected based on at least one of user
input, a detected state and a selected operating parameter for a
machine. The operational state may be a current or future
operational state. The at least one operation may enable or disable
one or more portions of the multiplexer circuit, or altering the
multiplexer control lines. The data acquisition circuit may include
at least two multiplexer circuits and the at least one operation
comprises changing connections between the at least two multiplexer
circuits.
[0664] In embodiments, a system for evaluating an operational state
a rotating component in a piece of equipment may comprise a data
acquisition circuit structured to interpret a plurality of
detection values, each of the plurality of detection values
corresponding to at least one of a plurality of input sensors,
wherein the plurality of input sensors comprises at least one of an
angular position sensor, an angular velocity sensor and an angular
acceleration sensor positioned to measure the rotating component; a
data storage circuit structured to store specifications, system
geometry, and anticipated state information for a plurality of
rotating components, store historical component performance and
buffer the plurality of detection values for a predetermined length
of time; and a torsional analysis circuit structured to utilize
transitory signal analysis to analyze the buffered detection values
relative to the rotating component specifications and anticipated
state information resulting in identification of any torsional
vibration; a system analysis circuit structured to utilize the
torsional vibration and at least one of an anticipated state,
historical data and a system geometry to identify the operational
state of the rotating component; and a communication module enabled
to communicate the operational state of the rotating component, the
torsional vibration and detection values to a remote server,
wherein the detection values communicated are based partly on the
operational state of the rotating component and the torsional
vibration; and a monitoring application on the remote server
structured to receive, store and jointly analyze a subset of the
detection values from the monitoring devices. The analysis of the
subset of detection values may include transitory signal analysis
to identify the presence of high frequency torsional vibration. The
monitoring application may be structured to subset detection values
based on one of: operational state, torsional vibration, type of
the rotating component, operational conditions under which
detection values were measured, and type or equipment. The analysis
of the subset of detection values may include feeding a neural net
with the subset of detection values and supplemental information to
learn to recognize various operating states, health states and
fault states utilizing deep learning techniques. The supplemental
information may include one of component specification, component
performance, equipment specification, equipment performance,
maintenance records, repair records an anticipated state model, and
the like. The operational state may include a current or future
operational state. The monitoring device may include a response
circuit to perform at least one operation in response to the
operational state of the rotating component. The at least one
operation may include storing additional data in the data storage
circuit.
[0665] In embodiments, a system for evaluating the health of a
rotating component in a piece of equipment may comprise a data
acquisition circuit structured to interpret a plurality of
detection values, each of the plurality of detection values
corresponding to at least one of a plurality of input sensors,
wherein the plurality of input sensors comprises at least one of:
an angular position sensor, an angular velocity sensor and an
angular acceleration sensor positioned to measure the rotating
component; a data storage circuit structured to store
specifications, system geometry, and anticipated state information
for a plurality of rotating components, store historical component
performance and buffer the plurality of detection values for a
predetermined length of time; and a torsional analysis circuit
structured to utilize transitory signal analysis to analyze the
buffered detection values relative to the rotating component
specifications and anticipated state information resulting in
identification of torsional vibration; a system analysis circuit
structured to utilize the torsional vibration and at least one of
an anticipated state, historical data and a system geometry to
identify the health of the rotating component; and a communication
module enabled to communicate the health of the rotating component,
the torsional vibrations and detection values to a remote server,
wherein the detection values communicated are based partly on the
health of the rotating component and the torsional vibration; and a
monitoring application on the remote server structured to receive,
store and jointly analyze a subset of the detection values from the
monitoring devices. In embodiments, the analysis of the subset of
detection values may include transitory signal analysis to identify
the presence of high frequency torsional vibration. The monitoring
application may be structured to subset detection values. The
analysis of the subset of detection values may include feeding a
neural net with the subset of detection values and supplemental
information to learn to recognize various operating states, health
states and fault states utilizing deep learning techniques. The
supplemental information may include one of component
specification, component performance, equipment specification,
equipment performance, maintenance records, repair records and an
anticipated state model. The operational state may be a current or
future operational state. A response circuit may perform at least
one operation in response to the health of the rotating
component.
[0666] In embodiments, a system for estimating an anticipated
lifetime of a rotating component in a piece of equipment may
comprise a data acquisition circuit structured to interpret a
plurality of detection values, each of the plurality of detection
values corresponding to at least one of a plurality of input
sensors, wherein the plurality of input sensors comprises at least
one of an angular position sensor, an angular velocity sensor and
an angular acceleration sensor positioned to measure the rotating
component; a data storage circuit structured to store
specifications, system geometry, and anticipated state information
for a plurality of rotating components, store historical component
performance and buffer the plurality of detection values for a
predetermined length of time; and a torsional analysis circuit
structured to utilize transitory signal analysis to analyze the
buffered detection values relative to the rotating component
specifications and anticipated state information resulting in
identification of torsional vibration; a system analysis circuit
structured to utilize the torsional vibration and at least one of
an anticipated state, historical data and a system geometry to
identify an anticipated life the rotating component; and a
communication module enabled to communicate the anticipated life of
the rotating component, the torsional vibrations and detection
values to a remote server, wherein the detection values
communicated are based partly on the anticipated life of the
rotating component and the torsional vibration; and a monitoring
application on the remote server structured to receive, store and
jointly analyze a subset of the detection values from the
monitoring devices. In embodiments, the analysis of the subset of
detection values may include transitory signal analysis to identify
the presence of high frequency torsional vibration. The monitoring
application may be structured to subset detection values based on
one of anticipated life of the rotating component, torsional
vibration, type of the rotating component, operational conditions
under which detection values were measured, and type of equipment.
The analysis of the subset of detection values may include feeding
a neural net with the subset of detection values and supplemental
information to learn to recognize various operating states, health
states, life expectancies and fault states utilizing deep learning
techniques. The supplemental information may include one of
component specification, component performance, equipment
specification, equipment performance, maintenance records, repair
records and an anticipated state model. The monitoring device may
include a response circuit to perform at least one operation in
response to the anticipated life of the rotating component. The at
least one operation may include one of ordering a replacement of
the rotating component, scheduling replacement of the rotating
component, and recommending alternatives to the rotating
component.
[0667] In embodiments, a system for evaluating the health of a
variable frequency motor in an industrial environment may comprise
a data acquisition circuit structured to interpret a plurality of
detection values, each of the plurality of detection values
corresponding to at least one of a plurality of input sensors,
wherein the plurality of input sensors comprises at least one of an
angular position sensor, an angular velocity sensor and an angular
acceleration sensor positioned to measure the rotating component; a
data storage circuit structured to store specifications, system
geometry, and anticipated state information for a plurality of
rotating components, store historical component performance and
buffer the plurality of detection values for a predetermined length
of time; and a torsional analysis circuit structured to utilize
transitory signal analysis to analyze the buffered detection values
relative to the rotating component specifications and anticipated
state information resulting in identification of torsional
vibration; a system analysis circuit structured to utilize the
torsional vibration and at least one of an anticipated state,
historical data and a system geometry to identify a motor health
parameter; and a communication module enabled to communicate the
motor health parameter, the torsional vibrations and detection
values to a remote server, wherein the detection values
communicated are based partly on the motor health parameter and the
torsional vibration; and a monitoring application on the remote
server structured to receive, store and jointly analyze a subset of
the detection values from the monitoring devices.
[0668] In embodiments, a system for data collection, processing,
and torsional analysis of a rotating component in an industrial
environment may comprise a data acquisition circuit structured to
interpret a plurality of detection values, each of the plurality of
detection values corresponding to at least one of a plurality of
input sensors, wherein the plurality of input sensors comprises at
least one of an angular position sensor, an angular velocity sensor
and an angular acceleration sensor positioned to measure the
rotating component; a streaming circuit for streaming at least a
subset of the acquired detection values to a remote learning
system; and a remote learning system including a torsional analysis
circuit structured to analyze the detection values relative to a
machine-based understanding of the state of the at least one
rotating component. The machine-based understanding may be
developed based on a model of the rotating component that
determines a state of the at least one rotating component based at
least in part on the relationship of the behavior of the rotating
component to an operating frequency of a component of the
industrial machine. The state of the at least one rotating
component may be at least one of an operating state, a health
state, a predicted lifetime state and a fault state. The
machine-based understanding may be developed based by providing
inputs to a deep learning machine, wherein the inputs comprise a
plurality of streams of detection values for a plurality of
rotating components and a plurality of measured state values for
the plurality of rotating components. The state of the at least one
rotating component may be at least one of an operating state, a
health state, a predicted lifetime state and a fault state.
[0669] In embodiments, information about the health or other status
or state information of or regarding a component or piece of
industrial equipment may be obtained by monitoring the condition of
various components throughout a process. Monitoring may include
monitoring the amplitude of a sensor signal measuring attributes
such as temperature, humidity, acceleration, displacement and the
like. An embodiment of a data monitoring device 9700 is shown in
FIG. 69 and may include a plurality of sensors 9706 communicatively
coupled to a controller 9702. The controller 9702 may include a
data acquisition circuit 9704, a signal evaluation circuit 9708, a
data storage circuit 9716 and a response circuit 9710. The signal
evaluation circuit 9708 may comprise a circuit for detecting a
fault in one or more sensors, or a set of sensors, such as an
overload detection circuit 9712, a sensor fault detection circuit
9714, or both. Additionally, the signal evaluation circuit 9708 may
optionally comprise one or more of a peak detection circuit, a
phase detection circuit, a bandpass filter circuit, a frequency
transformation circuit, a frequency analysis circuit, a phase lock
loop circuit, a torsional analysis circuit, a bearing analysis
circuit, and the like.
[0670] The plurality of sensors 9706 may be wired to ports on the
data acquisition circuit 9704. The plurality of sensors 9706 may be
wirelessly connected to the data acquisition circuit 9704. The data
acquisition circuit 9704 may be able to access detection values
corresponding to the output of at least one of the plurality of
sensors 9706 where the sensors 9706 may be capturing data on
different operational aspects of a piece of equipment or an
operating component.
[0671] The selection of the plurality of sensors 9706 for a data
monitoring device 9700 designed for a specific component or piece
of equipment may depend on a variety of considerations such as
accessibility for installing new sensors, incorporation of sensors
in the initial design, anticipated operational and failure
conditions, resolution desired at various positions in a process or
plant, reliability of the sensors, and the like. The impact of a
failure, time response of a failure (e.g., warning time and/or
off-nominal modes occurring before failure), likelihood of failure,
and/or sensitivity required and/or difficulty to detection failure
conditions may drive the extent to which a component or piece of
equipment is monitored with more sensors and/or higher capability
sensors being dedicated to systems where unexpected or undetected
failure would be costly or have severe consequences.
[0672] Depending on the type of equipment, the component being
measured, the environment in which the equipment is operating and
the like, sensors 9706 may comprise, without limitation, one or
more of the following: a vibration sensor, a thermometer, a
hygrometer, a voltage sensor and/or a current sensor (for the
component and/or other sensors measuring the component), an
accelerometer, a velocity detector, a light or electromagnetic
sensor (e.g., determining temperature, composition and/or spectral
analysis, and/or object position or movement), an image sensor, a
structured light sensor, a laser-based image sensor, a thermal
imager, an acoustic wave sensor, a displacement sensor, a turbidity
meter, a viscosity meter, a axial load sensor, a radial load
sensor, a tri-axial sensor, an accelerometer, a speedometer, a
tachometer, a fluid pressure meter, an air flow meter, a horsepower
meter, a flow rate meter, a fluid particle detector, an optical
(laser) particle counter, an ultrasonic sensor, an acoustical
sensor, a heat flux sensor, a galvanic sensor, a magnetometer, a pH
sensor, and the like, including, without limitation, any of the
sensors described throughout this disclosure and the documents
incorporated by reference.
[0673] The sensors 9706 may provide a stream of data over time that
has a phase component, such as relating to acceleration or
vibration, allowing for the evaluation of phase or frequency
analysis of different operational aspects of a piece of equipment
or an operating component. The sensors 9706 may provide a stream of
data that is not conventionally phase-based, such as temperature,
humidity, load, and the like. The sensors 9706 may provide a
continuous or near continuous stream of data over time, periodic
readings, event-driven readings, and/or readings according to a
selected interval or schedule.
[0674] Methods and systems are disclosed herein for a system for
data collection in an industrial environment using intelligent
management of data collection bands, referred to herein in some
cases as smart bands. Smart bands may facilitate intelligent,
situational, context-aware collection of data, such as by a data
collector (such as any of the wide range of data collector
embodiments described throughout this disclosure). Intelligent
management of data collection via smart bands may improve various
parameters of data collection, as well as parameters of the
processes, applications, and products that depend on data
collection, such as data quality parameters, consistency
parameters, efficiency parameters, comprehensiveness parameters,
reliability parameters, effectiveness parameters, storage
utilization parameters, yield parameters (including financial
yield, output yield, and reduction of adverse events), energy
consumption parameters, bandwidth utilization parameters,
input/output speed parameters, redundancy parameters, security
parameters, safety parameters, interference parameters,
signal-to-noise parameters, statistical relevancy parameters, and
others. Intelligent management of smart bands may optimize across
one or more such parameters, such as based on a weighting of the
value of the parameters; for example, a smart band may be managed
to provide a given level of redundancy for critical data, while not
exceeding a specified level of energy usage. This may include using
a variety of optimization techniques described throughout this
disclosure and the documents incorporated herein by reference.
[0675] In embodiments, such methods and systems for intelligent
management of smart bands include an expert system and supporting
technology components, services, processes, modules, applications
and interfaces, for managing the smart bands (collectively referred
to in some cases as a smart band platform 10722), which may include
a model-based expert system, a rule-based expert system, an expert
system using artificial intelligence (such as a machine learning
system, which may include a neural net expert system, a
self-organizing map system, a human-supervised machine learning
system, a state determination system, a classification system, or
other artificial intelligence system), or various hybrids or
combinations of any of the above. References to an expert system
should be understood to encompass utilization of any one of the
foregoing or suitable combinations, except where context indicates
otherwise. Intelligent management may be of data collection of
various types of data (e.g., vibration data, noise data and other
sensor data of the types described throughout this disclosure) for
event detection, state detection, and the like. Intelligent
management may include managing a plurality of smart bands each
directed at supporting an identified application, process or
workflow, such as confirming progress toward or alignment with one
or more objectives, goals, rules, policies, or guidelines.
Intelligent management may also involve managing data collection
bands targeted to backing out an unknown variable based on
collection of other data (such as based on a model of the behavior
of a system that involves the variable), selecting preferred inputs
among available inputs (including specifying combinations, fusions,
or multiplexing of inputs), and/or specifying an input band among
available input bands.
[0676] Data collection bands, or smart bands, may include any
number of items such as sensors, input channels, data locations,
data streams, data protocols, data extraction techniques, data
transformation techniques, data loading techniques, data types,
frequency of sampling, placement of sensors, static data points,
metadata, fusion of data, multiplexing of data, and the like as
described herein. Smart band settings, which may be used
interchangeably with smart band and data collection band, may
describe the configuration and makeup of the smart band, such as by
specifying the parameters that define the smart band. For example,
data collection bands, or smart bands, may include one or more
frequencies to measure. Frequency data may further include at least
one of a group of spectral peaks, a true-peak level, a crest factor
derived from a time waveform, and an overall waveform derived from
a vibration envelope, as well as other signal characteristics
described throughout this disclosure. Smart bands may include
sensors measuring or data regarding one or more wavelengths, one or
more spectra, and/or one or more types of data from various sensors
and metadata. Smart bands may include one or more sensors or types
of sensors of a wide range of types, such as described throughout
this disclosure and the documents incorporated by reference herein.
Indeed, the sensors described herein may be used in any of the
methods or systems described throughout this disclosure. For
example, one sensor may be an accelerometer, such as one that
measures voltage per G ("V/G") of acceleration (e.g., 100 mV/G, 500
mV/G, 1 V/G, 5 V/G, 10 V/G, and the like). In embodiments, the data
collection band circuit may alter the makeup of the subset of the
plurality of sensors used in a smart band based on optimizing the
responsiveness of the sensor, such as for example choosing an
accelerometer better suited for measuring acceleration of a low
speed mixer versus one better suited for measuring acceleration of
a high speed industrial centrifuge. Choosing may be done
intelligently, such as for example with a proximity probe and
multiple accelerometers disposed on a centrifuge where while at low
speed, one accelerometer is used for measuring in the smart band
and another is used at high speeds. Accelerometers come in various
types, such as piezo-electric crystal, low frequency (e.g., 10
V/G), high speed compressors (10 MV/G), MEMS, and the like. In
another example, one sensor may be a proximity probe which can be
used for sleeve or tilt-pad bearings (e.g., oil bath), or a
velocity probe. In yet another example, one sensor may be a
solid-state relay (SSR) that is structured to automatically
interface with a routed data collector (such as a mobile or
portable data collector) to obtain or deliver data. In another
example, a mobile or portable data collector may be routed to alter
the makeup of the plurality of available sensors, such as by
bringing an appropriate accelerometer to a point of sensing, such
as on or near a component of a machine. In still another example,
one sensor may be a triax probe (e.g., a 100 MV/G triax probe),
that in embodiments is used for portable data collection. In some
embodiments, of a triax probe, a vertical element on one axis of
the probe may have a high frequency response while the ones mounted
horizontally may influence the frequency response of the whole
triax. In another example, one sensor may be a temperature sensor
and may include a probe with a temperature sensor built inside,
such as to obtain a bearing temperature. In still additional
examples, sensors may be ultrasonic, microphone, touch, capacitive,
vibration, acoustic, pressure, strain gauges, thermographic (e.g.,
camera), imaging (e.g., camera, laser, IR, structured light), a
field detector, an EMF meter to measure an AC electromagnetic
field, a gaussmeter, a motion detector, a chemical detector, a gas
detector, a CBRNE detector, a vibration transducer, a magnetometer,
positional, location-based, a velocity sensor, a displacement
sensor, a tachometer, a flow sensor, a level sensor, a proximity
sensor, a pH sensor, a hygrometer/moisture sensor, a densitometric
sensor, an anemometer, a viscometer, or any analog industrial
sensor and/or digital industrial sensor. In a further example,
sensors may be directed at detecting or measuring ambient noise,
such as a sound sensor or microphone, an ultrasound sensor, an
acoustic wave sensor, and an optical vibration sensor (e.g., using
a camera to see oscillations that produce noise). In still another
example, one sensor may be a motion detector.
[0677] Data collection bands, or smart bands, may be of or may be
configured to encompass one or more frequencies, wavelengths, or
spectra for particular sensors, for particular groups of sensors,
or for combined signals from multiple sensors (such as involving
multiplexing or sensor fusion).
[0678] Data collection bands, or smart bands, may be of or may be
configured to encompass one or more sensors or sensor data
(including groups of sensors and combined signals) from one or more
pieces of equipment/components, areas of an installation, disparate
but interconnected areas of an installation (e.g., a machine
assembly line and a boiler room used to power the line), or
locations (e.g., a building in Cambridge and a building in Boston).
Smart band settings, configurations, instructions, or
specifications (collectively referred to herein using any one of
those terms) may include where to place a sensor, how frequently to
sample a data point or points, the granularity at which a sample is
taken (e.g., a number of sampling points per fraction of a second),
which sensor of a set of redundant sensors to sample, an average
sampling protocol for redundant sensors, and any other aspect that
would affect data acquisition.
[0679] Within the smart band platform 10722, an expert system,
which may comprise a neural net, a model-based system, a rule-based
system, a machine learning data analysis circuit, and/or a hybrid
of any of those, may begin iteration towards convergence on a smart
band that is optimized for a particular goal or outcome, such as
predicting and managing performance, health, or other
characteristics of a piece of equipment, a component, or a system
of equipment or components. Based on continuous or periodic
analysis of sensor data, as patterns/trends are identified, or
outliers appear, or a group of sensor readings begin to change,
etc., the expert system may modify its data collection bands
intelligently. This may occur by triggering a rule that reflects a
model or understanding of system behavior (e.g., recognizing a
shift in operating mode that calls for different sensors as
velocity of a shaft increases) or it may occur under control of a
neural net (either in combination with a rule-based approach or on
its own), where inputs are provided such that the neural net over
time learns to select appropriate collection modes based on
feedback as to successful outcomes (e.g., successful classification
of the state of a system, successful prediction, successful
operation relative to a metric, or the like). For example, when a
new pressure reactor is installed in a chemical processing
facility, data from the current data collection band may not
accurately predict the state or metric of operation of the system,
thus, the machine learning data analysis circuit may begin to
iterate to determine if a new data collection band is better at
predicting a state. Based on offset system data, such as from a
library or other data structure, certain sensors, frequency bands
or other smart band members may be used in the smart band initially
and data may be collected to assess performance. As the neural net
iterates, other sensors/frequency bands may be accessed to
determine their relative weight in identifying performance metrics.
Over time, a new frequency band may be identified (or a new
collection of sensors, a new set of configurations for sensors, or
the like) as a better gauge of performance in the system and the
expert system may modify its data collection band based on this
iteration. For example, perhaps a slightly different or older
associated turbine agitator in a chemical reaction facility dampens
one or more vibration frequencies while a different frequency is of
higher amplitude and present during optimal performance than what
was seen in the offset system. In this example, the smart band may
be altered from what was suggested by the corresponding offset
system to capture the higher amplitude frequency that is present in
the current system.
[0680] The expert system, in embodiments involving a neural net or
other machine learning system, may be seeded and may iterate, such
as towards convergence on a smart band, based on feedback and
operation parameters, such as described herein. Certain feedback
may include utilization measures, efficiency measures (e.g., power
or energy utilization, use of storage, use of bandwidth, use of
input/output use of perishable materials, use of fuel, and/or
financial efficiency), measures of success in prediction or
anticipation of states (e.g., avoidance and mitigation of faults),
productivity measures (e.g., workflow), yield measures, and profit
measures. Certain parameters may include: storage parameters (e.g.,
data storage, fuel storage, storage of inventory and the like);
network parameters (e.g., network bandwidth, input/output speeds,
network utilization, network cost, network speed, network
availability and the like); transmission parameters (e.g., quality
of transmission of data, speed of transmission of data, error rates
in transmission, cost of transmission and the like); security
parameters (e.g., number and/or type of exposure events;
vulnerability to attack, data loss, data breach, access parameters,
and the like); location and positioning parameters (e.g., location
of data collectors, location of workers, location of machines and
equipment, location of inventory units, location of parts and
materials, location of network access points, location of ingress
and egress points, location of landing positions, location of
sensor sets, location of network infrastructure, location of power
sources and the like); input selection parameters, data combination
parameters (e.g., for multiplexing, extraction, transformation,
loading, and the like); power parameters; states (e.g., operating
modes, availability states, environmental states, fault modes,
maintenance modes, anticipated states); events; and equipment
specifications. With respect to states, operating modes may include
mobility modes (direction, speed, acceleration, and the like), type
of mobility modes (e.g., rolling, flying, sliding, levitation,
hovering, floating, and the like), performance modes (e.g., gears,
rotational speeds, heat levels, assembly line speeds, voltage
levels, frequency levels, and the like), output modes, fuel
conversion modes, resource consumption modes, and financial
performance modes (e.g., yield, profitability, and the like).
Availability states may refer to anticipating conditions that could
cause machine to go offline or require backup. Environmental states
may refer to ambient temperature, ambient humidity/moisture,
ambient pressure, ambient wind/fluid flow, presence of pollution or
contaminants, presence of interfering elements (e.g., electrical
noise, vibration), power availability, and power quality.
Anticipated states may include: achieving or not achieving a
desired goal, such as a specified/threshold output production rate,
a specified/threshold generation rate, an operational
efficiency/failure rate, a financial efficiency/profit goal, a
power efficiency/resource utilization; an avoidance of a fault
condition (e.g., overheating, slow performance, excessive speed,
excessive motion, excessive vibration/oscillation, excessive
acceleration, expansion/contraction, electrical failure, running
out of stored power/fuel, overpressure, excessive radiation/melt
down, fire, freezing, failure of fluid flow (e.g., stuck valves,
frozen fluids); mechanical failures (e.g., broken component, worn
component, faulty coupling, misalignment, asymmetries/deflection,
damaged component (e.g., deflection, strain, stress, cracking],
imbalances, collisions, jammed elements, and lost or slipping chain
or belt); avoidance of a dangerous condition or catastrophic
failure; and availability (online status).
[0681] The expert system may comprise or be seeded with a model
that predicts an outcome or state given a set of data (which may
comprise inputs from sensors, such as via a data collector, as well
as other data, such as from system components, from external
systems and from external data sources). For example, the model may
be an operating model for an industrial environment, machine, or
workflow. In another example, the model may be for anticipating
states, for predicting fault and optimizing maintenance, for
self-organizing storage (e.g., on devices, in data pools and/or in
the cloud), for optimizing data transport (such as for optimizing
network coding, network-condition-sensitive routing, and the like),
for optimizing data marketplaces, and the like.
[0682] The iteration of the expert system may result in any number
of downstream actions based on analysis of data from the smart
band. In an embodiment, the expert system may determine that the
system should either keep or modify operational parameters,
equipment or a weighting of a neural net model given a desired
goal, such as a specified/threshold output production rate,
specified/threshold generation rate, an operational
efficiency/failure rate, a financial efficiency/profit goal, a
power efficiency/resource utilization, an avoidance of a fault
condition, an avoidance of a dangerous condition or catastrophic
failure, and the like. In embodiments, the adjustments may be based
on determining context of an industrial system, such as
understanding a type of equipment, its purpose, its typical
operating modes, the functional specifications for the equipment,
the relationship of the equipment to other features of the
environment (including any other systems that provide input to or
take input from the equipment), the presence and role of operators
(including humans and automated control systems), and ambient or
environmental conditions. For example, in order to achieve a profit
goal, a pipeline in a refinery may need to operate for a certain
amount of time a day and/or at a certain flow rate. The expert
system may be seeded with a model for operation of the pipeline in
a manner that results in a specified profit goal, such as
indicating a given flow rate of material through the pipeline based
on the current market sale price for the material and the cost of
getting the material into the pipeline. As it acquires data and
iterates, the model will predict whether the profit goal will be
achieved given the current data. Based on the results of the
iteration of the expert system, a recommendation may be made (or a
control instruction may be automatically provided) to operate the
pipeline at a higher flow rate, to keep it operational for longer
or the like. Further, as the system iterates, one or more
additional sensors may be sampled in the model to determine if
their addition to the smart band would improve predicting a state.
In another embodiment, the expert system may determine that the
system should either keep or modify operational parameters,
equipment or a weighting of a neural net or other model given a
constraint of operation (e.g., meeting a required endpoint (e.g.,
delivery date, amount, cost, coordination with another system),
operating with a limited resource (e.g., power, fuel, battery),
storage (e.g., data storage), bandwidth (e.g., local network, p2p,
WAN, internet bandwidth, availability, or input/output capacity),
authorization (e.g., role-based)), a warranty limitation, a
manufacturer's guideline, a maintenance guideline). For example, a
constraint of operating a boiler in a refinery is that the aeration
of the boiler feedwater needs to be reduced in the cycle;
therefore, the boiler must coordinate with the deaerator. In this
example, the expert system is seeded with a model for operation of
the boiler in coordination with the de-aerator that results in a
specified overall performance. As sensor data from the system is
acquired, the expert system may determine that an aspect of one or
both of the boiler and aerator must be changed to continue to
achieve the specific overall performance. In a further embodiment,
the expert system may determine that the system should either keep
or modify operational parameters, equipment or a weighting of a
neural net model given an identified choke point. In still another
embodiment, the expert system may determine that the system should
either keep or modify operational parameters, equipment or a
weighting of a neural net model given an off-nominal operation. For
example, a reciprocating compressor in a refinery that delivers
gases at high pressure may be measured as having an off-nominal
operation by sensors that feed their data into an expert system
(optionally including a neural net or other machine learning
system). As the expert system iterates and receives the off-nominal
data, it may predict that the refinery will not achieve a specified
goal and will recommend an action, such as taking the reciprocating
compressor offline for maintenance. In another embodiment, the
expert system may determine that the system should collect
more/fewer data points from one or more sensors. For example, an
anchor agitator in a pharmaceutical processing plant may be
programmed to agitate the contents of a tank until a certain level
of viscosity (e.g., as measured in centipoise) is obtained. As the
expert system collects data throughout the run indicating an
increase in viscosity, the expert system may recommend collecting
additional data points to confirm a predicted state in the face of
the increased strain on the plant systems from the viscosity. In
yet another embodiment, the expert system may determine that the
system should change a data storage technique. In still another
example, the expert system may determine that the system should
change a data presentation mode or manner. In a further embodiment,
the expert system may determine that the system should apply one or
more filters (low pass, high pass, band pass, etc.) to collected
data. In yet a further embodiment, the expert system may determine
that the system should collect data from a new smart band/new set
of sensors and/or begin measuring a new aspect that the neural net
identified itself. For example, various measurements may be made of
paddle-type agitator mixers operating in a pharmaceutical plant,
such as mixing times, temperature, homogeneous substrate
distribution, heat exchange with internal structures and the tank
wall or oxygen transfer rate, mechanical stress, forces and torques
on agitator vessels and internal structures, and the like. Various
sensor data streams may be included in a smart band monitoring
these various aspects of the paddle-type agitator mixer, such as a
flow meter, a thermometer, and others. As the expert system
iterates, perhaps having been seeded with minimal data from during
the agitator's run, a new aspect of the operation may become
apparent, such as the impact of pH on the state of the run. Thus, a
new smart band will be identified by the expert system that
includes sensor data from a pH meter. In yet still a further
embodiment, the expert system may determine that the system should
discontinue collection of data from a smart band, one or more
sensors, or the like. In another embodiment, the expert system may
determine that the system should initiate data collection from a
new smart band, such as a new smart band identified by the neural
net itself. In yet another embodiment, the expert system may
determine that the system should adjust the weights/biases of a
model used by the expert system. In still another embodiment, the
expert system may determine that the system should remove/re-task
under-utilized equipment. For example, a plurality of agitators
working with a pump blasting liquid in a pharmaceutical processing
plant may be monitored during operation of the plant by the expert
system. Through iteration of the expert system seeded with data
from a run of the plant with the agitators, the expert system may
predict that a state will be achieved even if one or more agitators
are taken out of service.
[0683] In embodiments, a monitoring system for data collection in
an industrial environment may include a plurality of input sensors,
such as any of those described herein, communicatively coupled to a
data collector having a controller. The monitoring system may
include a data collection band circuit structured to determine at
least one subset of the plurality of sensors from which to process
output data. The monitoring system may also include a machine
learning data analysis circuit structured to receive output data
from the at least one subset of the plurality of sensors and learn
received output data patterns indicative of a state. In some
embodiments, the data collection band circuit may alter the at
least one subset of the plurality of sensors, or an aspect thereof,
based on one or more of the learned received output data patterns
and the state. In certain embodiments, the machine learning data
analysis circuit is seeded with a model that enables it to learn
data patterns. The model may be a physical model, an operational
model, a system model, and the like. In other embodiments, the
machine learning data analysis circuit is structured for deep
learning wherein input data is fed to the circuit with no or
minimal seeding and the machine learning data analysis circuit
learns based on output feedback. For example, a static mixer in a
chemical processing plant producing polymers may be used to
facilitate the polymerization reaction. The static mixer may employ
turbulent or laminar flow in its operation. Minimal data, such as
heat transfer, velocity of flow out of the mixer, Reynolds number
or pressure drop, acquired during the operation of the static mixer
may be fed into the expert system which may iterate towards a
prediction based on initial feedback (e.g., viscosity of the
polymer, color of the polymer, reactivity of the polymer).
[0684] There may be a balance of multiple goals/guidelines in the
management of smart bands by the expert system. For example, a
repair and maintenance organization (RMO) may have operating
parameters designed for maintenance of a storage tank in a
refinery, while the owner of the refinery may have particular
operating parameters for the storage tank that are designed for
meeting a production goal. These goals, in this example relating to
a maintenance goal or a production output, may be tracked by a
different data collection bands. For example, maintenance of a
storage tank may be tracked by sensors including a vibration
transducer and a strain gauge, while the production goal of a
storage tank may be tracked by sensors including a temperature
sensor and a flow meter. The expert system may (optionally using a
neural net, machine learning system, deep learning system, or the
like, which may occur under supervision by one or more supervisors
(human or automated)) intelligently manage bands aligned with
different goals and assign weights, parameter modifications, or
recommendations based on a factor, such as a bias towards one goal
or a compromise to allow better alignment with all goals being
tracked, for example. Compromises among the goals delivered to the
expert system may be based on one or more hierarchies or rules
(relating to the authority, role, criticality, or the like) of the
applicable goals. In embodiments, compromises among goals may be
optimized using machine learning, such as a neural net, deep
learning system, or other artificial intelligence system as
described throughout this disclosure. In one illustrative example,
in a chemical processing plant where a gas-powered agitator is
operating, the expert system may manage multiple smart bands, such
as one directed to detecting the operational status of the
gas-powered agitator, one directed at identifying a probability of
hitting a production goal, and one directed at determining if the
operation of the gas-powered agitator is meeting a fuel efficiency
goal. Each of these smart bands may be populated with different
sensors or data from different sensors (e.g., a vibration
transducer to indicate operational status, a flow meter to indicate
production goal, and a fuel gauge to indicate a fuel efficiency)
whose output data are indicative of an aspect of the particular
goal. Where a single sensor or a set of sensors is helpful for more
than one goal, overlapping smart bands (having some sensors in
common and other sensors not in common) may take input from that
sensor or set of sensors, as managed by the smart band platform
10722. If there are constraints on data collection (such as due to
power limitations, storage limitations, bandwidth limitations,
input/output processing capabilities, or the like), a rule may
indicate that one goal (e.g., a fuel utilization goal or a
pollution reduction goal that is mandated by law or regulation)
takes precedence, such that the data collection for the smart bands
associated with that goal are maintained as others are paused or
shut down. Management of prioritization of goals may be
hierarchical or may occur by machine learning. The expert system
may be seeded with models, or may not be seeded at all, in
iterating towards a predicted state (i.e., meeting the goal) given
the current data it has acquired. In this example, during operation
of the gas-powered agitator, the plant owner may decide to bias the
system towards fuel efficiency. All of the bands may still be
monitored, but as the expert system iterates and predicts that the
system will not meet or is not meeting a particular goal, and then
offers recommended changes directed at increasing the chance of
meeting the goal, the plant owner may structure the system with a
bias towards fuel efficiency so that the recommended changes to
parameters affecting fuel efficiency are made in favor of making
other recommended changes.
[0685] In embodiments, the expert system may continue iterating in
a deep-learning fashion to arrive at a single smart band, after
being seeded with more than one smart band, that optimizes meeting
more than one goal. For example, there may be multiple goals
tracked for a thermic heating system in a chemical processing or a
food processing plant, such as thermal efficiency and economic
efficiency. Thermal efficiency for the thermic heating system may
be expressed by comparing BTUs put in to the system, which can be
obtained by knowing the amount of and quality of the fuel being
used, and the BTUs out of the system, which is calculated using the
flow out of the system and the temperature differential of
materials in and out of the system. Economic efficiency of the
thermic heating system may be expressed as the ratio between costs
to run the system (including fuel, labor, materials, and services)
and energy output from the system for a period of time. Data used
to track thermal efficiency may include data from a flow meter,
quality data point(s), and a thermometer, and data used to track
economic efficiency may be an energy output from the system (e.g.,
kWh) and costs data. These data may be used in smart bands by the
expert system to predict states, however, the expert system may
iterate toward a smart band that is optimized to predict states
related to both thermal and economic efficiency. The new smart band
may include data used previously in the individual smart bands but
may also use new data from different sensors or data sources. In
embodiments, the expert system may be seeded with a plurality of
smart bands and iterate to predict various states, but may also
iterate towards reducing the number of smart bands needed to
predict the same set of states.
[0686] Iteration of the expert system may be governed by rules, in
some embodiments. For example, the expert system may be structured
to collect data for seeding at a pre-determined frequency. The
expert system may be structured to iterate at least a number of
times, such as when a new component/equipment/fuel source is added,
when a sensor goes off-line, or as standard practice. For example,
when a sensor measuring the rotation of a stirrer in a food
processing line goes off-line and the expert system begins
acquiring data from a new sensor measuring the same data points,
the expert system may be structured to iterate for a number of
times before the state is utilized in or allowed to affect any
downstream actions. The expert system may be structured to train
off-line or train in situ/online. The expert system may be
structured to include static and/or manually input data in its
smart bands. For example, an expert system managing smart bands
associated with a mixer in a food processing plant may be
structured to iterate towards predicting a duration of mixing
before the food being processed achieves a particular viscosity,
wherein the smart band includes data regarding the speed of the
mixer, temperature of its contents, viscometric measurements and
the required endpoint for viscosity and temperature of the food.
The expert system may be structured to include a minimum/maximum
number of variables.
[0687] In embodiments, the expert system may be overruled. In
embodiments, the expert system may revert to prior band settings,
such as in the event the expert system fails, such as if a neural
network fails in a neural net expert system, if uncertainty is too
high in a model-based system, if the system is unable to resolve
conflicting rules in rule-based system, or the system cannot
converge on a solution in any of the foregoing. For example, sensor
data on an irrigation system used by the expert system in a smart
band may indicate a massive leak in the field, but visual
inspection, such as by a drone, indicates no such leak. In this
event, the expert system will revert to an original smart band for
seeding the expert system. In another example, one or more point
sensors on an industrial pressure cooker indicates imminent failure
in a seal, but the data collection band that the expert system
converged to with a weighting towards a performance metric did not
identify the failure. In this event, the smart band will revert to
an original setting or a version of the smart band that would have
also identified the imminent failure of the pressure cooker seal.
In embodiments, the expert system may change smart band settings in
the event that a new component is added that makes the system
closer to a different offset system. For example, a vacuum
distillation unit is added to an oil & gas refinery to distill
naphthalene, but the current smart band settings for the expert
system are derived from a refinery that distills kerosene. In this
example, a data structure with smart band settings for various
offset systems may be searched for a system that is more closely
matched to the current system. When a new offset system is
identified as more closely matched, such as one that also distill
naphthalene, the new smart band settings (e.g., which sensors to
use, where to place them, how frequently to sample, what static
data points are needed, etc. as described herein) are used to seed
the expert system to iterate towards predicting a state for the
system. In embodiments, the expert system may change smart band
settings in the event that a new set of offset data is available
from a third-party library. For example, a pharmaceutical
processing plant may have optimized a catalytic reactor to operate
in a highly efficient way and deposited the smart band settings in
a data structure. The data structure may be continuously scanned
for new smart bands that better aid in monitoring catalytic
reactions and thus, result in optimizing the operation of the
reactor.
[0688] In embodiments, the expert system may be used to uncover
unknown variables. For example, the expert system may iterate to
identify a missing variable to be used for further iterations, such
as further neural net iterations. For example, an under-utilized
tank in a legacy condensate/make-up water system of a power station
may have an unknown capacity because it is inaccessible and no
documentation exists on the tank. Various aspects of the tank may
be measured by a swarm of sensors to arrive at an estimated volume
(e.g., flow into a downstream space, duration of a dye traced
solution to work through the system), then that volume can be fed
into the neural net as a new variable in the smart band.
[0689] In embodiments, the location of expert system node locations
may be on a machine, on a data collector (or a group of them), in a
network infrastructure (enterprise or other), or in the cloud. In
embodiments, there may be distributed neurons across nodes (e.g.,
machine, data collector, network, cloud).
[0690] Referring to FIG. 71, in an aspect, a monitoring system
10700 for data collection in an industrial environment, comprising
a plurality of input sensors 10702 communicatively coupled to a
data collector 10704 having a controller 10706, a data collection
band circuit 10708 structured to determine at least one collection
parameter for at least one of the plurality of sensors 10702 from
which to process output data 10710, and a machine learning data
analysis circuit 10712 structured to receive output data 10710 from
the at least one of the plurality of sensors 10702 and learn
received output data patterns 10718 indicative of a state. The data
collection band circuit 10708 alters the at least one collection
parameter for the at least one of the plurality of sensors 10702
based on one or more of the learned received output data patterns
10718 and the state. The state may correspond to an outcome
relating to a machine in the environment, an anticipated outcome
relating to a machine in the environment, an outcome relating to a
process in the environment, an anticipated outcome relating to a
process in the environment, and the like. The collection parameter
may be a bandwidth parameter, may be used to govern the
multiplexing of a plurality of the input sensors, may be a timing
parameter, may relate to a frequency range, may relate to the
granularity of collection of sensor data, is a storage parameter
for the collected data. The machine learning data analysis circuit
may be structured to learn received output data patterns 10718 by
being seeded with a model 10720, which may be a physical model, an
operational model, or a system model. The machine learning data
analysis circuit may be structured to learn received output data
patterns 10718 based on the state. The data collection band circuit
may alter the subset of the plurality of sensors when the learned
received output data pattern does not reliably predict the state,
which may include discontinuing collection of data from the at
least one subset.
[0691] The monitoring system 10700 may keep or modify operational
parameters of an item of equipment in the environment based on the
determined state. The controller 10706 may adjust the weighting of
the machine learning data analysis circuit 10712 based on the
learned received output data patterns 10718 or the state. The
controller 10706 may collect more/fewer data points from one or
more members of the at least one subset of plurality of sensors
10702 based on the learned received output data patterns 10718 or
the state. The controller 10706 may change a data storage technique
for the output data 10710 based on the learned received output data
patterns 10718 or the state. The controller 10706 may change a data
presentation mode or manner based on the learned received output
data patterns 10718 or the state. The controller 10706 may apply
one or more filters to the output data 10710. The controller 10706
may identify a new data collection band circuit 10708 based on one
or more of the learned received output data patterns 10718 and the
state. The controller 10706 may adjust the weights/biases of the
machine learning data analysis circuit 10712, such as in response
to the learned received output data patterns 10718, in response to
the accuracy of the prediction of an anticipated state by the
machine learning data analysis circuit, in response to the accuracy
of a classification of a state by the machine learning data
analysis circuit, and the like. The monitoring device 10700 may
remove or re-task under-utilized equipment based on one or more of
the learned received output data patterns 10718 and the state. The
machine learning data analysis circuit 10712 may include a neural
network expert system. At least one subset of the plurality of
sensors measures vibration and noise data. The machine learning
data analysis circuit 10712 may be structured to learn received
output data patterns 10718 indicative of progress/alignment with
one or more goals/guidelines, wherein progress/alignment of each
goal/guideline may be determined by a different subset of the
plurality of sensors. The machine learning data analysis circuit
10712 may be structured to learn received output data patterns
10718 indicative of an unknown variable. The machine learning data
analysis circuit 10712 may be structured to learn received output
data patterns 10718 indicative of a preferred input among available
inputs. The machine learning data analysis circuit 10712 may be
structured to learn received output data patterns 10718 indicative
of a preferred input data collection band among available input
data collection bands. The machine learning data analysis circuit
10712 may be disposed in part on a machine, on one or more data
collectors, in network infrastructure, in the cloud, or any
combination thereof.
[0692] In embodiments , a monitoring device for data collection in
an industrial environment may include a plurality of input sensors
10702 communicatively coupled to a controller 10706, the controller
10706 including a data collection band circuit 10708 structured to
determine at least one subset of the plurality of sensors 10702
from which to process output data 10710; and a machine learning
data analysis circuit 10712 structured to receive output data from
the at least one subset of the plurality of sensors 10702 and learn
received output data patterns 10718 indicative of a state, wherein
the data collection band circuit 10708 alters an aspect of the at
least one subset of the plurality of sensors 10702 based on one or
more of the learned received output data patterns 10718 and the
state. The aspect that the data collection band circuit 10708
alters is a number or a frequency of data points collected from one
or more members of the at least one subset of plurality of sensors
10702. The aspect that the data collection band circuit 10708
alters is a bandwidth parameter, a timing parameter, a frequency
range, a granularity of collection of sensor data, a storage
parameter for the collected data, and the like.
[0693] In an embodiment, a monitoring system 10700 for data
collection in an industrial environment may include a plurality of
input sensors 10702 communicatively coupled to a data collector
10704 having a controller 10706, a data collection band circuit
10708 structured to determine at least one collection parameter for
at least one of the plurality of sensors 10702 from which to
process output data 10710, and a machine learning data analysis
circuit 10712 structured to receive output data 10710 from the at
least one of the plurality of sensors 10702 and learn received
output data patterns indicative of a state, wherein the data
collection band circuit 10708 alters the at least one collection
parameter for the at least one of the plurality of sensors 10702
based on one or more of the learned received output data patterns
10718 and the state, and wherein the data collection band circuit
10708 alters the at least one of the plurality of sensors 10702
when the learned received output data pattern 10718 does not
reliably predict the state.
[0694] In an embodiment, a monitoring system 10700 for data
collection in an industrial environment may include a plurality of
input sensors 10702 communicatively coupled to a data collector
10704 having a controller 10706, a data collection band circuit
10708 structured to determine at least one collection parameter for
at least one of the plurality of sensors 10702 from which to
process output data 10710, and a machine learning data analysis
circuit 10712 structured to receive output data 10710 from the at
least one of the plurality of sensors 10702 and learn received
output data patterns 10718 indicative of a state, wherein the data
collection band circuit 10708 alters the at least one collection
parameter for the at least one of the plurality of sensors 10702
based on one or more of the learned received output data patterns
10718 and the state, and wherein the data collector 10704 collects
more or fewer data points from the at least one of the plurality of
sensors 10702 based on the learned received output data patterns
10718 or the state.
[0695] In an embodiment, a monitoring system 10700 for data
collection in an industrial environment may include a plurality of
input sensors 10702 communicatively coupled to a data collector
10704 having a controller 10706, a data collection band circuit
10708 structured to determine at least one collection parameter for
at least one of the plurality of sensors 10702 from which to
process output data 10710, and a machine learning data analysis
circuit 10712 structured to receive output data 10710 from the at
least one of the plurality of sensors 10702 and learn received
output data 10710 patterns indicative of a state, wherein the data
collection band circuit 10708 alters the at least one collection
parameter for the at least one of the plurality of sensors 10702
based on one or more of the learned received output data patterns
10718 and the state, and wherein the controller 10706 changes a
data storage technique for the output data 10710 based on the
learned received output data patterns 10718 or the state.
[0696] In an embodiment, a monitoring system 10700 for data
collection in an industrial environment may include a plurality of
input sensors 10702 communicatively coupled to a data collector
10704 having a controller 10706, a data collection band circuit
10708 structured to determine at least one collection parameter for
at least one of the plurality of sensors 10702 from which to
process output data 10710, and a machine learning data analysis
circuit 10712 structured to receive output data 10710 from the at
least one of the plurality of sensors 10702 and learn received
output data patterns 10718 indicative of a state, wherein the data
collection band circuit 10708 alters the at least one collection
parameter for the at least one of the plurality of sensors 10702
based on one or more of the learned received output data patterns
10718 and the state, and wherein the controller 10706 changes a
data presentation mode or manner based on the learned received
output data patterns 10718 or the state.
[0697] In an embodiment, a monitoring system 10700 for data
collection in an industrial environment may include a plurality of
input sensors 10702 communicatively coupled to a data collector
10704 having a controller 10706, a data collection band circuit
10708 structured to determine at least one collection parameter for
at least one of the plurality of sensors 10702 from which to
process output data 10710, and a machine learning data analysis
circuit 10712 structured to receive output data 10710 from the at
least one of the plurality of sensors 10702 and learn received
output data patterns 10718 indicative of a state, wherein the data
collection band circuit 10708 alters the at least one collection
parameter for the at least one of the plurality of sensors 10702
based on one or more of the learned received output data patterns
10718 and the state, and wherein the controller 10706 identifies a
new data collection band circuit 10708 based on one or more of the
learned received output data patterns 10718 and the state.
[0698] In an embodiment, a monitoring system 10700 for data
collection in an industrial environment may include a plurality of
input sensors 10702 communicatively coupled to a data collector
10704 having a controller 10706, a data collection band circuit
10708 structured to determine at least one collection parameter for
at least one of the plurality of sensors 10702 from which to
process output data 10710, and a machine learning data analysis
circuit 10712 structured to receive output data 10710 from the at
least one of the plurality of sensors 10702 and learn received
output data patterns 10718 indicative of a state, wherein the data
collection band circuit 10708 alters the at least one collection
parameter for the at least one of the plurality of sensors 10702
based on one or more of the learned received output data patterns
10718 and the state, and wherein the controller 10706 adjusts the
weights/biases of the machine learning data analysis circuit 10712.
The adjustment may be in response to the learned received output
data patterns, in response to the accuracy of the prediction of an
anticipated state by the machine learning data analysis circuit, in
response to the accuracy of a classification of a state by the
machine learning data analysis circuit, and the like.
[0699] In an embodiment, a monitoring system 10700 for data
collection in an industrial environment may include a plurality of
input sensors 10702 communicatively coupled to a data collector
10704 having a controller 10706, a data collection band circuit
10708 structured to determine at least one collection parameter for
at least one of the plurality of sensors 10702 from which to
process output data 10710, and a machine learning data analysis
circuit 10712. This machine learning data analysis circuit is
structured to receive output data 10710 from the at least one of
the plurality of sensors 10702 and learn received output data
patterns 10718 indicative of a state, wherein the data collection
band circuit 10708 alters the at least one collection parameter for
the at least one of the plurality of sensors 10702 based on one or
more of the learned received output data patterns 10718 and the
state, and wherein the machine learning data analysis circuit 10712
is structured to learn received output data patterns 10718
indicative of progress or alignment with one or more goals or
guidelines. [0700] Clause 1. In embodiments, a monitoring system
for data collection in an industrial environment, comprising: a
plurality of input sensors communicatively coupled to a data
collector having a controller; a data collection band circuit
structured to determine at least one collection parameter for at
least one of the plurality of sensors from which to process output
data; and a machine learning data analysis circuit structured to
receive output data from the at least one of the plurality of
sensors and learn received output data patterns indicative of a
state, wherein the data collection band circuit alters the at least
one collection parameter for the at least one of the plurality of
sensors based on one or more of the learned received output data
patterns and the state. 2. The system of clause 1, wherein the
state corresponds to an outcome relating to a machine in the
environment. 3. The system of clause 1, wherein the state
corresponds to an anticipated outcome relating to a machine in the
environment. 4. The system of clause 1, wherein the state
corresponds to an outcome relating to a process in the environment.
5. The system of clause 1, wherein the state corresponds to an
anticipated outcome relating to a process in the environment. 6.
The system of clause 1, wherein the collection parameter is a
bandwidth parameter. 7. The system of clause 1, wherein the
collection parameter is used to govern the multiplexing of a
plurality of the input sensors. 8. The system of clause 1, wherein
the collection parameter is a timing parameter. 9. The system of
clause 1, wherein the collection parameter relates to a frequency
range. 10. The system of clause 1, wherein the collection parameter
relates to the granularity of collection of sensor data. 11. The
system of clause 1, wherein the collection parameter is a storage
parameter for the collected data. 12. The system of clause 1,
wherein the machine learning data analysis circuit is structured to
learn received output data patterns by being seeded with a model.
13. The system of clause 12, wherein the model is a physical model,
an operational model, or a system model. 14. The system of clause
1, wherein the machine learning data analysis circuit is structured
to learn received output data patterns based on the state. 15. The
system of clause 1, wherein the data collection band circuit alters
the subset of the plurality of sensors when the learned received
output data pattern does not reliably predict the state. 16. The
system of clause 15, wherein altering at least one subset comprises
discontinuing collection of data from the at least one subset. 17.
The system of clause 1, wherein the monitoring system keeps or
modifies operational parameters of an item of equipment in the
environment based on the determined state. 18. The system of clause
1, wherein the controller adjusts the weighting of the machine
learning data analysis circuit based on the learned received output
data patterns or the state. 19. The system of clause 1, wherein the
controller collects more or fewer data points from one or more
members of the at least one subset of plurality of sensors based on
the learned received output data patterns or the state. 20. The
system of clause 1, wherein the controller changes a data storage
technique for the output data based on the learned received output
data patterns or the state. 21. The system of clause 1, wherein the
controller changes a data presentation mode or manner based on the
learned received output data patterns or the state. 22. The system
of clause 1, wherein the controller applies one or more filters to
the output data. 23. The system of clause 1, wherein the controller
identifies a new data collection band circuit based on one or more
of the learned received output data patterns and the state. 24. The
system of clause 1, wherein the controller adjusts the
weights/biases of the machine learning data analysis circuit. 25.
The system of clause 24, wherein the adjustment is in response to
the learned received output data patterns. 26. The system of clause
24, wherein the adjustment is in response to the accuracy of the
prediction of an anticipated state by the machine learning data
analysis circuit. 27. The system of clause 24, wherein the
adjustment is in response to the accuracy of a classification of a
state by the machine learning data analysis circuit. 28. The system
of clause 1, wherein the monitoring device removes/re-tasks
under-utilized equipment based on one or more of the learned
received output data patterns and the state. 29. The system of
clause 1, wherein the machine learning data analysis circuit
comprises a neural network expert system. 30. The system of clause
1, wherein the at least one subset of the plurality of sensors
measure vibration and noise data. 31. The system of clause 1,
wherein the machine learning data analysis circuit is structured to
learn received output data patterns indicative of
progress/alignment with one or more goals/guidelines. 32. The
system of clause 31, wherein progress/alignment of each
goal/guideline is determined by a different subset of the plurality
of sensors. 33. The system of clause 1, wherein the machine
learning data analysis circuit is structured to learn received
output data patterns indicative of an unknown variable. 34. The
system of clause 1, wherein the machine learning data analysis
circuit is structured to learn received output data patterns
indicative of a preferred input among available inputs. 35. The
system of clause 1, wherein the machine learning data analysis
circuit is structured to learn received output data patterns
indicative of a preferred input data collection band among
available input data collection bands. 36. The system of clause 1,
wherein the machine learning data analysis circuit is disposed in
part on a machine, on one or more data collectors, in network
infrastructure, in the cloud, or any combination thereof 37. A
monitoring device for data collection in an industrial environment,
comprising: a plurality of input sensors communicatively coupled to
a controller, the controller comprising: a data collection band
circuit structured to determine at least one subset of the
plurality of sensors from which to process output data; and a
machine learning data analysis circuit structured to receive output
data from the at least one subset of the plurality of sensors and
learn received output data patterns indicative of a state, wherein
the data collection band circuit alters an aspect of the at least
one subset of the plurality of sensors based on one or more of the
learned received output data patterns and the state. 38. The system
of clause 37, wherein the aspect that the data collection band
circuit alters is a number of data points collected from one or
more members of the at least one subset of plurality of sensors.
39. The system of clause 37, wherein the aspect that the data
collection band circuit alters is a frequency of data points
collected from one or more members of the at least one subset of
plurality of sensors. 40. The system of clause 37, wherein the
aspect that the data collection band circuit alters is a bandwidth
parameter. 41. The system of clause 37, wherein the aspect that the
data collection band circuit alters is a timing parameter. 42. The
system of clause 37, wherein the aspect that the data collection
band circuit alters relates to a frequency range. 43. The system of
clause 37, wherein the aspect that the data collection band circuit
alters relates to the granularity of collection of sensor data. 44.
The system of clause 37, wherein the collection parameter is a
storage parameter for the collected data. 45. A monitoring system
for data collection in an industrial environment, comprising: a
plurality of input sensors communicatively coupled to a data
collector having a controller; a data collection band circuit
structured to determine at least one collection parameter for at
least one of the plurality of sensors from which to process output
data; and a machine learning data analysis circuit structured to
receive output data from the at least one of the plurality of
sensors and learn received output data patterns indicative of a
state, wherein the data collection band circuit alters the at least
one collection parameter for the at least one of the plurality of
sensors based on one or more of the learned received output data
patterns and the state, and wherein the data collection band
circuit alters the at least one of the plurality of sensors when
the learned received output data pattern does not reliably predict
the state. 46. A monitoring system for data collection in an
industrial environment, comprising: a plurality of input sensors
communicatively coupled to a data collector having a controller; a
data collection band circuit structured to determine at least one
collection parameter for at least one of the plurality of sensors
from which to process output data; and a machine learning data
analysis circuit structured to receive output data from the at
least one of the plurality of sensors and learn received output
data patterns indicative of a state, wherein the data collection
band circuit alters the at least one collection parameter for the
at least one of the plurality of sensors based on one or more of
the learned received output data patterns and the state, and
wherein the data collector collects more or fewer data points from
the at least one of the plurality of sensors based on the learned
received output data patterns or the state. 47. A monitoring system
for data collection in an industrial environment, comprising: a
plurality of input sensors communicatively coupled to a data
collector having a controller; a data collection band circuit
structured to determine at least one collection parameter for at
least one of the plurality of sensors from which to process output
data; and a machine learning data analysis circuit structured to
receive output data from the at least one of the plurality of
sensors and learn received output data patterns indicative of a
state, wherein the data collection band circuit alters the at least
one collection parameter for the at least one of the plurality of
sensors based on one or more of the learned received output data
patterns and the state, and wherein the controller changes a data
storage technique for the output data based on the learned received
output data patterns or the state. 48. A monitoring system for data
collection in an industrial environment, comprising: a plurality of
input sensors communicatively coupled to a data collector having a
controller; a data collection band circuit structured to determine
at least one collection parameter for at least one of the plurality
of sensors from which to process output data; and a machine
learning data analysis circuit structured to receive output data
from the at least one of the plurality of sensors and learn
received output data patterns indicative of a state, wherein the
data collection band circuit alters the at least one collection
parameter for the at least one of the plurality of sensors based on
one or more of the learned received output data patterns and the
state, and wherein the controller changes a data presentation mode
or manner based on the learned received output data patterns or the
state. 49. A monitoring system for data collection in an industrial
environment, comprising: a plurality of input sensors
communicatively coupled to a data collector having a controller; a
data collection band circuit structured to determine at least one
collection parameter for at least one of the plurality of sensors
from which to process output data; and a machine learning data
analysis circuit structured to receive output data from the at
least one of the plurality of sensors and learn received output
data patterns indicative of a state, wherein the data collection
band circuit alters the at least one collection parameter for the
at least one of the plurality of sensors based on one or more of
the learned received output data patterns and the state, and
wherein the controller identifies a new data collection band
circuit based on one or more of the learned received output data
patterns and the state. 50. A monitoring system for data collection
in an industrial environment, comprising: a plurality of input
sensors communicatively coupled to a data collector having a
controller; a data collection band circuit structured to determine
at least one collection parameter for at least one of the plurality
of sensors from which to process output data; and a machine
learning data analysis circuit structured to receive output data
from the at least one of the plurality of sensors and learn
received output data patterns indicative of a state, wherein the
data collection band circuit alters the at least one collection
parameter for the at least one of the plurality of sensors based on
one or more of the learned received output data patterns and the
state, and wherein the controller adjusts the weights/biases of the
machine learning data analysis circuit. 51. The system of clause
50, wherein the adjustment is in response to the learned received
output data patterns. 52. The system of clause 50, wherein the
adjustment is in response to the accuracy of the prediction of an
anticipated state by the machine learning data analysis circuit.
53. The system of clause 50, wherein the adjustment is in response
to the accuracy of a classification of a state by the machine
learning data analysis circuit. 54. A monitoring system for data
collection in an industrial environment, comprising: a plurality of
input sensors communicatively coupled to a data collector having a
controller; a data collection band circuit structured to determine
at least one collection parameter for at least one of the plurality
of sensors from which to process output data; and a machine
learning data analysis circuit structured to receive output data
from the at least one of the plurality of sensors and learn
received output data patterns indicative of a state, wherein the
data collection band circuit alters the at least one collection
parameter for the at least one of the plurality of sensors based on
one or more of the learned received output data patterns and the
state, and wherein the machine learning data analysis circuit is
structured to learn received output data patterns indicative of
progress or alignment with one or more goals or guidelines.
[0701] As described elsewhere herein, an expert system in an
industrial environment may use sensor data to make predictions
about outcomes or states of the environment or items in the
environment. Data collection may be of various types of data (e.g.,
vibration data, noise data and other sensor data of the types
described throughout this disclosure) for event detection, state
detection, and the like. For example, the expert system may utilize
ambient noise, or the overall sound environment of the area and/or
overall vibration of the device of interest, optionally in
conjunction with other sensor data, in detecting or predicting
events or states. For example, a reciprocating compressor in a
refinery, which may generate its own vibration, may also have an
ambient vibration through contact with other aspects of the
system.
[0702] In embodiments, all three types of noise (ambient noise,
local noise and vibration noise) including various subsets thereof
and combinations with other types of data, may be organized into
large data sets, along with measured results, that are processed by
a "deep learning" machine/expert system that learns to predict one
or more states (e.g., maintenance, failure, or operational) or
overall outcomes, such as by learning from human supervision or
from other feedback, such as feedback from one or more of the
systems described throughout this disclosure and the documents
incorporated by reference herein.
[0703] Throughout this disclosure, various examples will involve
machines, components, equipment, assemblies, and the like, and it
should be understood that the disclosure could apply to any of the
aforementioned. Elements of these machines operating in an
industrial environment (e.g., rotating elements, reciprocating
elements, swinging elements, flexing elements, flowing elements,
suspending elements, floating elements, bouncing elements, bearing
elements, etc.) may generate vibrations that may be of a specific
frequency and/or amplitude typical of the element when the element
is in a given operating condition or state (e.g., a normal mode of
operation of a machine at a given speed, in a given gear, or the
like). Changes in a parameter of the vibration may be indicative or
predictive of a state or outcome of the machine. Various sensors
may be useful in measuring vibration, such as accelerometers,
velocity transducers, imaging sensors, acoustic sensors, and
displacement probes, which may collectively be known as vibration
sensors. Vibration sensors may be mounted to the machine, such as
permanently or temporarily (e.g., adhesive, hook-and-loop, or
magnetic attachment), or may be disposed on a mobile or portable
data collector. Sensed conditions may be compared to historical
data to identify or predict a state, condition or outcome. Typical
faults that can be identified using vibration analysis include:
machine out of balance, machine out of alignment, resonance, bent
shafts, gear mesh disturbances, blade pass disturbances, vane pass
disturbances, recirculation & cavitation, motor faults (rotor
& stator), bearing failures, mechanical looseness, critical
machine speeds, and the like, as well as excessive friction, clutch
slipping, belt problems, suspension and shock absorption problems,
valve and other fluid leaks, under-pressure states in lubrication
and other fluid systems, overheating (such as due to many of the
above), blockage or freezing of engagement of mechanical systems,
interference effects, and other faults described throughout this
disclosure and in the documents incorporated by reference.
[0704] Given that machines are frequently found adjacent to or
working in concert with other machinery, measuring the vibration of
the machine may be complicated by the presence of various noise
components in the environment or associated vibrations that the
machine may be subjected to. Indeed, the ambient and/or local
environment may have its own vibration and/or noise pattern that
may be known. In embodiments, the combination of vibration data
with ambient and/or local noise or other ambient sensed conditions
may form its own pattern, as will be further described herein.
[0705] In embodiments, measuring vibration noise may involve one or
more vibration sensors on or in a machine to measure vibration
noise of the machine that occurs continuously or periodically.
Analysis of the vibration noise may be performed, such as
filtering, signal conditioning, spectral analysis, trend analysis,
and the like. Analysis may be performed on aggregate or individual
sensor measurements to isolate vibration noise of equipment to
obtain a characteristic vibration, vibration pattern or "vibration
fingerprint" of the machine. The vibration fingerprints may be
stored in a data structure, or library, of vibration fingerprints.
The vibration fingerprints may include frequencies, spectra (i.e.,
frequency vs. amplitude), velocities, peak locations, wave peak
shapes, waveform shapes, wave envelope shapes, accelerations, phase
information, phase shifts (including complex phase measurements)
and the like. Vibration fingerprints may be stored in the library
in association with a parameter by which it may be searched or
sorted. The parameters may include a brand or type of
machine/component/equipment, location of sensor(s) attachment or
placement, duty cycle of the equipment/machine, load sharing of the
equipment/machine, dynamic interactions with other devices, RPM,
flow rate, pressure, other vibration driving characteristic,
voltage of line power, age of equipment, time of operation, known
neighboring equipment, associated auxiliary equipment/components,
size of space equipment is in, material of platform for equipment,
heat flux, magnetic fields, electrical fields, currents, voltage,
capacitance, inductance, aspect of a product, and combinations
(e.g., simple ratios) of the same. Vibration fingerprints may be
obtained for machines under normal operation or for other periods
of operation (e.g., off-nominal operation, malfunction, maintenance
needed, faulty component, incorrect parameters of operation, other
conditions, etc.) and can be stored in the library for comparison
to current data. The library of vibration fingerprints may be
stored as indicators with associated predictions, states, outcomes
and/or events. Trend analysis data of measured vibration
fingerprints can indicate time between maintenance events/failure
events.
[0706] In embodiments, vibration noise may be used by the expert
system to confirm the status of a machine, such as a favorable
operation, a production rate, a generation rate, an operational
efficiency, a financial efficiency (e.g., output per cost), a power
efficiency, and the like. In embodiments, the expert system may
make a comparison of the vibration noise with a stored vibration
fingerprint. In other embodiments, the expert system may be seeded
with vibration noise and initial feedback on states and outcomes in
order to learn to predict other states and outcomes. For example, a
center pivot irrigation system may be remotely monitored by
attached vibration sensors to provide a measured vibration noise
that can be compared to a library of vibration fingerprints to
confirm that the system is operating normally. If the system is not
operating normally, the expert system may automatically dispatch a
field crew or drone to investigate. In another example of a vacuum
distillation unit in a refinery, the vibration noise may be
compared, such as by the expert system, to stored vibration
fingerprints in a library to confirm a production rate of diesel.
In a further example, the expert system may be seeded with
vibration noise for a pipeline under conditions of a normal
production rate and as the expert system iterates with current data
(e.g., altered vibration noise, and possibly other altered
parameters), it may predict that the production rate has increased
as caused by the alterations. Measurements may be continually
analyzed in this way to remotely monitor operation.
[0707] In embodiments, vibration noise may be compared, such as by
the expert system, to stored vibration fingerprints and associated
states and outcomes in the library, or alternatively, may be used
to seed an expert system to predict when maintenance is required
(e.g., off-nominal measurement, artifacts in signal, etc.), such as
when vibration noise is matched to a condition when the
equipment/component required maintenance, vibration noise exceeds a
threshold/limit, vibration noise exceeds a threshold/limit or
matches a library vibration fingerprint together with one or more
additional parameters, as described herein. For example, when the
vibration fingerprint from a turbine agitator in a pharmaceutical
processing plant matches a vibration fingerprint for a turbine
agitator when it required a replacement bearing, the expert system
may cause an action to occur, such as immediately shutting down the
agitator or scheduling its shutdown and maintenance.
[0708] In embodiments, vibration noise may be compared, such as by
the expert system, to stored vibration fingerprints and associated
states and outcomes in the library, or alternatively, may be used
to seed an expert system to predict a failure or an imminent
failure. For example, vibration noise from a gas agitator in a
pharmaceutical processing plant may be matched to a condition when
the agitator previously failed or was about to fail. In this
example, the expert system may immediately shut down the agitator,
schedule its shutdown, or cause a backup agitator to come online.
In another example, vibration noise from a pump blasting liquid
agitator in a chemical processing plant may exceed a threshold or
limit and the expert system may cause an investigation into the
cause of the excess vibration noise, shut down the agitator, or the
like. In another example, vibration noise from an anchor agitator
in a pharmaceutical processing plant may exceed a threshold/limit
or match a library vibration fingerprint together with one or more
additional parameters (see parameters herein), such as a decreased
flow rate, increased temperature, or the like. Using vibration
noise taken together with the parameters, the expert system may
more reliably predict the failure or imminent failure.
[0709] In embodiments, vibration noise may be compared, such as by
the expert system, to stored vibration fingerprints and associated
states and outcomes in the library, or alternatively, may be used
to seed an expert system to predict or diagnose a problem (e.g.,
unbalanced, misaligned, worn, or damaged) with the equipment or an
external source contributing vibration noise to the equipment. For
example, when the vibration noise from a paddle-type agitator mixer
matches a vibration fingerprint from a prior imbalance, the expert
system may immediately shut down the mixer.
[0710] In embodiments, when the expert system makes a prediction of
an outcome or state using vibration noise, the expert system may
perform a downstream action, or cause it to be performed.
Downstream actions may include: triggering an alert of a failure,
imminent failure, or maintenance event; shutting down
equipment/component; initiating maintenance/lubrication/alignment;
deploying a field technician; recommending a vibration
absorption/dampening device; modifying a process to utilize backup
equipment/component; modifying a process to preserve
products/reactants, etc.; generating/modifying a maintenance
schedule; coupling the vibration fingerprint with duty cycle of the
equipment, RPM, flow rate, pressure, temperature or other
vibration-driving characteristic to obtain equipment/component
status and generate a report, and the like. For example, vibration
noise for a catalytic reactor in a chemical processing plant may be
matched to a condition when the catalytic reactor required
maintenance. Based on this predicted state of required maintenance,
the expert system may deploy a field technician to perform the
maintenance.
[0711] In embodiments, the library may be updated if a changed
parameter resulted in a new vibration fingerprint, or if a
predicted outcome or state did not occur in the absence of
mitigation. In embodiments, the library may be updated if a
vibration fingerprint was associated with an alternative state than
what was predicted by the library. The update may occur after just
one time that the state that actually occurred did not match the
predicted state from the library. In other embodiments, it may
occur after a threshold number of times. In embodiments, the
library may be updated to apply one or more rules for comparison,
such as rules that govern how many parameters to match along with
the vibration fingerprint, or the standard deviation for the match
in order to accept the predicted outcome.
[0712] In embodiments, vibration noise may be compared, such as by
the expert system, to stored vibration fingerprints and associated
states and outcomes in the library, or alternatively, may be used
to seed an expert system to determine if a change in a system
parameter external or internal to the machine has an effect on its
intrinsic operation. In embodiments, a change in one or more of a
temperature, flow rate, materials in use, duration of use, power
source, installation, or other parameter (see parameters above) may
alter the vibration fingerprint of a machine. For example, in a
pressure reactor in a chemical processing plant, the flow rate and
a reactant may be changed. The changes may alter the vibration
fingerprint of the machine such that the vibration fingerprint
stored in the library for normal operation is no longer
correct.
[0713] Ambient noise, or the overall sound environment of the area
and/or overall vibration of the device of interest, optionally in
conjunction with other ambient sensed conditions, may be used in
detecting or predicting events, outcomes, or states. Ambient noise
may be measured by a microphone, ultrasound sensors, acoustic wave
sensors, optical vibration sensors (e.g., using a camera to see
oscillations that produce noise), or "deep learning" neural
networks involving various sensor arrays that learn, using large
data sets, to identify patterns, sounds types, noise types, etc. In
an embodiment, the ambient sensed condition may relate to motion
detection. For example, the motion may be a platform motion (e.g.,
vehicle, oil platform, suspended platform on land, etc.) or an
object motion (e.g., moving equipment, people, robots, parts (e.g.,
fan blades or turbine blades), etc.). In an embodiment, the ambient
sensed condition may be sensed by imaging, such as to detect a
location and nature of various machines, equipment, and other
objects, such as ones that might impact local vibration. In an
embodiment, the ambient sensed condition may be sensed by thermal
detection and imaging (e.g., for presence of people; presence of
heat sources that may affect performance parameters, etc.). In an
embodiment, the ambient sensed condition may be sensed by field
detection (e.g., electrical, magnetic, etc.). In an embodiment, the
ambient sensed condition may be sensed by chemical detection (e.g.,
smoke, other conditions). Any sensor data may be used by the expert
system to provide an ambient sensed condition for analysis along
with the vibration fingerprint to predict an outcome, event, or
state. For example, an ambient sensed condition near a stirrer or
mixer in a food processing plant may be the operation of a space
heater during winter months, wherein the ambient sensed condition
may include an ambient noise and an ambient temperature.
[0714] In an aspect, local noise may be the noise or vibration
environment which is ambient, but known to be locally generated.
The expert system may filter out ambient noise, employ common mode
noise removal, and/or physically isolate the sensing
environment.
[0715] In embodiments, a system for data collection in an
industrial environment may use ambient, local and vibration noise
for prediction of outcomes, events, and states. A library may be
populated with each of the three noise types for various conditions
(e.g., start up, shut down, normal operation, other periods of
operation as described elsewhere herein). In other embodiments, the
library may be populated with noise patterns representing the
aggregate ambient, local, and/or vibration noise. Analysis (e.g.,
filtering, signal conditioning, spectral analysis, trend analysis)
may be performed on the aggregate noise to obtain a characteristic
noise pattern and identify changes in noise pattern as possible
indicators of a changed condition. A library of noise patterns may
be generated with established vibration fingerprints and local and
ambient noise that can be sorted by a parameter (see parameters
herein), or other parameters/features of the local and ambient
environment (e.g., company type, industry type, products, robotic
handling unit present/not present, operating environment, flow
rates, production rates, brand or type of auxiliary equipment
(e.g., filters, seals, coupled machinery)). The library of noise
patterns may be used by an expert system, such as one with machine
learning capacity, to confirm a status of a machine, predict when
maintenance is required (e.g., off-nominal measurement, artifacts
in signal), predict a failure or an imminent failure,
predict/diagnose a problem, and the like.
[0716] Based on a current noise pattern, the library may be
consulted or used to seed an expert system to predict an outcome,
event, or state based on the noise pattern. Based on the
prediction, the expert system may one or more of trigger an alert
of a failure, imminent failure, or maintenance event, shut down
equipment/component/line, initiate
maintenance/lubrication/alignment, deploy a field technician,
recommend a vibration absorption/dampening device, modify a process
to utilize backup equipment/component, modify a process to preserve
products/reactants, etc., generate/modify a maintenance schedule,
or the like.
[0717] For example, a noise pattern for a thermic heating system in
a pharmaceutical plant or cooking system may include local,
ambient, and vibration noise. The ambient noise may be a result of,
for example, various pumps to pump fuel into the system. Local
noise may be a result of a local security camera chirping with
every detection of motion. Vibration noise may result from the
combustion machinery used to heat the thermal fluid. These noise
sources may form a noise pattern which may be associated with a
state of the thermic system. The noise pattern and associated state
may be stored in a library. An expert system used to monitor the
state of the thermic heating system may be seeded with noise
patterns and associated states from the library. As current data
are received into the expert system, it may predict a state based
on having learned noise patterns and associated states.
[0718] In another example, a noise pattern for boiler feed water in
a refinery may include local and ambient noise. The local noise may
be attributed to the operation of, for example, a feed pump feeding
the feed water into a steam drum. The ambient noise may be
attributed to nearby fans. These noise sources may form a noise
pattern which may be associated with a state of the boiler feed
water. The noise pattern and associated state may be stored in a
library. An expert system used to monitor the state of the boiler
may be seeded with noise patterns and associated states from the
library. As current data are received into the expert system, it
may predict a state based on having learned noise patterns and
associated states.
[0719] In yet another example, a noise pattern for a storage tank
in a refinery may include local, ambient, and vibration noise. The
ambient noise may be a result of, for example, a pump that pumps a
product into the tank. Local noise may be a result of a fan
ventilating the tank room. Vibration noise may result from line
noise of a power supply into the storage tank. These noise sources
may form a noise pattern which may be associated with a state of
the storage tank. The noise pattern and associated state may be
stored in a library. An expert system used to monitor the state of
the storage tank may be seeded with noise patterns and associated
states from the library. As current data are received into the
expert system, it may predict a state based on having learned noise
patterns and associated states.
[0720] In another example, a noise pattern for condensate/make-up
water system in a power station may include vibration and ambient
noise. The ambient noise may be attributed to nearby fans. The
vibration noise may be attributed to the operation of the
condenser. These noise sources may form a noise pattern which may
be associated with a state of the condensate/make-up water system.
The noise pattern and associated state may be stored in a library.
An expert system used to monitor the state of the
condensate/make-up water system may be seeded with noise patterns
and associated states from the library. As current data are
received into the expert system, it may predict a state based on
having learned noise patterns and associated states.
[0721] A library of noise patterns may be updated if a changed
parameter resulted in a new noise pattern or if a predicted outcome
or state did not occur in the absence of mitigation of a diagnosed
problem. A library of noise patterns may be updated if a noise
pattern resulted in an alternative state than what was predicted by
the library. The update may occur after just one time that the
state that actually occurred did not match the predicted state from
the library. In other embodiments, it may occur after a threshold
number of times. In embodiments, the library may be updated to
apply one or more rules for comparison, such as rules that govern
how many parameters to match along with the noise pattern, or the
standard deviation for the match in order to accept the predicted
outcome. For example, a baffle may be replaced in a static agitator
in a pharmaceutical processing plant which may result in a changed
noise pattern. In another example, as the seal on a pressure cooker
in a food processing plant ages, the noise pattern associated with
the pressure cooker may change.
[0722] In embodiments, the library of vibration fingerprints, noise
sources and/or noise patterns may be available for subscription.
The libraries may be used in offset systems to improve operation of
the local system. Subscribers may subscribe at any level (e.g.,
component, machinery, installation, etc.) in order to access data
that would normally not be available to them, such as because it is
from a competitor, or is from an installation of the machinery in a
different industry not typically considered. Subscribers may search
on indicators/predictors based on or filtered by system conditions,
or update an indicator/predictor with proprietary data to customize
the library. The library may further include parameters and
metadata auto-generated by deployed sensors throughout an
installation, onboard diagnostic systems and instrumentation and
sensors, ambient sensors in the environment, sensors (e.g.,
inflexible sets) that can be put into place temporarily, such as in
one or more mobile data collectors, sensors that can be put into
place for longer term use, such as being attached to points of
interest on devices or systems, and the like.
[0723] In embodiments, a third party (e.g., RMOs, manufacturers)
can aggregate data at the component level, equipment level,
factory/installation level and provide a statistically valid data
set against which to optimize their own systems. For example, when
a new installation of a machine is contemplated, it may be
beneficial to review a library for best data points to acquire in
making state predictions. For example, a particular sensor package
may be recommended to reliably determine if there will be a
failure. For example, if vibration noise of equipment coupled with
particular levels of local noise or other ambient sensed conditions
reliably is an indicator of imminent failure, a given vibration
transducer/temp/microphone package observing those elements may be
recommended for the installation Knowing such information may
inform the choice to rent or buy a piece of machinery or associated
warranties and service plans, such as based on knowing the quantity
and depth of information that may be needed to reliably maintain
the machinery.
[0724] In embodiments, manufacturers may utilize the library to
rapidly collect in-service information for machines to draft
engineering specifications for new customers.
[0725] In embodiments, noise and vibration data may be used to
remotely monitor installs and automatically dispatch a field
crew.
[0726] In embodiments, noise and vibration data may be used to
audit a system. For example, equipment running outside the range of
a licensed duty cycle may be detected by a suite of vibration
sensors and/or ambient/local noise sensors. In embodiments, alerts
may be triggered of potential out-of-warranty violations based on
data from vibration sensors and/or ambient/local noise sensors.
[0727] In embodiments, noise and vibration data may be used in
maintenance. This may be particularly useful where multiple
machines are deployed that may vibrationally interact with the
environment, such as two large generating machines on the same
floor or platform with each other, such as in power generation
plants.
[0728] In embodiments (FIG. 72), a monitoring system 10800 for data
collection in an industrial environment, may include a plurality of
sensors 10802 selected among vibration sensors, ambient environment
condition sensors and local sensors for collecting non-vibration
data proximal to a machine in the environment, the plurality of
sensors 10802 communicatively coupled to a data collector 10804, a
data collection circuit 10808 structured to collect output data
10810 from the plurality of sensors 10802, and a machine learning
data analysis circuit 10812 structured to receive the output data
10810 and learn received output data patterns 10814 predictive of
at least one of an outcome and a state. The state may correspond to
an outcome relating to a machine in the environment, an anticipated
outcome relating to a machine in the environment, an outcome
relating to a process in the environment, or an anticipated outcome
relating to a process in the environment. The system may be
deployed on the data collector 10804 or distributed between the
data collector 10804 and a remote infrastructure. The data
collector 10804 may include the data collection circuit 10808. The
ambient environment condition or local sensors include one or more
of a noise sensor, a temperature sensor, a flow sensor, a pressure
sensor, a chemical sensor, a vibration sensor, an acceleration
sensor, an accelerometer, a Pressure sensor, a force sensor, a
position sensor, a location sensor, a velocity sensor, a
displacement sensor, a temperature sensor, a thermographic sensor,
a heat flux sensor, a tachometer sensor, a motion sensor, a
magnetic field sensor, an electrical field sensor, a galvanic
sensor, a current sensor, a flow sensor, a gaseous flow sensor, a
non-gaseous fluid flow sensor, a heat flow sensor, a particulate
flow sensor, a level sensor, a proximity sensor, a toxic gas
sensor, a chemical sensor, a CBRNE sensor, a pH sensor, a
hygrometer, a moisture sensor, a densitometer, an imaging sensor, a
camera, an SSR, a triax probe, an ultrasonic sensor, a touch
sensor, a microphone, a capacitive sensor, a strain gauge, an EMF
meter, and the like.
[0729] In embodiments, a monitoring system 10800 for data
collection in an industrial environment may include a data
collection circuit 10808 structured to collect output data 10810
from a plurality of sensors 10802 selected among vibration sensors,
ambient environment condition sensors and local sensors for
collecting non-vibration data proximal to a machine in the
environment, the plurality of sensors 10802 communicatively coupled
to a data collection circuit 10808, and a machine learning data
analysis circuit 10812 structured to receive the output data 10810
and learn received output data patterns 10814 predictive of at
least one of an outcome and a state, wherein the monitoring system
10800 is structured to determine if the output data matches a
learned received output data pattern. The machine learning data
analysis circuit 10812 may be structured to learn received output
data patterns 10814 by being seeded with a model 10816. The model
10816 may be a physical model, an operational model, or a system
model. The machine learning data analysis circuit 10812 may be
structured to learn received output data patterns 10814 based on
the outcome or the state. The monitoring system 10700 keeps or
modifies operational parameters or equipment based on the predicted
outcome or the state. The data collection circuit 10808 collects
more or fewer data points from one or more of the plurality of
sensors 10802 based on the learned received output data patterns
10814, the outcome or the state. The data collection circuit 10808
changes a data storage technique for the output data based on the
learned received output data patterns 10814, the outcome, or the
state. The data collector 10804 changes a data presentation mode or
manner based on the learned received output data patterns 10814,
the outcome, or the state. The data collection circuit 10808
applies one or more filters (low pass, high pass, band pass, etc.)
to the output data. The data collection circuit 10808 adjusts the
weights/biases of the machine learning data analysis circuit 10812,
such as in response to the learned received output data patterns
10814. The monitoring system 10800 removes/re-tasks under-utilized
equipment based on one or more of the learned received output data
patterns 10814, the outcome, or the state. The machine learning
data analysis circuit 10812 may include a neural network expert
system. The machine learning data analysis circuit 10812 may be
structured to learn received output data patterns 10814 indicative
of progress/alignment with one or more goals/guidelines, wherein
progress/alignment of each goal/guideline is determined by a
different subset of the plurality of sensors 10802. The machine
learning data analysis circuit 10812 may be structured to learn
received output data patterns 10814 indicative of an unknown
variable. The machine learning data analysis circuit 10812 may be
structured to learn received output data patterns 10814 indicative
of a preferred input sensor among available input sensors. The
machine learning data analysis circuit 10812 may be disposed in
part on a machine, on one or more data collection circuits 10808,
in network infrastructure, in the cloud, or any combination
thereof. The output data 10810 from the vibration sensors forms a
vibration fingerprint, which may include one or more of a
frequency, a spectrum, a velocity, a peak location, a wave peak
shape, a waveform shape, a wave envelope shape, an acceleration, a
phase information, and a phase shift. The data collection circuit
10808 may apply a rule regarding how many parameters of the
vibration fingerprint to match or the standard deviation for the
match in order to identify a match between the output data 10810
and the learned received output data pattern. The state may be one
of a normal operation, a maintenance required, a failure, or an
imminent failure. The monitoring system 10800 may trigger an alert,
shut down equipment/component/line, initiate
maintenance/lubrication/alignment based on the predicted outcome or
state, deploy a field technician based on the predicted outcome or
state, recommend a vibration absorption/dampening device based on
the predicted outcome or state, modify a process to utilize backup
equipment/component based on the predicted outcome or state, and
the like. The monitoring system 10800 may modify a process to
preserve products/reactants, etc. based on the predicted outcome or
state. The monitoring system 10800 may generate or modify a
maintenance schedule based on the predicted outcome or state. The
data collection circuit 10808 may include the data collection
circuit 10808. The system may be deployed on the data collection
circuit 10808 or distributed between the data collection circuit
10808 and a remote infrastructure.
[0730] In embodiments, a monitoring system 10800 for data
collection in an industrial environment may include a data
collection circuit 10808 structured to collect output data 10810
from a plurality of sensors 10802 selected among vibration sensors,
ambient environment condition sensors and local sensors for
collecting non-vibration data proximal to a machine in the
environment, the plurality of sensors 10802 communicatively coupled
to the data collection circuit 10808, and a machine learning data
analysis circuit 10812 structured to receive the output data 10810
and learn received output data patterns 10814 predictive of at
least one of an outcome and a state, wherein the monitoring system
10800 is structured to determine if the output data matches a
learned received output data pattern and keep or modify operational
parameters or equipment based on the determination.
[0731] In embodiments, a monitoring system 10800 for data
collection in an industrial environment may include a data
collection circuit 10808 structured to collect output data 10810
from the plurality of sensors 10802 selected among vibration
sensors, ambient environment condition sensors and local sensors
for collecting non-vibration data proximal to a machine in the
environment, the plurality of sensors 10802 communicatively coupled
to the data collection circuit 10808, and a machine learning data
analysis circuit 10812 structured to receive the output data 10810
and learn received output data patterns 10814 predictive of at
least one of an outcome and a state, wherein the output data 10810
from the vibration sensors forms a vibration fingerprint. The
vibration fingerprint may include one or more of a frequency, a
spectrum, a velocity, a peak location, a wave peak shape, a
waveform shape, a wave envelope shape, an acceleration, a phase
information, and a phase shift. The data collection circuit 10808
may apply a rule regarding how many parameters of the vibration
fingerprint to match or the standard deviation for the match in
order to identify a match between the output data 10810 and the
learned received output data pattern. The monitoring system 10800
may be structured to determine if the output data matches a learned
received output data pattern and keep or modify operational
parameters or equipment based on the determination
[0732] In embodiments, a monitoring system 10800 for data
collection in an industrial environment may include a data
collection band circuit 10818 that identifies a subset of the
plurality of sensors 10802 from which to process output data, the
sensors selected among vibration sensors, ambient environment
condition sensors and local sensors for collecting non-vibration
data proximal to a machine in the environment, the plurality of
sensors 10802 communicatively coupled to a data collection band
circuit 10818, a data collection circuit 10808 structured to
collect the output data 10810 from the subset of plurality of
sensors 10802, and a machine learning data analysis circuit 10812
structured to receive the output data 10810 and learn received
output data patterns 10814 predictive of at least one of an outcome
and a state, wherein when the learned received output data patterns
10814 do not reliably predict the outcome or the state, the data
collection band circuit 10818 alters at least one parameter of at
least one of the plurality of sensors 10802. A controller 10806
identifies a new data collection band circuit 10818 based on one or
more of the learned received output data patterns 10814 and the
outcome or state. The machine learning data analysis circuit 10812
may be further structured to learn received output data patterns
10814 indicative of a preferred input data collection band among
available input data collection bands. The system may be deployed
on the data collection circuit 10808 or distributed between the
data collection circuit 10808 and a remote infrastructure.
[0733] In embodiments, a monitoring system for data collection in
an industrial environment may include a data collection circuit
10808 structured to collect output data 10810 from a plurality of
sensors 10802, the sensors selected among vibration sensors,
ambient environment condition sensors and local sensors for
collecting non-vibration data proximal to a machine in the
environment, the plurality of sensors 10802 communicatively coupled
to the data collection circuit 10808, wherein the output data 10810
from the vibration sensors is in the form of a vibration
fingerprint, a data structure 10820 comprising a plurality of
vibration fingerprints and associated outcomes, and a machine
learning data analysis circuit 10812 structured to receive the
output data 10810 and learn received output data patterns 10814
predictive of an outcome or a state based on processing of the
vibration fingerprints. The machine learning data analysis circuit
10812 may be seeded with one of the plurality of vibration
fingerprints from the data structure 10820. The data structure
10820 may be updated if a changed parameter resulted in a new
vibration fingerprint or if a predicted outcome did not occur in
the absence of mitigation. The data structure 10820 may be updated
when the learned received output data patterns 10814 do not
reliably predict the outcome or the state. The system may be
deployed on the data collection circuit or distributed between the
data collection circuit and a remote infrastructure.
[0734] In embodiments, a monitoring system 10800 for data
collection in an industrial environment may include a data
collection circuit 10808 structured to collect output data 10810
from a plurality of sensors 10802 selected among vibration sensors,
ambient environment condition sensors and local sensors for
collecting non-vibration data proximal to a machine in the
environment, the plurality of sensors 10802 communicatively coupled
to a data collection circuit 10808, wherein the output data 10810
from the plurality of sensors 10802 is in the form of a noise
pattern, a data structure 10820 comprising a plurality of noise
patterns and associated outcomes, and a machine learning data
analysis circuit 10812 structured to receive the output data 10810
and learn received output data patterns 10814 predictive of an
outcome or a state based on processing of the noise patterns.
[0735] In embodiments, a monitoring system for data collection in
an industrial environment may comprise: a plurality of sensors
selected among vibration sensors, ambient environment condition
sensors and local sensors for collecting non-vibration data
proximal to a machine in the environment, the plurality of sensors
communicatively coupled to a data collector; a data collection
circuit structured to collect output data from the plurality of
sensors; and a machine learning data analysis circuit structured to
receive the output data and learn received output data patterns
predictive of at least one of an outcome and a state. The state may
correspond to an outcome, anticipated outcome, outcome relating to
a process, as relating to a machine in the environment. The system
may be deployed on the data collector. The system may be
distributed between the data collector and a remote infrastructure.
The ambient environment condition sensors may include a noise
sensor, a temperature sensor, a flow sensor, a pressure sensor,
include a chemical sensor, a noise sensor, a temperature sensor, a
flow sensor, a pressure sensor, a chemical sensor, a vibration
sensor, an acceleration sensor, an accelerometer, a pressure
sensor, a force sensor, a position sensor, a location sensor, a
velocity sensor, a displacement sensor, a temperature sensor, a
thermographic sensor, a heat flux sensor, a tachometer sensor, a
motion sensor, a magnetic field sensor, an electrical field sensor,
a galvanic sensor, a current sensor, a flow sensor, a gaseous flow
sensor, a non-gaseous fluid flow sensor, a heat flow sensor, a
particulate flow sensor, a level sensor, a proximity sensor, a
toxic gas sensor, a chemical sensor, a CBRNE sensor, a pH sensor, a
hygrometer, a moisture sensor, a densitometer, an imaging sensor, a
camera, an SSR, a triax probe, an ultrasonic sensor, a touch
sensor, a microphone, a capacitive sensor, a strain gauge, and an
EMF meter. The local sensors may comprise one or more of a
vibration sensor, an acceleration sensor, an accelerometer, a
pressure sensor, a force sensor, a position sensor, a location
sensor, a velocity sensor, a displacement sensor, a temperature
sensor, a thermographic sensor, a heat flux sensor, a tachometer
sensor, a motion sensor, a magnetic field sensor, an electrical
field sensor, a galvanic sensor, a current sensor, a flow sensor, a
gaseous flow sensor, a non-gaseous fluid flow sensor, a heat flow
sensor, a particulate flow sensor, a level sensor, a proximity
sensor, a toxic gas sensor, a chemical sensor, a CBRNE sensor, a pH
sensor, a hygrometer, a moisture sensor, a densitometer, an imaging
sensor, a camera, an SSR, a triax probe, an ultrasonic sensor, a
touch sensor, a microphone, a capacitive sensor, a strain gauge,
and an EMF meter.
[0736] In embodiments, a monitoring system for data collection in
an industrial environment may comprise: a data collection circuit
structured to collect output data from a plurality of sensors
selected among vibration sensors, ambient environment condition
sensors and local sensors for collecting non-vibration data
proximal to a machine in the environment, the plurality of sensors
communicatively coupled to the data collection circuit; and a
machine learning data analysis circuit structured to receive the
output data and learn received output data patterns predictive of
at least one of an outcome and a state, wherein the monitoring
system is structured to determine if the output data matches a
learned received output data pattern. In embodiments, the machine
learning data analysis circuit may be structured to learn received
output data patterns by being seeded with a model, such as where
the model is a physical model, an operational model, or a system
model. The machine learning data analysis circuit may be structured
to learn received output data patterns based on the outcome or the
state. The monitoring system may keep or modify operational
parameters or equipment based on the predicted outcome or the
state. The data collection circuit collects data points from one or
more of the plurality of sensors based on the learned received
output data patterns, the outcome, or the state. The data
collection circuit may change a data storage technique for the
output data based on the learned received output data patterns, the
outcome, or the state. The data collection circuit may change a
data presentation mode or manner based on the learned received
output data patterns, the outcome, or the state. The data
collection circuit may apply one or more filters (low pass, high
pass, band pass, etc.) to the output data. The data collection
circuit may adjust the weights/biases of the machine learning data
analysis circuit, such as where the adjustment is in response to
the learned received output data patterns. The monitoring system
may remove, or re-task under-utilized equipment based on one or
more of the learned received output data patterns, the outcome, or
the state. The machine learning data analysis circuit may include a
neural network expert system. The machine learning data analysis
circuit may be structured to learn received output data patterns
indicative of progress/alignment with one or more goals or
guidelines, such as where progress or alignment of each goal or
guideline is determined by a different subset of the plurality of
sensors. The machine learning data analysis circuit may be
structured to learn received output data patterns indicative of an
unknown variable. The machine learning data analysis circuit may be
structured to learn received output data patterns indicative of a
preferred input sensor among available input sensors. The machine
learning data analysis circuit may be disposed in part on a
machine, on one or more data collectors, in network infrastructure,
in the cloud, or any combination thereof. The output data from the
vibration sensors may form a vibration fingerprint, such as where
the vibration fingerprint includes one or more of a frequency, a
spectrum, a velocity, a peak location, a wave peak shape, a
waveform shape, a wave envelope shape, an acceleration, a phase
information, and a phase shift. The data collection circuit may
apply a rule regarding how many parameters of the vibration
fingerprint to match or the standard deviation for the match in
order to identify a match between the output data and the learned
received output data pattern. The state may be one of a normal
operation, a maintenance required, a failure, or an imminent
failure. The monitoring system may trigger an alert based on the
predicted outcome or state. The monitoring system may shut down
equipment, component, or line based on the predicted outcome or
state. The monitoring system may initiate maintenance, lubrication,
or alignment based on the predicted outcome or state. The
monitoring system may deploy a field technician based on the
predicted outcome or state. The monitoring system may recommend a
vibration absorption or dampening device based on the predicted
outcome or state. The monitoring system may modify a process to
utilize backup equipment or a component based on the predicted
outcome or state. The monitoring system may modify a process to
preserve products or reactants based on the predicted outcome or
state. The monitoring system may generate or modify a maintenance
schedule based on the predicted outcome or state. The system may be
distributed between the data collector and a remote
infrastructure.
[0737] In embodiments, a monitoring system for data collection in
an industrial environment may comprise: a data collection circuit
structured to collect output data from a plurality of sensors
selected among vibration sensors, ambient environment condition
sensors and local sensors for collecting non-vibration data
proximal to a machine in the environment, the plurality of sensors
communicatively coupled to the data collection circuit; and a
machine learning data analysis circuit structured to receive the
output data and learn received output data patterns predictive of
at least one of an outcome and a state, wherein the monitoring
system is structured to determine if the output data matches a
learned received output data pattern and keep or modify operational
parameters or equipment based on the determination.
[0738] In embodiments, a monitoring system for data collection in
an industrial environment may comprise: a data collection circuit
structured to collect output data from a plurality of sensors
selected among vibration sensors, ambient environment condition
sensors and local sensors for collecting non-vibration data
proximal to a machine in the environment, the plurality of sensors
communicatively coupled to the data collection circuit; and a
machine learning data analysis circuit structured to receive the
output data and learn received output data patterns predictive of
at least one of an outcome and a state, wherein the output data
from the vibration sensors forms a vibration fingerprint. In
embodiments, the vibration fingerprint may comprise one or more of
a frequency, a spectrum, a velocity, a peak location, a wave peak
shape, a waveform shape, a wave envelope shape, an acceleration, a
phase information, and a phase shift. The data collection circuit
may apply a rule regarding how many parameters of the vibration
fingerprint to match or the standard deviation for the match in
order to identify a match between the output data and the learned
received output data pattern. The monitoring system may be
structured to determine if the output data matches a learned
received output data pattern and keep or modify operational
parameters or equipment based on the determination
[0739] In embodiments, a monitoring system for data collection in
an industrial environment may comprise: a data collection band
circuit that identifies a subset of a plurality of sensors from
which to process output data, the sensors selected among vibration
sensors, ambient environment condition sensors and local sensors
for collecting non-vibration data proximal to a machine in the
environment, the plurality of sensors communicatively coupled to
the data collection band circuit; a data collection circuit
structured to collect the output data from the subset of plurality
of sensors; and a machine learning data analysis circuit structured
to receive the output data and learn received output data patterns
predictive of at least one of an outcome and a state wherein when
the learned received output data patterns do not reliably predict
the outcome or the state, the data collection band circuit alters
at least one parameter of at least one of the plurality of sensors.
In embodiments, the controller may identify a new data collection
band circuit based on one or more of the learned received output
data patterns and the outcome or state. The machine learning data
analysis circuit may be further structured to learn received output
data patterns indicative of a preferred input data collection band
among available input data collection bands. The system may be
distributed between the data collection circuit and a remote
infrastructure.
[0740] In embodiments, a monitoring system for data collection in
an industrial environment may comprise a data collection circuit
structured to collect output data from the plurality of sensors,
the sensors selected among vibration sensors, ambient environment
condition sensors and local sensors for collecting non-vibration
data proximal to a machine in the environment and being
communicatively coupled to the data collection circuit, wherein the
output data from the vibration sensors is in the form of a
vibration fingerprint; a data structure comprising a plurality of
vibration fingerprints and associated outcomes; and a machine
learning data analysis circuit structured to receive the output
data and learn received output data patterns predictive of an
outcome or a state based on processing of the vibration
fingerprints. The machine learning data analysis circuit may be
seeded with one of the plurality of vibration fingerprints from the
data structure. The data structure maybe updated if a changed
parameter resulted in a new vibration fingerprint or if a predicted
outcome did not occur in the absence of mitigation. The data
structure may be updated when the learned received output data
patterns do not reliably predict the outcome or the state. The
system may be distributed between the data collection circuit and a
remote infrastructure.
[0741] In embodiments, a monitoring system for data collection in
an industrial environment may comprise a data collection circuit
structured to collect output data from the plurality of sensors
selected among vibration sensors, ambient environment condition
sensors and local sensors for collecting non-vibration data
proximal to a machine in the environment, the plurality of sensors
communicatively coupled to the data collection circuit, wherein the
output data from the plurality of sensors is in the form of a noise
pattern; a data structure comprising a plurality of noise patterns
and associated outcomes; and a machine learning data analysis
circuit structured to receive the output data and learn received
output data patterns predictive of an outcome or a state based on
processing of the noise patterns.
[0742] An example system for data collection in an industrial
environment includes an industrial system having a number of
components, and a number of sensors wherein each of the sensors is
operatively coupled to at least one of the components. The example
system further includes a sensor communication circuit that
interprets a number of sensor data values in response to a sensed
parameter group, a pattern recognition circuit that determines a
recognized pattern value in response to a least a portion of the
sensor data values, and a sensor learning circuit that updates the
sensed parameter group in response to the recognized pattern value.
The example sensor communication circuit further adjusts the
interpreting the sensor data values in response to the updated
sensed parameter group.
[0743] Certain further aspects of an example system are described
following, any one or more of which may be present in certain
embodiments. An example system includes the sensed parameter group
being a fused number of sensors, and where the recognized pattern
value further includes a secondary value including a value
determined in response to the fused number of sensors. An example
system further includes the pattern recognition circuit and the
sensor learning circuit iteratively performing the determining the
recognized pattern value and the updating the sensed parameter
group to improve a sensing performance value. An example system
further includes the sensing performance value include a
determination of one or more of the following: a signal-to-noise
performance for detecting a value of interest in the industrial
system; a network utilization of the sensors in the industrial
system; an effective sensing resolution for a value of interest in
the industrial system; a power consumption value for a sensing
system in the industrial system, the sensing system including the
sensors; a calculation efficiency for determining the secondary
value; an accuracy and/or a precision of the secondary value; a
redundancy capacity for determining the secondary value; and/or a
lead time value for determining the secondary value. Example and
non-limiting calculation efficiency values include one or more
determinations such as: processor operations to determine the
secondary value; memory utilization for determining the secondary
value; a number of sensor inputs from the number of sensors for
determining the secondary value; and/or supporting data long-term
storage for supporting the secondary value.
[0744] An example system includes one or more, or all, of the
sensors as analog sensors and/or as remote sensors. An example
system includes the secondary value being a value such as: a
virtual sensor output value; a process prediction value; a process
state value; a component prediction value; a component state value;
and/or a model output value having the sensor data values from the
fused number of sensors as an input. An example system includes the
fused number of sensors being one or more of the combinations of
sensors such as: a vibration sensor and a temperature sensor; a
vibration sensor and a pressure sensor; a vibration sensor and an
electric field sensor; a vibration sensor and a heat flux sensor; a
vibration sensor and a galvanic sensor; and/or a vibration sensor
and a magnetic sensor.
[0745] An example sensor learning circuit further updates the
sensed parameter group by performing an operation such as: updating
a sensor selection of the sensed parameter group; updating a sensor
sampling rate of at least one sensor from the sensed parameter
group; updating a sensor resolution of at least one sensor from the
sensed parameter group; updating a storage value corresponding to
at least one sensor from the sensed parameter group; updating a
priority corresponding to at least one sensor from the sensed
parameter group; and/or updating at least one of a sampling rate,
sampling order, sampling phase, and/or a network path configuration
corresponding to at least one sensor from the sensed parameter
group. An example pattern recognition circuit further determines
the recognized pattern value by performing an operation such as:
determining a signal effectiveness of at least one sensor of the
sensed parameter group and the updated sensed parameter group
relative to a value of interest; determining a sensitivity of at
least one sensor of the sensed parameter group and the updated
sensed parameter group relative to the value of interest;
determining a predictive confidence of at least one sensor of the
sensed parameter group and the updated sensed parameter group
relative to the value of interest; determining a predictive delay
time of at least one sensor of the sensed parameter group and the
updated sensed parameter group relative to the value of interest;
determining a predictive accuracy of at least one sensor of the
sensed parameter group and the updated sensed parameter group
relative to the value of interest; determining a predictive
precision of at least one sensor of the sensed parameter group and
the updated sensed parameter group relative to the value of
interest; and/or updating the recognized pattern value in response
to external feedback. Example and non-limiting values of interest
include: a virtual sensor output value; a process prediction value;
a process state value; a component prediction value; a component
state value; and/or a model output value having the sensor data
values from the fused plurality of sensors as an input.
[0746] An example pattern recognition circuit further accesses
cloud-based data including a second number of sensor data values,
the second number of sensor data values corresponding to at least
one offset industrial system. An example sensor learning circuit
further accesses the cloud-based data including a second updated
sensor parameter group corresponding to the at least one offset
industrial system.
[0747] An example procedure for data collection in an industrial
environment includes an operation to provide a number of sensors to
an industrial system including a number of components, each of the
number of sensors operatively coupled to at least one of the number
of components, an operation to interpret a number of sensor data
values in response to a sensed parameter group, the sensed
parameter group including a fused number of sensors from the number
of sensors, an operation to determine a recognized pattern value
including a secondary value determined in response to the number of
sensor data values, an operation to update the sensed parameter
group in response to the recognized pattern value, and an operation
to adjust the interpreting the number of sensor data values in
response to the updated sensed parameter group.
[0748] Certain further aspects of an example procedure are
described following, any one or more of which may be included in
certain embodiments. An example procedure includes an operation to
iteratively perform the determining the recognized pattern value
and the updating the sensed parameter group to improve a sensing
performance value, where determining the sensing performance value
includes an least one operation for determining a value, such as
determining: a signal-to-noise performance for detecting a value of
interest in the industrial system; a network utilization of the
plurality of sensors in the industrial system; an effective sensing
resolution for a value of interest in the industrial system; a
power consumption value for a sensing system in the industrial
system, the sensing system including the plurality of sensors; a
calculation efficiency for determining the secondary value; an
accuracy and/or a precision of the secondary value; a redundancy
capacity for determining the secondary value; and/or a lead time
value for determining the secondary value.
[0749] An example procedure includes an operation to update the
sensed parameter group comprised by performing at least one
operation such as: updating a sensor selection of the sensed
parameter group; updating a sensor sampling rate of at least one
sensor from the sensed parameter group; updating a sensor
resolution of at least one sensor from the sensed parameter group;
updating a storage value corresponding to at least one sensor from
the sensed parameter group; updating a priority corresponding to at
least one sensor from the sensed parameter group; and/or updating
at least one of a sampling rate, sampling order, sampling phase,
and a network path configuration corresponding to at least one
sensor from the sensed parameter group. An example procedure
includes determining the recognized pattern value by performing at
least one operation such as: determining a signal effectiveness of
at least one sensor of the sensed parameter group and the updated
sensed parameter group relative to a value of interest; determining
a sensitivity of at least one sensor of the sensed parameter group
and the updated sensed parameter group relative to the value of
interest; determining a predictive confidence of at least one
sensor of the sensed parameter group and the updated sensed
parameter group relative to the value of interest; determining a
predictive delay time of at least one sensor of the sensed
parameter group and the updated sensed parameter group relative to
the value of interest; determining a predictive accuracy of at
least one sensor of the sensed parameter group and the updated
sensed parameter group relative to the value of interest;
determining a predictive precision of at least one sensor of the
sensed parameter group and the updated sensed parameter group
relative to the value of interest; and/or updating the recognized
pattern value in response to external feedback.
[0750] The term industrial system (and similar terms) as utilized
herein should be understood broadly. Without limitation to any
other aspect or description of the present disclosure, an
industrial system includes any large scale process system,
mechanical system, chemical system, assembly line, oil and gas
system (including, without limitation, production, transportation,
exploration, remote operations, offshore operations, and/or
refining), mining system (including, without limitation,
production, exploration, transportation, remote operations, and/or
underground operations), rail system (yards, trains, shipments,
etc.), construction, power generation, aerospace, agriculture, food
processing, and/or energy generation. Certain components may not be
considered industrial individually, but may be considered
industrially in an aggregated system--for example a single fan,
motor, and/or engine may be not an industrial system, but may be a
part of a larger system and/or be accumulated with a number of
other similar components to be considered an industrial system
and/or a part of an industrial system. In certain embodiments, a
system may be considered an industrial system for some purposes but
not for other purposes--for example a large data server farm may be
considered an industrial system for certain sensing operations,
such as temperature detection, vibration, or the like, but not an
industrial system for other sensing operations such as gas
composition. Additionally, in certain embodiments, otherwise
similar looking systems may be differentiated in determining
whether such system are industrial systems, and/or which type of
industrial system. For example, one data server farm may not, at a
given time, have process stream flow rates that are critical to
operation, while another data server farm may have process stream
flow rates that are critical to operation (e.g., a coolant flow
stream), and accordingly one data farm server may be an industrial
system for a data collection and/or sensing improvement process or
system, while the other is not. Accordingly, the benefits of the
present disclosure may be applied in a wide variety of systems, and
any such systems may be considered an industrial system herein,
while in certain embodiments a given system may not be considered
an industrial system herein. One of skill in the art, having the
benefit of the disclosure herein and knowledge about a contemplated
system ordinarily available to that person, can readily determine
which aspects of the present disclosure will benefit a particular
system, how to combine processes and systems from the present
disclosure to enhance operations of the contemplated system.
Certain considerations for the person of skill in the art, in
determining whether a contemplated system is an industrial system
and/or whether aspects of the present disclosure can benefit or
enhance the contemplated system include, without limitation: the
accessibility of portions of the system to positioning sensing
devices; the sensitivity of the system to capital costs (e.g.,
initial installation) and operating costs (e.g., optimization of
processes, reduction of power usage); the transmission environment
of the system (e.g., availability of broadband internet; satellite
coverage; wireless cellular access; the electro-magnetic ("EM")
environment of the system; the weather, temperature, and
environmental conditions of the system; the availability of
suitable locations to run wires, network lines, and the like; the
presence and/or availability of suitable locations for network
infrastructure, router positioning, and/or wireless repeaters); the
availability of trained personnel to interact with computing
devices; the desired spatial, time, and/or frequency resolution of
sensed parameters in the system; the degree to which a system or
process is well understood or modeled; the turndown ratio in system
operations (e.g., high load differential to low load; high flow
differential to low flow; high temperature operation differential
to low temperature operation); the turndown ratio in operating
costs (e.g., effects of personnel costs based on time (day, season,
etc.); effects of power consumption cost variance with time,
throughput, etc.); the sensitivity of the system to failure,
down-time, or the like; the remoteness of the contemplated system
(e.g., transport costs, time delays, etc.); and/or qualitative
scope of change in the system over the operating cycle (e.g., the
system runs several distinct processes requiring a variable sensing
environment with time; time cycle and nature of changes such as
periodic, event driven, lead times generally available, etc.).
While specific examples of industrial systems and considerations
are described herein for purposes of illustration, any system
benefitting from the disclosures herein, and any considerations
understood to one of skill in the art having the benefit of the
disclosures herein, are specifically contemplated within the scope
of the present disclosure.
[0751] The term sensor (and similar terms) as utilized herein
should be understood broadly. Without limitation to any other
aspect or description of the present disclosure, sensor includes
any device configured to provide a sensed value representative of a
physical value (e.g., temperature, force, pressure) in a system, or
representative of a conceptual value in a system at least having an
ancillary relationship to a physical value (e.g., work, state of
charge, frequency, phase, etc.).
[0752] Example and non-limiting sensors include vibration,
acceleration, noise, pressure, force, position, location, velocity,
displacement, temperature, heat flux, speed, rotational speed
(e.g., a tachometer), motion, accelerometers, magnetic field,
electrical field, galvanic, current, flow (gas, fluid, heat,
particulates, particles, etc.), level, proximity, gas composition,
fluid composition, toxicity, corrosiveness, acidity, pH, humidity,
hygrometer measures, moisture, density (bulk or specific),
ultrasound, imaging, analog, and/or digital sensors. The list of
sensed values is a non-limiting example, and the benefits of the
present disclosure in many applications can be realized independent
of the sensor type, while in other applications the benefits of the
present disclosure may be dependent upon the sensor type.
[0753] The sensor type and mechanism for detection may be any type
of sensor understood in the art. Without limitation, an
accelerometer may be any type and scaling, for example 500 mV per g
(1 g=9.8 m/s.sup.2), 100 mV, 1 V per g, 5 V per g, 10 V per g, 10
MV per g, as well as any frequency capability. It will be
understood for accelerometers, and for all sensor types, that the
scaling and range may be competing (e.g., in a fixed-bit or low bit
A/D system), and/or selection of high resolution scaling with a
large range may drive up sensor and/or computing costs, which may
be acceptable in certain embodiments, and may be prohibitive in
other embodiments. Example and non-limiting accelerometers include
piezo-electric devices, high resolution and sampling speed position
detection devices (e.g., laser based devices), and/or detection of
other parameters (strain, force, noise, etc.) that can be
correlated to acceleration and/or vibration. Example and
non-limiting proximity probes include electro-magnetic devices
(e.g., Hall effect, Variable Reluctance, etc.), a sleeve/oil film
device, and/or determination of other parameters than can be
correlated to proximity. An example vibration sensor includes a
tri-axial probe, which may have high frequency response (e.g.,
scaling of 100 MV/g). Example and non-limiting temperature sensors
include thermistors, thermocouples, and/or optical temperature
determination.
[0754] A sensor may, additionally or alternatively, provide a
processed value (e.g., a de-bounced, filtered, and/or compensated
value) and/or a raw value, with processing downstream (e.g., in a
data collector, controller, plant computer, and/or on a cloud-based
data receiver). In certain embodiments, a sensor provides a
voltage, current, data file (e.g., for images), or other raw data
output, and/or a sensor provides a value representative of the
intended sensed measurement (e.g., a temperature sensor may
communicate a voltage or a temperature value). Additionally or
alternatively, a sensor may communicate wirelessly, through a wired
connection, through an optical connection, or by any other
mechanism The described examples of sensor types and/or
communication parameters are non-limiting examples for purposes of
illustration.
[0755] Additionally or alternatively, in certain embodiments, a
sensor is a distributed physical device--for example where two
separate sensing elements coordinate to provide a sensed value
(e.g., a position sensing element and a mass sensing element may
coordinate to provide an acceleration value). In certain
embodiments, a single physical device may form two or more sensors,
and/or parts of more than one sensor. For example, a position
sensing element may form a position sensor and a velocity sensor,
where the same physical hardware provides the sensed data for both
determinations.
[0756] The term smart sensor, smart device (and similar terms) as
utilized herein should be understood broadly. Without limitation to
any other aspect or description of the present disclosure, a smart
sensor includes any sensor and aspect thereof as described
throughout the present disclosure. A smart sensor includes an
increment of processing reflected in the sensed value communicated
by the sensor, including at least basic sensor processing (e.g.,
de-bouncing, filtering, compensation, normalization, and/or output
limiting), more complex compensations (e.g., correcting a
temperature value based on known effects of current environmental
conditions on the sensed temperature value, common mode or other
noise removal, etc.), a sensing device that provides the sensed
value as a network communication, and/or a sensing device that
aggregates a number of sensed values for communication (e.g.,
multiple sensors on a device communicated out in a parseable or
deconvolutable manner or as separate messages; multiple sensors
providing a value to a single smart sensor, which relays sensed
values on to a data collector, controller, plant computer, and/or
cloud-based data receiver). The use of the term smart sensor is for
purposes of illustration, and whether a sensor is a smart sensor
can depend upon the context and the contemplated system, and can be
a relative description compared to other sensors in the
contemplated system. Thus, a given sensor having identical
functionality may be a smart sensor for the purposes of one
contemplated system, and just a sensor for the purposes of another
contemplated system, and/or may be a smart sensor in a contemplated
system during certain operating conditions, and just a sensor for
the purposes of the same contemplated system during other operating
conditions.
[0757] The terms sensor fusion, fused sensors, and similar terms,
as utilized herein, should be understood broadly, except where
context indicates otherwise, without limitation to any other aspect
or description of the present disclosure. A sensor fusion includes
a determination of second order data from sensor data, and further
includes a determination of second order data from sensor data of
multiple sensors, including involving multiplexing of streams of
data, combinations of batches of data, and the like from the
multiple sensors. Second order data includes a determination about
a system or operating condition beyond that which is sensed
directly. For example, temperature, pressure, mixing rate, and
other data may be analyzed to determine which parameters are
result-effective on a desired outcome (e.g., a reaction rate). The
sensor fusion may include sensor data from multiple sources, and/or
longitudinal data (e.g., taken over a period of time, over the
course of a process, and/or over an extent of components in a
plant--for example tracking a number of assembled parts, a virtual
slug of fluid passing through a pipeline, or the like). The sensor
fusion may be performed in real-time (e.g., populating a number of
sensor fusion determinations with sensor data as a process
progresses), off-line (e.g., performed on a controller, plant
computer, and/or cloud-based computing device), and/or as a
post-processing operation (e g , utilizing historical data, data
from multiple plants or processes, etc.). In certain embodiments, a
sensor fusion includes a machine pattern recognition operation--for
example where an outcome of a process is given to the machine
and/or determined by the machine, and the machine pattern
recognition operation determines result-effective parameters from
the detected sensor value space to determine which operating
conditions were likely to be the cause of the outcome and/or the
off-nominal result of the outcome (e.g., process was less effective
or more effective than nominal, failed, etc.). In certain
embodiments, the outcome may be a quantitative outcome (e.g., 20%
more product was produced than a nominal run) or a qualitative
outcome (e.g., product quality was unacceptable, component X of the
contemplated system failed during the process, component X of the
contemplated system required a maintenance or service event,
etc.).
[0758] In certain embodiments, a sensor fusion operation is
iterative or recursive--for example an estimated set of result
effective parameters is updated after the sensor fusion operation,
and a subsequent sensor fusion operation is performed on the same
data or another data set with an updated set of the result
effective parameters. In certain embodiments, subsequent sensor
fusion operations include adjustments to the sensing scheme--for
example higher resolution detections (e.g., in time, space, and/or
frequency domains), larger data sets (and consequent commitment of
computing and/or networking resources), changes in sensor
capability and/or settings (e.g., changing an A/D scaling, range,
resolution, etc.; changing to a more capable sensor and/or more
capable data collector, etc.) are performed for subsequent sensor
fusion operations. In certain embodiments, the sensor fusion
operation demonstrates improvements to the contemplated system
(e.g., production quantity, quality, and/or purity, etc.) such that
expenditure of additional resources to improve the sensing scheme
are justified. In certain embodiments, the sensor fusion operation
provides for improvement in the sensing scheme without incremental
cost--for example by narrowing the number of result effective
parameters and thereby freeing up system resources to provide
greater resolution, sampling rates, etc., from hardware already
present in the contemplated system. In certain embodiments,
iterative and/or recursive sensor fusion is performed on the same
data set, a subsequent data set, and/or a historical data set. For
example, high resolution data may already be present in the system,
and a first sensor fusion operation is performed with low
resolution data (e.g., sampled from the high resolution data set),
such as to allow for completion of sensor fusion processing
operations within a desired time frame, within a desired processor,
memory, and/or network utilization, and/or to allow for checking a
large number of variables as potential result effective parameters.
In a further example, a greater number of samples from the high
resolution data set may be utilized in a subsequent sensor fusion
operation in response to confidence that improvements are present,
narrowing of the potential result effective variables, and/or a
determination that higher resolution data is required to determine
the result effective parameters and/or effective values for such
parameters.
[0759] The described operations and aspects for sensor fusion are
non-limiting examples, and one of skill in the art, having the
benefit of the disclosures herein and information ordinarily
available about a contemplated system, can readily design a system
to utilize and/or benefit from a sensor fusion operation. Certain
considerations for a system to utilize and/or benefit from a sensor
fusion operation include, without limitation the number of
components in the system; the cost of components in the system; the
cost of maintenance and/or down-time for the system; the value of
improvements in the system (production quantity, quality, yield,
etc.); the presence, possibility, and/or consequences of
undesirable system outcomes (e.g., side products, thermal and/or
luminary events, environmental benefits or consequences, hazards
present in the system); the expense of providing a multiplicity of
sensors for the system; the complexity between system inputs and
system outputs; the availability and cost of computing resources
(e.g., processing, memory, and/or communication throughput); the
size/scale of the contemplated system and/or the ability of such a
system to generate statistically significant data; whether offset
systems exist, including whether data from offset systems is
available and whether combining data from offset systems will
generate a statistically improved data set relative to the system
considered alone; and/or the cost of upgrading, improving, or
changing a sensing scheme for the contemplated system. The
described considerations for a contemplated system that may benefit
from or utilize a sensor fusion operation are non-limiting
illustrations.
[0760] Certain systems, processes, operations, and/or components
are described in the present disclosure as "offset systems" or the
like. An offset system is a system distinct from a contemplated
system, but having relevance to the contemplated system. For
example, a contemplated refinery may have an "offset refinery,"
which may be a refinery operated by a competitor, by a same entity
operating the contemplated refinery, and/or a historically operated
refinery that no longer exists. The offset refinery bears some
relevant relationship to the contemplated refinery, such as
utilizing similar reactions, process flows, production volumes,
feed stock, effluent materials, or the like. A system which is an
offset system for one purpose may not be an offset system for
another purpose. For example, a manufacturing process utilizing
conveyor belts and similar motors may be an offset process for a
contemplated manufacturing process for the purpose of tracking
product movement, understanding motor operations and failure modes,
or the like, but may not be an offset process for product quality
if the products being produced have distinct quality outcome
parameters. Any industrial system contemplated herein may have an
offset system for certain purposes. One of skill in the art, having
the benefit of the present disclosure and information ordinarily
available for a contemplated system, can readily determine what is
disclosed by an offset system or offset aspect of a system.
[0761] Any one or more of the terms computer, computing device,
processor, circuit, and/or server include a computer of any type,
capable to access instructions stored in communication thereto such
as upon a non-transient computer readable medium, whereupon the
computer performs operations of systems or methods described herein
upon executing the instructions. In certain embodiments, such
instructions themselves comprise a computer, computing device,
processor, circuit, and/or server. Additionally or alternatively, a
computer, computing device, processor, circuit, and/or server may
be a separate hardware device, one or more computing resources
distributed across hardware devices, and/or may include such
aspects as logical circuits, embedded circuits, sensors, actuators,
input and/or output devices, network and/or communication
resources, memory resources of any type, processing resources of
any type, and/or hardware devices configured to be responsive to
determined conditions to functionally execute one or more
operations of systems and methods herein.
[0762] Certain operations described herein include interpreting,
receiving, and/or determining one or more values, parameters,
inputs, data, or other information. Operations including
interpreting, receiving, and/or determining any value parameter,
input, data, and/or other information include, without limitation:
receiving data via a user input; receiving data over a network of
any type; reading a data value from a memory location in
communication with the receiving device; utilizing a default value
as a received data value; estimating, calculating, or deriving a
data value based on other information available to the receiving
device; and/or updating any of these in response to a later
received data value. In certain embodiments, a data value may be
received by a first operation, and later updated by a second
operation, as part of the receiving a data value. For example, when
communications are down, intermittent, or interrupted, a first
operation to interpret, receive, and/or determine a data value may
be performed, and when communications are restored an updated
operation to interpret, receive, and/or determine the data value
may be performed.
[0763] Certain logical groupings of operations herein, for example
methods or procedures of the current disclosure, are provided to
illustrate aspects of the present disclosure. Operations described
herein are schematically described and/or depicted, and operations
may be combined, divided, re-ordered, added, or removed in a manner
consistent with the disclosure herein. It is understood that the
context of an operational description may require an ordering for
one or more operations, and/or an order for one or more operations
may be explicitly disclosed, but the order of operations should be
understood broadly, where any equivalent grouping of operations to
provide an equivalent outcome of operations is specifically
contemplated herein. For example, if a value is used in one
operational step, the determining of the value may be required
before that operational step in certain contexts (e.g., where the
time delay of data for an operation to achieve a certain effect is
important), but may not be required before that operation step in
other contexts (e.g., where usage of the value from a previous
execution cycle of the operations would be sufficient for those
purposes). Accordingly, in certain embodiments an order of
operations and grouping of operations as described is explicitly
contemplated herein, and in certain embodiments re-ordering,
subdivision, and/or different grouping of operations is explicitly
contemplated herein.
[0764] Referencing FIG. 73, an example procedure 11038 includes an
operation 11040 to provide a number of sensors to an industrial
system including a number of components, each of the number of
sensors operatively coupled to at least one of the number of
components, an operation 11042 to interpret a number of sensor data
values in response to a sensed parameter group, the sensed
parameter group including at least one sensor of the number of
sensors, an operation 11044 to determine a recognized pattern value
in response to a least a portion of the number of sensor data
values, and an operation 11046 to provide a system characterization
value for the industrial system in response to the recognized
pattern value.
[0765] An example procedure 11038 further includes the operation
11046 to provide the system characterization value by performing an
operation such as: determining a predicted outcome for a process
associated with the industrial system; determining a predicted
future state for a process associated with the industrial system;
determining a predicted off-nominal operation for the process
associated with the industrial system; determining a prediction
value for one of the plurality of components; determining a future
state value for one of the plurality of components; determining an
anticipated maintenance health state information for one of the
plurality of components; determining a predicted maintenance
interval for at least one of the plurality of components;
determining a predicted off-nominal operation for one of the
plurality of components; determining a predicted fault operation
for one of the plurality of components; determining a predicted
exceedance value for one of the plurality of components; and/or
determining a predicted saturation value for one of the plurality
of sensors.
[0766] An example procedure 11038 includes an operation 11050 to
interpret external data and/or cloud-based data, and where the
operation 11044 to determine the recognized pattern value is
further in response to the external data and/or the cloud-based
data. An example procedure 11038 includes an operation to
iteratively improve pattern recognition operations in response to
the external data and/or the cloud-based data, for example by
operation 11048 to adjust the operation 11042 interpreting sensor
values, such as by updating the sensed parameter group. The
operation to iteratively improve pattern recognition may further
include repeating operations 11042 through 11048, periodically, at
selected intervals, in response to a system change, and/or in
response to a prediction value of a component, process, or the
system.
[0767] In embodiments, a system for data collection in an
industrial environment may comprise: an industrial system
comprising a plurality of components, and a plurality of sensors
each operatively coupled to at least one of the plurality of
components; a sensor communication circuit structured to interpret
a plurality of sensor data values in response to a sensed parameter
group, the sensed parameter group comprising at least one sensor of
the plurality of sensors; a pattern recognition circuit structured
to determine a recognized pattern value in response to a least a
portion of the plurality of sensor data values; and a system
characterization circuit structured to provide a system
characterization value for the industrial system in response to the
recognized pattern value. In embodiments, a characterization value
may include at least one characterization value selected from the
characterization values consisting of: a predicted outcome for a
process associated with the industrial system; a predicted future
state for a process associated with the industrial system; and a
predicted off-nominal operation for the process associated with the
industrial system. The system characterization value may include at
least one characterization value selected from the characterization
values consisting of: a prediction value for one of the plurality
of components; a future state value for one of the plurality of
components; an anticipated maintenance health state information for
one of the plurality of components; and a predicted maintenance
interval for at least one of the plurality of components. The
system characterization value may include at least one
characterization value selected from the characterization values
consisting of: a predicted off-nominal operation for one of the
plurality of components; a predicted fault operation for one of the
plurality of components; and a predicted exceedance value for one
of the plurality of components. The system characterization value
may include a predicted saturation value for one of the plurality
of sensors. A system collaboration circuit may be included that is
structured to interpret external data, and wherein the pattern
recognition circuit is further structured to determine the
recognized pattern value further in response to the external data.
The pattern recognition circuit may be further structured to
iteratively improve pattern recognition operations in response to
the external data. The external data may include at least one of:
an indicated component maintenance event; an indicated component
failure event; an indicated component wear value; an indicated
component operational exceedance value; and an indicated fault
value. The external data may include at least one of: an indicated
process failure value; an indicated process success value; an
indicated process outcome value; and an indicated process
operational exceedance value. The external data may include an
indicated sensor saturation value. A system collaboration circuit
may be included that is structured to interpret cloud-based data
comprising a second plurality of sensor data values, the second
plurality of sensor data values corresponding to at least one
offset industrial system, and wherein the pattern recognition
circuit is further structured to determine the recognized pattern
value further in response to the cloud-based data. The pattern
recognition circuit may be further structured to iteratively
improve pattern recognition operations in response to the
cloud-based data. The sensed parameter group may include a triaxial
vibration sensor. The sensed parameter group may include a
vibration sensor and a second sensor that is not a vibration
sensor, such as where the second sensor comprises a digital sensor.
The sensed parameter group may include a plurality of analog
sensors.
[0768] In embodiments, a method may comprise: providing a plurality
of sensors to an industrial system comprising a plurality of
components, each of the plurality of sensors operatively coupled to
at least one of the plurality of components; interpreting a
plurality of sensor data values in response to a sensed parameter
group, the sensed parameter group comprising at least one sensor of
the plurality of sensors; determining a recognized pattern value in
response to a least a portion of the plurality of sensor data
values; and providing a system characterization value for the
industrial system in response to the recognized pattern value. The
system characterization value may be provided by performing at
least one operation selected from the operations consisting of:
determining a prediction value for one of the plurality of
components; determining a future state value for one of the
plurality of components; determining an anticipated maintenance
health state information for one of the plurality of components;
and determining a predicted maintenance interval for at least one
of the plurality of components. The system characterization value
may be provided by performing at least one operation selected from
the operations consisting of: determining a predicted outcome for a
process associated with the industrial system; determining a
predicted future state for a process associated with the industrial
system; and determining a predicted off-nominal operation for the
process associated with the industrial system. The system
characterization value may be provided by performing at least one
operation selected from the operations consisting of: determining a
predicted off-nominal operation for one of the plurality of
components; determining a predicted fault operation for one of the
plurality of components; and determining a predicted exceedance
value for one of the plurality of components. The system
characterization value may be provided by determining a predicted
saturation value for one of the plurality of sensors. Determining
the recognized pattern value may be further in response to the
external data. Iteratively improving pattern recognition operations
may be provided in response to the external data. Interpreting the
external data may include at least one operation selected from the
operations consisting of: interpreting an indicated component
maintenance event; interpreting an indicated component failure
event; interpreting an indicated component wear value; interpreting
an indicated component operational exceedance value; and
interpreting an indicated fault value. Interpreting the external
data may include at least one operation selected from the
operations consisting of: interpreting an indicated process success
value; interpreting an indicated process failure value;
interpreting an indicated process outcome value; and interpreting
an indicated process operational exceedance value. Interpreting the
external data may include interpreting an indicated sensor
saturation value. Interpreting cloud-based data may include a
second plurality of sensor data values, the second plurality of
sensor data values corresponding to at least one offset industrial
system, and wherein determining the recognized pattern value is
further in response to the cloud-based data. Iteratively improving
pattern recognition operations may be provided in response to the
cloud-based data.
[0769] In embodiments, a system for data collection in an
industrial environment may comprise: an industrial system
comprising a plurality of components, and a plurality of sensors
each operatively coupled to at least one of the plurality of
components; a sensor communication circuit structured to interpret
a plurality of sensor data values in response to a sensed parameter
group, the sensed parameter group comprising at least one sensor of
the plurality of sensors; a means for determining a recognized
pattern value in response to at least a portion of the plurality of
sensor data values; and a means for providing a system
characterization value for the industrial system in response to the
recognized pattern value. The means for providing the system
characterization value may comprise a means for performing at least
one operation selected from the operations consisting of:
determining a predicted outcome for a process associated with the
industrial system; determining a predicted future state for a
process associated with the industrial system; and determining a
predicted off-nominal operation for the process associated with the
industrial system. The means for providing the system
characterization value may include a means for performing at least
one operation selected from the operations consisting of:
determining a prediction value for one of the plurality of
components; determining a future state value for one of the
plurality of components; determining an anticipated maintenance
health state information for one of the plurality of components;
and determining a predicted maintenance interval for at least one
of the plurality of components. The means for providing the system
characterization value may include a means for performing at least
one operation selected from the operations consisting of:
determining a predicted off-nominal operation for one of the
plurality of components; determining a predicted fault operation
for one of the plurality of components; and determining a predicted
exceedance value for one of the plurality of components. The means
for providing the system characterization value may include a means
for determining a predicted saturation value for one of the
plurality of sensors. A system collaboration circuit may be
provided that is structured to interpret external data, and wherein
the means for determining the recognized pattern value determines
the recognized pattern value further in response to the external
data. A means for iteratively improving pattern recognition
operations may be provided in response to the external data. The
external data may include at least one of: an indicated process
success value; an indicated process failure value; and an indicated
process outcome value. The external data may include at least one
of: an indicated component maintenance event; an indicated
component failure event; and an indicated component wear value. The
external data may include at least one of: an indicated process
operational exceedance value; an indicated component operational
exceedance value; and an indicated fault value. The external data
may include an indicated sensor saturation value. A system
collaboration circuit may be provided that is structured to
interpret cloud-based data comprising a second plurality of sensor
data values, the second plurality of sensor data values
corresponding to at least one offset industrial system, and wherein
the means for determining the recognized pattern value determines
the recognized pattern value further in response to the cloud-based
data. A means for iteratively improving pattern recognition
operations may be provided in response to the cloud-based data.
[0770] In embodiments, a system for data collection in an
industrial environment may comprise: a distillation column
comprising a plurality of components, and a plurality of sensors
each operatively coupled to at least one of the plurality of
components; a sensor communication circuit structured to interpret
a plurality of sensor data values in response to a sensed parameter
group, the sensed parameter group comprising at least one sensor of
the plurality of sensors; a pattern recognition circuit structured
to determine a recognized pattern value in response to a least a
portion of the plurality of sensor data values; and a system
characterization circuit structured to provide a system
characterization value for the distillation column in response to
the recognized pattern value. The plurality of components may
include a thermodynamic treatment component, and wherein the system
characterization value comprises at least one value selected from
the values consisting of: determining a prediction value for the
thermodynamic treatment component; determining a future state value
for the thermodynamic treatment component; determining an
anticipated maintenance health state information for the
thermodynamic treatment component; and determining a process rate
limitation according to a capacity of the thermodynamic treatment
component. The thermodynamic treatment component may include at
least one of a compressor or a boiler.
[0771] In embodiments, a system for data collection in an
industrial environment may comprise: a chemical process system
comprising a plurality of components, and a plurality of sensors
each operatively coupled to at least one of the plurality of
components; a sensor communication circuit structured to interpret
a plurality of sensor data values in response to a sensed parameter
group, the sensed parameter group comprising at least one sensor of
the plurality of sensors; a pattern recognition circuit structured
to determine a recognized pattern value in response to a least a
portion of the plurality of sensor data values; and a system
characterization circuit structured to provide a system
characterization value for the chemical process system in response
to the recognized pattern value. The chemical process system may
include one of a chemical plant, a pharmaceutical plant, or an oil
refinery. The system characterization value may include at least
one value selected from the values consisting of: a separation
process value comprising at least one of a capacity value or a
purity value; a side reaction process value comprising a side
reaction rate value; and a thermodynamic treatment value comprising
one of a capability, a capacity, and an anticipated maintenance
health for a thermodynamic treatment component.
[0772] A system for data collection in an industrial environment,
the system comprising:
[0773] an irrigation system comprising a plurality of components
including a pump, and a plurality of sensors each operatively
coupled to at least one of the plurality of components; a sensor
communication circuit structured to interpret a plurality of sensor
data values in response to a sensed parameter group, the sensed
parameter group comprising at least one sensor of the plurality of
sensors; a pattern recognition circuit structured to determine a
recognized pattern value in response to a least a portion of the
plurality of sensor data values; and a system characterization
circuit structured to provide a system characterization value for
the irrigation system in response to the recognized pattern value.
The system characterization value may include at least one of an
anticipated maintenance health value for the pump and a future
state value for the pump. The pattern recognition circuit may
determine an off-nominal process condition in response to the at
least a portion of the plurality of sensor data values, and wherein
the sensor communication circuit is further structured to change
the sensed parameter group in response to the off-nominal process
condition. The off-nominal process condition may include an
indication of below normal water feed availability, and wherein the
updated sensed parameter group comprises at least one sensor
selected from the sensors consisting of: a water level sensor, a
humidity sensor, and an auxiliary water level sensor.
[0774] As described elsewhere herein, feedback to various
intelligent and/or expert systems, control systems (including
remote and local systems, autonomous systems, and the like), and
the like, which may comprise rule-based systems, model-based
systems, artificial intelligence (AI) systems (including neural
nets, self-organizing systems, and others described throughout this
disclosure), and various combinations and hybrids of those
(collectively referred to herein as the "expert system" except
where context indicates otherwise), may include a wide range of
information, including measures such as utilization measures,
efficiency measures (e.g., power, financial such as reduction of
costs), measures of success in prediction or anticipation of states
(e.g., avoidance and mitigation of faults), productivity measures
(e.g., workflow), yield measures, profit measures, and the like, as
described herein. In embodiments feedback to the expert system may
be industry-specific, domain-specific, factory-specific,
machine-specific and the like.
[0775] Industry-specific feedback for the expert system may be
offered by a third party, such as a repair and maintenance
organization, manufacturer, one or more consortia, and the like, or
may be generated by one or more elements of the subject system
itself. Industry-specific feedback may be aggregated, such as into
one or more data structures, wherein the data are aggregated at the
component level, equipment level, factory/installation level,
and/or industry level. Users of the data structure(s) may access
data at any level (e.g., component, equipment, factory, industry,
etc.) Users may search the data structure(s) for
indicators/predictors based on or filtered by system conditions
specific to their need, or update an indicator/predictor with
proprietary data to customize the data structure to their industry.
In embodiments, the expert system may be seeded with
industry-specific feedback, such as in a deep learning fashion, to
provide an anticipated outcome or state and/or to perform actions
to optimize specific machines, devices, components, processes, and
the like.
[0776] In embodiments, feedback provided to the expert system may
be used in one or more smart bands to predict progress towards one
or more goals. The expert system may use the feedback to determine
a modification, alteration, addition, change, or the like to one or
more components of the system that provided the feedback, as
described elsewhere herein. Based on the industry-specific
feedback, the expert system may alter an input, a way of treating
or storing an input or output, a sensor or sensors used to provide
feedback, an operating parameter, a piece of equipment used in the
system, or any other aspect of the participants in the industrial
system that gave rise to the feedback. As described elsewhere
herein, the expert system may track multiple goals, such as with
one or more smart bands. Industry-specific feedback may be used in
or by the smart bands in predicting an outcome or state relating to
the one or more goals, and to recommend or instruct a change that
is directed in increasing a likelihood of achieving the outcome or
state.
[0777] For example, a mixer may be used in a food processing
environment or in a chemical processing environment, but the
feedback that is relevant in the food processing plant (e.g.,
required sterilization temperatures, food viscosity, particle
density (e.g., such as measured by an optical sensor), completion
of cooking (e.g., completion of reactions involved in baking),
sanitation (e.g., absence of pathogens) may be different than what
is relevant in the chemical processing plant (e.g., impeller speed,
velocity vectors, flow rate, absence of high contaminant levels, or
the like). This industry specific feedback is useful in optimizing
the operation of the mixer in its particular environment.
[0778] In another example, the expert system may use feedback from
agricultural systems to train a model related to an irrigation
system deployed in a field, wherein the industry-specific feedback
relates to one or more of an amount of water used across the
industry (e.g., such as measured by a flowmeter), a trend of water
usage over a time period (e.g., such as measured by a flowmeter), a
harvest amount (e.g., such as measured by a weight scale), an
insect infestation (e.g., such as identified and/or measured by a
drone imaging), a plant death (e.g., such as identified and/or
measured by drone imaging), and the like.
[0779] In another example of a fluid flow system (e.g., fan, pump
or compressor) controlling cooling in the manufacturing industry,
the expert system may use feedback from manufacturing of components
involving materials (e.g., polymers) that require cooling during
the manufacturing process, such as one or more of quality of output
product, strength of output product, flexibility of output product,
and the like (e.g., such as measured by a suite of sensors,
including densitometer, viscometer, size exclusion chromatograph,
and torque meter). If the sensors indicate that the polymer is
cooling too quickly during monomer conversion, the expert system
may relay an instruction to one or more of a fan, pump, or
compressor in the fluid flow system to decrease an aspect of its
operation in order to meet a quality goal.
[0780] In another example of a reciprocating compressor operating
in a refinery performing refinery processes (e.g., hydrotreating,
hydrocracking, isomerization, reforming), the expert system may use
feedback related to one or more of an amount of sulfur, nitrogen
and/or aromatics downstream of the compressor (e.g., such as
measured by a near infrared ("IR") analyzer), the cetane/octane
number or smoke point of a product (e.g., such as with an octane
analyzer), the density of a product (e.g., such as measured by a
densitometer), byproduct gas amounts (e.g., such as measured by an
electrochemical gas sensor), and the like. In this example, as
feedback is received during isomerization of butane to isobutene by
an inline near IR analyzer measuring the amount and/or quality of
isobutene, the expert system may determine that the performance of
one or more components of the isomerization system, including the
reciprocating compressor, should be altered in order to meet a
production goal.
[0781] In another example of a vacuum distillation unit operating
in a refinery, the expert system may use feedback related to an
amount of raw gasoline recovered (e.g., such as by measuring the
volume or composition of various fractions using IR), boiling point
of recovered fractions (e.g., such as with a boiling point
analyzer), a vapor cooling rate (e.g., such as measured by
thermometer), and the like. In this example, as feedback is
received during vacuum distillation to recover diesel, as the
amounts recovered indicate off-nominal rations of production, the
expert system may instruct the vacuum distillation unit to alter a
feedstock source and initiate more detailed analysis of the prior
feedstock.
[0782] In yet another example of a pipeline in a refinery, the
expert system may use feedback related to flow type (e.g., bubble,
stratified, slug, annular, transition, mist) of hydrocarbon
products (e.g., such as measured by dye tracing), flow rate, vapor
velocity (such as with a flow meter), vapor shear, and the like. In
this example, as feedback is received during operation of the
pipeline regarding the flow type and its rate, modifications may be
recommended by the expert system to improve the flow through the
pipeline.
[0783] In still another example of a paddle-type or anchor-type
agitator/mixer in a pharmaceutical plant, the expert system may use
feedback related to degree of mixing of high-viscosity liquids,
heating of medium- to low-viscosity liquids, a density of the
mixture, a growth rate of an organism in the mixture, and the like.
In this example, as feedback is received during operation of the
agitator that a bacterial growth rate is too high (such as measured
with a spectrophotometer), the expert system may instruct the
agitator to reduce its speed to limit the amount of air being added
to the mixture or growth substrate.
[0784] In a further example of a pressure reactor in a chemical
processing plant, the expert system may use feedback related to a
catalytic reaction rate (such as measured by a mass spectrometer),
a particle density (such as measured by a densitometer), a
biological growth rate (such as measured by a spectrophotometer),
and the like. In this example, as feedback is received during
operation of the pressure reactor that the particle density and
biological growth rate are off-nominal, the expert system may
instruct the pressure reactor to modify one or more operational
parameters, such as a reduction in pressure, an increase in
temperature, an increase in volume of the reaction, and the
like.
[0785] In another example of a gas agitator operating in a chemical
processing plant, the expert system may use feedback related to
effective density of a gassed liquid, a viscosity, a gas pressure,
and the like, as measured by appropriate sensors or equipment. In
this example, as feedback is received during operation of the gas
agitator, the expert system may instruct the gas agitator to modify
one or more operational parameters, such as to increase or decrease
a rate of agitation.
[0786] In still another example of a pump blasting liquid type
agitator in a chemical processing plant, the expert system may use
feedback related to a viscosity of a mixture, an optical density of
a growth medium, and a temperature of a solution. In this example,
as feedback is received during operation of the agitator, the
expert system may instruct the agitator to modify one or more
operational parameters, such as to increase or decrease a rate of
agitation and/or inject additional heat.
[0787] In yet another example of a turbine type agitator in a
chemical processing plant, the expert system may use feedback
related to a vibration noise, a reaction rate of the reactants, a
heat transfer, or a density of a suspension. In this example, as
feedback is received during operation of the agitator, the expert
system may instruct the agitator to modify one or more operational
parameters, such as to increase or decrease a rate of agitation
and/or inject an additional amount of catalyst.
[0788] In yet another example of a static agitator mixing monomers
in a chemical processing plant to produce a polymer, the expert
system may use feedback related to the viscosity of the polymer,
color of the polymer, reactivity of the polymer and the like to
iterate to a new setting or parameter for the agitator, such as for
example, a setting that alters the Reynolds number, an increase in
temperature, a pressure increase, and the like.
[0789] In a further example of a catalytic reactor in a chemical
processing plant, the expert system may use feedback related to a
reaction rate, a product concentration, a product color, and the
like. In this example, as feedback is received during operation of
the catalytic reactor, the expert system may instruct the reactor
to modify one or more operational parameters, such as to increase
or decrease a temperature and/or inject an additional amount of
catalyst.
[0790] In yet a further example of a thermic heating systems in a
chemical processing or food plant, the expert system may use
feedback related to BTUs out of the system, a flow rate, and the
like. In this example, as feedback is received during operation of
the thermic heating system, the expert system may instruct the
system to modify one or more operational parameters, such as to
change the input feedstock, to increase the flow of the feedstock,
and the like.
[0791] In still a further example of using boiler feed water in a
refinery, the expert system may use feedback related to an aeration
level, a temperature, and the like. In this example, as feedback is
received related to the boiler feed water, the expert system may
instruct the system to modify one or more operational parameters of
a boiler, such as to increase a reduction in aeration, to increase
the flow of the feed water, and the like.
[0792] In still a further example of a storage tank in a refinery,
the expert system may use feedback related to a temperature, a
pressure, a flow rate out of the tank, and the like. In this
example, as feedback is received related to the storage tank, the
expert system may instruct the system to modify one or more
operational parameters of, such as to increase cooling or heating
begin agitation, and the like.
[0793] In an example of a condensate/make-up water system in a
power station that condenses steam from turbines and recirculates
it back to a boiler feeder along with make-up water, the expert
system may use feedback related to measuring inward air leaks, heat
transfer, and make-up water quality. In this example, as feedback
is received related to the condensate/make-up water system, the
expert system may instruct the system to increase a purification of
the make-up water, bring a vacuum pump online, and the like.
[0794] In another example of a stirrer in a food plant, the expert
system may use feedback related to a viscosity of the food, a color
of the food, a temperature of the food, and the like. In this
example, as feedback is received, the expert system may instruct
the stirrer to speed up or slow down, depending on the predicted
success in reaching a goal.
[0795] In another example of a pressure cooker in a food plant, the
expert system may use feedback related to a viscosity of the food,
a color of the food, a temperature of the food, and the like. In
this example, as feedback is received, the expert system may
instruct the pressure cooker to continue operating, increase a
temperature, or the like, depending on the predicted success in
reaching a goal.
[0796] In an embodiment (FIG. 74), a system 11100 for data
collection in an industrial environment may include a plurality of
input sensors 11102 communicatively coupled to a controller 11106,
a data collection circuit 11104 structured to collect output data
11108 from the input sensors 11102, and a machine learning data
analysis circuit 11110 structured to receive the output data 11108
and learn received output data patterns 11112 indicative of an
outcome, wherein the machine learning data analysis circuit 11110
is structured to learn received output data patterns 11112 by being
seeded with a model 11114 based on industry-specific feedback
11118. The model 11114 may be a physical model, an operational
model, or a system model. The industry-specific feedback 11118 may
be one or more of a utilization measure, an efficiency measure
(e.g., power and/or financial), a measure of success in prediction
or anticipation of states (e.g., an avoidance and mitigation of
faults), a productivity measure (e.g., a workflow), a yield
measure, and a profit measure. The industry-specific feedback 11118
includes an amount of power generated by a machine about which the
input sensors provide information during operation of the machine.
The industry-specific feedback 11118 includes a measure of the
output of an assembly line about which the input sensors provide
information. The industry-specific feedback 11118 includes a
failure rate of units of product produced by a machine about which
the input sensors provide information. The industry-specific
feedback 11118 includes a fault rate of a machine about which the
input sensors provide information. The industry-specific feedback
11118 includes the power utilization efficiency of a machine about
which the input sensors provide information, wherein the machine is
one of a turbine, a transformer, a generator, a compressor, one
that stores energy, and one that includes power train components
(e.g., the rate of extraction of a material by a machine about
which the input sensors provide information, the rate of production
of a gas by a machine about which the input sensors provide
information, the rate of production of a hydrocarbon product by a
machine about which the input sensors provide information), and the
rate of production of a chemical product by a machine about which
the input sensors provide information. The machine learning data
analysis circuit 11110 may be further structured to learn received
output data patterns 11112 based on the outcome. The system 11100
may keep or modify operational parameters or equipment. The
controller 11106 may adjust the weighting of the machine learning
data analysis circuit 11110 based on the learned received output
data patterns 11112 or the outcome, collect more/fewer data points
from the input sensors based on the learned received output data
patterns 11112 or the outcome, change a data storage technique for
the output data 11108 based on the learned received output data
patterns 11112 or the outcome, change a data presentation mode or
manner based on the learned received output data patterns 11112 or
the outcome, and apply one or more filters (low pass, high pass,
band pass, etc.) to the output data 11108. In embodiments, the
system 11100 may remove/re-task under-utilized equipment based on
one or more of the learned received output data patterns 11112 and
the outcome. The machine learning data analysis circuit 11110 may
include a neural network expert system. The input sensors may
measure vibration and noise data. The machine learning data
analysis circuit 11110 may be structured to learn received output
data patterns 11112 indicative of progress/alignment with one or
more goals/guidelines (e.g., which may be determined by a different
subset of the input sensors). The machine learning data analysis
circuit 11110 may be structured to learn received output data
patterns 11112 indicative of an unknown variable. The machine
learning data analysis circuit 11110 may be structured to learn
received output data patterns 11112 indicative of a preferred input
among available inputs. The machine learning data analysis circuit
11110 may be structured to learn received output data patterns
11112 indicative of a preferred input data collection band among
available input data collection bands. The machine learning data
analysis circuit 11110 may be disposed in part on a machine, on one
or more data collectors, in network infrastructure, in the cloud,
or any combination thereof. The system 11100 may be deployed on the
data collection circuit 11104. The system 11100 may be distributed
between the data collection circuit 11104 and a remote
infrastructure. The data collection circuit 11104 may include a
data collector.
[0797] In embodiments, a system 11100 for data collection in an
industrial environment may include a plurality of input sensors
11102 communicatively coupled to a controller 11106, a data
collection circuit 11104 structured to collect output data 11108
from the input sensors, and a machine learning data analysis
circuit 11110 structured to receive the output data 11108 and learn
received output data patterns 11112 indicative of an outcome,
wherein the machine learning data analysis circuit 11110 is
structured to learn received output data patterns 11112 by being
seeded with a model 11114 based on a utilization measure.
[0798] In embodiments, a system 11100 for data collection in an
industrial environment may include a plurality of input sensors
11102 communicatively coupled to a controller 11106, a data
collection circuit 11104 structured to collect output data 11108
from the input sensors, and a machine learning data analysis
circuit 11110 structured to receive the output data 11108 and learn
received output data patterns 11112 indicative of an outcome,
wherein the machine learning data analysis circuit 11110 is
structured to learn received output data patterns 11112 by being
seeded with a model 11114 based on an efficiency measure.
[0799] In embodiments, a system 11100 for data collection in an
industrial environment may include a plurality of input sensors
11102 communicatively coupled to a controller 11106, a data
collection circuit 11104 structured to collect output data 11108
from the input sensors, and a machine learning data analysis
circuit 11110 structured to receive the output data 11108 and learn
received output data patterns 11112 indicative of an outcome,
wherein the machine learning data analysis circuit 11110 is
structured to learn received output data patterns 11112 by being
seeded with a model 11114 based on a measure of success in
prediction or anticipation of states.
[0800] In embodiments, a system 11100 for data collection in an
industrial environment may include a plurality of input sensors
11102 communicatively coupled to a controller 11106, a data
collection circuit 11104 structured to collect output data 11108
from the input sensors, and a machine learning data analysis
circuit 11110 structured to receive the output data 11108 and learn
received output data patterns 11112 indicative of an outcome,
wherein the machine learning data analysis circuit 11110 is
structured to learn received output data patterns 11112 by being
seeded with a model 11114 based on a productivity measure.
[0801] Clause 1. In embodiments, a system for data collection in an
industrial environment, comprising: a plurality of input sensors
communicatively coupled to a controller; a data collection circuit
structured to collect output data from the input sensors; and a
machine learning data analysis circuit structured to receive the
output data and learn received output data patterns indicative of
an outcome, wherein the machine learning data analysis circuit is
structured to learn received output data patterns by being seeded
with a model based on industry-specific feedback. 2. The system of
clause 1, wherein the model is a physical model, an operational
model, or a system model. 3. The system of clause 1, wherein the
industry-specific feedback is a utilization measure. 4. The system
of clause 1, wherein the industry-specific feedback is an
efficiency measure. 5. The system of clause 4, wherein the
efficiency measure is one of power and financial. 6. The system of
clause 1, wherein the industry-specific feedback is a measure of
success in prediction or anticipation of states. 7. The system of
clause 6, wherein the measure of success is an avoidance and
mitigation of faults. 8. The system of clause 1, wherein the
industry-specific feedback is a productivity measure. 9. The system
of clause 8, wherein the productivity measure is a workflow. 10.
The system of clause 1, wherein the industry-specific feedback is a
yield measure. 11. The system of clause 1, wherein the
industry-specific feedback is a profit measure. 12. The system of
clause 1, wherein the machine learning data analysis circuit is
further structured to learn received output data patterns based on
the outcome. 13. The system of clause 1, wherein the system keeps
or modifies operational parameters or equipment. 14. The system of
clause 1, wherein the controller adjusts the weighting of the
machine learning data analysis circuit based on the learned
received output data patterns or the outcome. 15. The system of
clause 1, wherein the controller collects more/fewer data points
from the input sensors based on the learned received output data
patterns or the outcome. 16. The system of clause 1, wherein the
controller changes a data storage technique for the output data
based on the learned received output data patterns or the outcome.
17. The system of clause 1, wherein the controller changes a data
presentation mode or manner based on the learned received output
data patterns or the outcome. 18. The system of clause 1, wherein
the controller applies one or more filters (low pass, high pass,
band pass, etc.) to the output data. 19. The system of clause 1,
wherein the system removes/re-tasks under-utilized equipment based
on one or more of the learned received output data patterns and the
outcome. 20. The system of clause 1, wherein the machine learning
data analysis circuit comprises a neural network expert system. 21.
The system of clause 1, wherein the input sensors measure vibration
and noise data. 22. The system of clause 1, wherein the machine
learning data analysis circuit is structured to learn received
output data patterns indicative of progress/alignment with one or
more goals/guidelines. 23. The system of clause 22, wherein
progress/alignment of each goal/guideline is determined by a
different subset of the input sensors. 24. The system of clause 1,
wherein the machine learning data analysis circuit is structured to
learn received output data patterns indicative of an unknown
variable. 25. The system of clause 1, wherein the machine learning
data analysis circuit is structured to learn received output data
patterns indicative of a preferred input among available inputs.
26. The system of clause 1, wherein the machine learning data
analysis circuit is structured to learn received output data
patterns indicative of a preferred input data collection band among
available input data collection bands. 27. The system of clause 1,
wherein the machine learning data analysis circuit is disposed in
part on a machine, on one or more data collectors, in network
infrastructure, in the cloud, or any combination thereof. 28. The
system of clause 1, wherein the system is deployed on the data
collection circuit. 29. The system of clause 1, wherein the system
is distributed between the data collection circuit and a remote
infrastructure. 30. The system of clause 1, wherein the
industry-specific feedback includes an amount of power generated by
a machine about which the input sensors provide information during
operation of the machine. 31. The system of clause 1, wherein the
industry-specific feedback includes a measure of the output of an
assembly line about which the input sensors provide information.
32. The system of clause 1, wherein the industry-specific feedback
includes a failure rate of units of product produced by a machine
about which the input sensors provide information. 33. The system
of clause 1, wherein the industry-specific feedback includes a
fault rate of a machine about which the input sensors provide
information. 34. The system of clause 1, wherein the
industry-specific feedback includes the power utilization
efficiency of a machine about which the input sensors provide
information. 35. The system of clause 34, wherein the machine is a
turbine. 36. The system of clause 34, wherein the machine is a
transformer. 37. The system of clause 34, wherein the machine is a
generator. 38. The system of clause 34, wherein the machine is a
compressor. 39. The system of clause 34, wherein the machine stores
energy. 40. The system of clause 1, wherein the machine includes
power train components. 41. The system of clause 34, wherein the
industry-specific feedback includes the rate of extraction of a
material by a machine about which the input sensors provide
information. 42. The system of clause 34, wherein the
industry-specific feedback includes the rate of production of a gas
by a machine about which the input sensors provide information. 43.
The system of clause 34, wherein the industry-specific feedback
includes the rate of production of a hydrocarbon product by a
machine about which the input sensors provide information. 44. The
system of clause 34, wherein the industry-specific feedback
includes the rate of production of a chemical product by a machine
about which the input sensors provide information. 45. The system
of clause 1, wherein the data collection circuit comprises a data
collector. 46. A system for data collection in an industrial
environment, comprising: a plurality of input sensors
communicatively coupled to a controller; a data collection circuit
structured to collect output data from the input sensors; and a
machine learning data analysis circuit structured to receive the
output data and learn received output data patterns indicative of
an outcome, wherein the machine learning data analysis circuit is
structured to learn received output data patterns by being seeded
with a model based on a utilization measure. 47. A system for data
collection in an industrial environment, comprising: a plurality of
input sensors communicatively coupled to a controller; a data
collection circuit structured to collect output data from the input
sensors; and a machine learning data analysis circuit structured to
receive the output data and learn received output data patterns
indicative of an outcome, wherein the machine learning data
analysis circuit is structured to learn received output data
patterns by being seeded with a model based on an efficiency
measure. 48. A system for data collection in an industrial
environment, comprising: a plurality of input sensors
communicatively coupled to a controller; a data collection circuit
structured to collect output data from the input sensors; and a
machine learning data analysis circuit structured to receive the
output data and learn received output data patterns indicative of
an outcome, wherein the machine learning data analysis circuit is
structured to learn received output data patterns by being seeded
with a model based on a measure of success in prediction or
anticipation of states. 49. A system for data collection in an
industrial environment, comprising: a plurality of input sensors
communicatively coupled to a controller; a data collection circuit
structured to collect output data from the input sensors; and a
machine learning data analysis circuit structured to receive the
output data and learn received output data patterns indicative of
an outcome, wherein the machine learning data analysis circuit is
structured to learn received output data patterns by being seeded
with a model based on a productivity measure.
[0802] In embodiments, a system for data collection in an
industrial environment may include an expert system graphical user
interface in which a user may, by interacting with a graphical user
interface element, set a parameter of a data collection band for
collection by a data collector. The parameter may relate to at
least one of setting a frequency range for collection and setting
an extent of granularity for collection.
[0803] In embodiments, a system for data collection in an
industrial environment may include an expert system graphical user
interface in which a user may, by interacting with a graphical user
interface element, identify a set of sensors among a larger set of
available sensors for collection by a data collector. The user
interface may include views of available data collectors, their
capabilities, one or more corresponding smart bands, and the
like.
[0804] In embodiments, a system for data collection in an
industrial environment may include an expert system graphical user
interface in which a user may, by interacting with a graphical user
interface element, select a set of inputs to be multiplexed among a
set of available inputs.
[0805] In embodiments, a system for data collection in an
industrial environment may include an expert system graphical user
interface in which a user may, by interacting with a graphical user
interface element, select a component of an industrial machine
displayed in the graphical user interface for data collection, view
a set of sensors that are available to provide data about the
industrial machine, and select a subset of sensors for data
collection.
[0806] In embodiments, a system for data collection in an
industrial environment may include an expert system graphical user
interface in which a user may, by interacting with a graphical user
interface element, view a set of indicators of fault conditions of
one or more industrial machines, where the fault conditions are
identified by application of an expert system to data collected
from a set of data collectors. In embodiments, the fault conditions
may be identified by manufacturers of portions of the one or more
industrial machines. The fault conditions may be identified by
analysis of industry trade data, third-party testing agency data,
industry standards, and the like. In embodiments, a set of
indicators of fault conditions of one or more industrial machines
may include indicators of stress, vibration, heat, wear, ultrasonic
signature, operational deflection shape, and the like, optionally
including any of the widely varying conditions that can be sensed
by the types of sensors described throughout this disclosure and
the documents incorporated herein by reference.
[0807] In embodiments, a system for data collection in an
industrial environment may include an expert graphical user
interface that enables a user to select from a list of component
parts of an industrial machine for establishing smart-band
monitoring and in response thereto presents the user with at least
one smart-band definition of an acceptable range of values for at
least one sensor of the industrial machine and a list of correlated
sensors from which data will be gathered and analyzed when an out
of acceptable range condition is detected from the at least one
sensor.
[0808] In embodiments, a system for data collection in an
industrial environment may include an expert graphical user
interface that enables a user to select from a list of conditions
of an industrial machine for establishing smart-band monitoring
and, in response thereto, presents the user with at least one
smart-band definition of an acceptable range of values for at least
one sensor of the industrial machine and a list of correlated
sensors from which data will be gathered and analyzed when an out
of acceptable range condition is detected from the at least one
sensor.
[0809] In embodiments, a system for data collection in an
industrial environment may include an expert graphical user
interface that enables a user to select from a list of reliability
measures of an industrial machine for establishing smart-band
monitoring and, in response thereto, presents the user with at
least one smart-band definition of an acceptable range of values
for at least one sensor of the industrial machine and a list of
correlated sensors from which data will be gathered and analyzed
when an out of acceptable range condition is detected from the at
least one sensor. In the system, the reliability measures may
include one or more of industry average data, manufacturer's
specifications, material specifications, recommendations, and the
like. In embodiments, reliability measures may include measures
that correlate to failures, such as stress, vibration, heat, wear,
ultrasonic signature, operational deflection shape effect, and the
like. In embodiments, manufacturer's specifications may include
cycle count, working time, maintenance recommendations, maintenance
schedules, operational limits, material limits, warranty terms, and
the like. In embodiments, the sensors in the industrial environment
may be correlated to manufacturer's specifications by associating a
condition being sensed by the sensor to a specification type. In
embodiments, a non-limiting example of correlating a sensor to a
manufacturer's specification may include a duty cycle specification
being correlated to a sensor that detects revolutions of a moving
part. In embodiments, a temperature specification may correlate to
a thermal sensor disposed to sense an ambient temperature proximal
to the industrial machine.
[0810] In embodiments, a system for data collection in an
industrial environment may include an expert graphical user
interface that automatically creates a smart-band group of sensors
disposed in the industrial environment in response to receiving a
condition of the industrial environment for monitoring and an
acceptable range of values for the condition.
[0811] In embodiments, a system for data collection in an
industrial environment may include an expert graphical user
interface that presents a representation of components of an
industrial machine deployable in the industrial environment on an
electronic display, and in response to a user selecting one or more
of the components, searches a database of industrial machine
failure modes for modes involving the selected component(s) and
conditions associated with the failure mode(s) to be monitored, and
further identifies a plurality of sensors in, on, or available to
be disposed on the presented machine representation from which data
will automatically be captured when the condition(s) to be
monitored are detected to be outside of an acceptable range. In
embodiments, the identified plurality of sensors includes at least
one sensor through which the condition(s) will be monitored.
[0812] In embodiments, a system for data collection in an
industrial environment may include a user interface for working
with smart bands that may facilitate a user identifying sensors to
include in a smart band group of sensors by selecting sensors
presented on a map of a machine in the environment. A user may be
guided to select among a subset of all possible sensors based on
smart band criteria, such as types of sensor data required for the
smart band. A smart band may be focused on detecting trending
conditions in a portion of the industrial environment; therefore,
the user interface may direct the user choose among an identified
subset of sensors, such as by only allowing sensors proximal to the
smart band directed portion of the environment to be selectable in
the user interface.
[0813] In embodiments, a smart band data collection configuration
and deployment user interface may include views of components in an
industrial environment and related available sensors. In
embodiments, in response to selection of a component part of an
industrial machine depicted in the user interface, sensors
associated with smart band data collection for the component part
may be highlighted so that the user may select one or more of the
sensors. User selection in this context may comprise a verification
of an automatic selection of sensors, or manually identifying
sensors to include in the smart band sensor group.
[0814] In embodiments, in response to selection of a smart band
condition, such as trending of bearing temperature, a user
interface for smart band configuration and use may automatically
identify and present sensors that contribute to smart band analysis
for the condition. A user may responsive to this presentation of
sensors, confirm or otherwise acknowledge one or more sensors
individually or as a set to be included in the smart band data
collection group.
[0815] In embodiments, a smart band user interface may present
locations of industrial machines in an industrial environment on a
map. The locations may be annotated with indicators of smart band
data collection templates that are configured for collecting smart
band data for the machines at the annotated locations. The
locations may be color coded to reflect a degree of smart band
coverage for a machine at the location. In embodiments, a location
of a machine with a high degree of smart band coverage may be
colored green, whereas a location of a machine with low smart band
coverage may be colored red or some other contrasting color. Other
annotations, such as visual annotations may be used. A user may
select a machine at a location and by dragging the selected machine
to a location of a second machine, effectively configure smart
bands for the second machine that correspond to smart bands for the
first machine. In this way, a user may configure several smart band
data collection templates for a newly added machine or a new
industrial environment and the like.
[0816] In embodiments, various configurations and selections of
smart bands may be stored for use throughout a data collection
platform, such as for selecting templates for sensing, templates
for routing, provisioning of devices and the like, as well as for
direct the placement of sensors, such as by personnel or by
machines, such as autonomous or remote-control drones.
[0817] In embodiments, a smart band user interface may present a
map of an industrial environment that may include industrial
machines, machine-specific data collectors, mobile data collectors
(robotic and human), and the like. A user may view a list of smart
band data collection actions to be performed and may select a data
collection resource set to undertake the collection. In an example,
a guided mobile robot may be equipped with data collection systems
for collecting data for a plurality of smart band data sets. A user
may view an industrial environment with which the robot is
associated and assign the robot to perform a smart band data
collection activity by selecting the robot, a smart band data
collection template, and a location in the industrial environment,
such as a machine or a part of a machine. The user interface may
provide a status of the collection undertaking so that the user can
be informed when the data collection is complete.
[0818] In embodiments, a smart band operation management user
interface may include presentation of smart band data collection
activity, analysis of results, actions taken based on results,
suggestions for changes to smart band data collection (e.g.,
addition of sensors to a smart band collection template, increasing
duration of data collection for a template-specific collection
activity), and the like. The user interface may facilitate "what
if" type analysis by presenting potential impacts on reliability,
costs, resource utilization, data collection tradeoffs, maintenance
schedule impacts, risk of failure (increase/decrease), and the like
in response to a user's attempt to make a change to a smart band
data collection template, such as a user relaxing a threshold for
performing smart band data collection and the like. In embodiments,
a user may select or enter a target budget for preventive
maintenance per unit time (e.g., per month, quarter, and the like)
into the user interface and an expert system of the user interface
may recommend a smart band data collection template and thresholds
for complying with the budget.
[0819] In embodiments, a smart band user interface may facilitate a
user configuring a system for data collection in an industrial
environment for smart band data gathering. The user interface may
include display of industrial machine components, such as motors,
linkages, bearings, and the like that a user may select. In
response to such a selection, an expert system may work with the
user interface to present a list of potential failure conditions
related to the part to monitor. The user may select one or more
conditions to monitor. The user interface may present the
conditions to monitor as a set that the user may be asked to
approve. The user may indicate acceptance of the set or of select
conditions in the set monitor. As a follow-on to a user
selection/approval of one or more conditions to monitor, the user
interface may display a map of relevant sensors available in the
industrial environment for collecting data as a smart band group of
sensors. The relevant sensors may be associated with one or more
parts (e.g., the part(s) originally selected by the user), one or
more failure conditions, and the like.
[0820] In embodiments, the expert system may compare the relevant
sensors in the environment to a preferred set of sensors for smart
band monitoring of the failure condition(s) and provide feedback to
the user, such as a confidence factor for performing smart band
monitoring based on the available sensors for the failure
condition(s). The user may evaluate the failure condition and smart
band analysis information presented and may take an action in the
user interface, such as approving the relevant sensors. In
response, a smart band data collection template for configuring the
data collection system may be created. In embodiments, a smart band
data collection template may be created independently of a user
approval. In such embodiments, the user may indicate explicitly or
implicitly via approval of the smart band analysis information an
approval of the created template.
[0821] In embodiments, a smart band user interface may work with an
expert system to present candidate portions of an industrial
machine in an industrial environment for smart band condition
monitoring based on information such as manufacturer's
specifications, statistical information derived from real-world
experience with similar industrial machines, and the like. In
embodiments, the user interface may permit a user to select certain
aspects of the smart band data collection and analysis process
including--for example, a degree of reliability/failure risk to
monitor (e.g., near failure, best performance, industry average,
and the like). In response thereto, the expert system may adjust an
aspect of the smart band analysis, such as a range of acceptable
value to monitor, a monitor frequency, a data collection frequency,
a data collection amount, a priority for the data collection
activity (e.g., effectively a priority of a template for data
collection for the smart band), weightings of data from sensors
(e.g., specific sensors in the group, types of sensors, and the
like).
[0822] In embodiments, a smart bands user interface may be
structured to allow a user to let an expert system recommend one or
more smart bands to implement based on a range of comparative data
that the user might prioritize, such as industry average data,
industry best data, near-by comparable machines, most similarly
configured machines, and the like. Based on the comparative data
weighting, the expert system may use the user interface to
recommend one or more smart band templates that align with the
weighting to the user, who may take an action in the user
interface, such as approving one or more of the recommended
templates for use.
[0823] In embodiments, a user interface for configuring arrangement
of sensors in an industrial environment may include recommendations
by industrial environment equipment suppliers (e.g., manufacturers,
wholesalers, distributors, dealers, third-party consultants, and
the like) of group(s) of sensors to include for performing smart
band analysis of components of the industrial equipment. The
information may be presented to a user as data collection
template(s) that the user may indicate as being accepted/approved,
such as by positioning a graphic representing a template(s) over a
portion of the industrial equipment.
[0824] In embodiments, a smart band discovery portal may facilitate
sharing of smart band related information, such as recommendations,
actual use cases, results of smart band data collection and
processing, and the like. The discovery portal may be embodied as a
panel in a smart band user interface.
[0825] In embodiments, a smart band assessment portal may
facilitate assessment of smart band-based data collection and
analysis. Content that may be presented in such a portal may
include depictions of uses of existing smart band templates for one
or more industrial machines, industrial environments, industries,
and the like. A value of a smart band may be ascribed to each smart
band in the portal based, for example, on historical use and
outcomes. A smart band assessment portal may also include
visualization of candidate sensors to include in a smart band data
collection template based on a range of factors including ascribed
value, preventive maintenance costs, failure condition being
monitored, and the like.
[0826] In embodiments, a smart bands graphical user interface
associated with a system for data collection in an industrial
environment may be deployed for industrial components, such as of
factory-based air conditioning units. A user interface of a system
for data collection for smart band analysis of air conditioning
units may facilitate graphical configuration of smart band data
collection templates and the like for specific air conditioning
system installations. In embodiments, major components of an air
conditioning system, such as a compressor, condenser, heat
exchanger, ducting, coolant regulators, filters, fans, and the like
along with corresponding sensors for a particular installation of
the air conditioning system may be depicted in a user interface. A
user may select one or more of these components in the user
interface for configuring a system for smart band data collection.
In response to the user selecting, for example, a coolant
compressor, sensors associated with the compressor may be
automatically identified in the user interface. The user may be
presented with a recommended data collection template to perform
smart band data collection for the selected compressor.
Alternatively, the user may request a candidate collection template
from a community of smart band users, such as through a smart band
template sharing panel of the user interface. Once a template is
selected, the user interface may offer the user customization
options, such as frequency of collection, degree of reliability to
monitor, and the like. Upon final acceptance of the template, the
user interface may interact with a data collection system of the
installed air conditioning system (if such a system is available)
to implement the data collection template and provide an indication
to the user of the result of implementing the template. In response
thereto, the user may make a final approval of the template for use
with the air conditioning unit.
[0827] In embodiments, a smart bands graphical user interface
associated with a system for data collection in an industrial
environment may be deployed for oil and gas refinery-based
chillers. A user interface of a system for data collection for
smart band analysis of refinery-based chillers may facilitate
graphical configuration of smart band data collection templates and
the like for specific refinery-based chiller installations. In
embodiments, major components of a refinery-based chiller including
heat exchangers, compressors, water regulators and the like along
with corresponding sensors for the particular installation of the
refinery-based chiller may be depicted in a user interface. A user
may select one or more of these components in the user interface
for configuring a system for smart band data collection. In
response to the user selecting, for example, water regulators,
sensors associated with the water regulators may be automatically
identified in the user interface. The user may be presented with a
recommended data collection template to perform smart band data
collection for the selected component. Alternatively, the user may
request a candidate collection template from a community of smart
band users, such as through a smart band template sharing panel of
the user interface. Once a template is selected, the user interface
may offer the user customization options, such as frequency of
collection, degree of reliability to monitor, and the like. Upon
final acceptance of the template, the user interface may interact
with a data collection system of the installed refinery-based
chiller (if such a system is available) to implement the data
collection template and provide an indication to the user of the
result of implementing the template. In response thereto, the user
may make a final approval of the template for use with the
refinery-based chiller.
[0828] In embodiments, a smart bands graphical user interface
associated with a system for data collection in an industrial
environment may be deployed for automotive production line robotic
assembly systems. A user interface of a system for data collection
for smart band analysis of production line robotic assembly systems
may facilitate graphical configuration of smart band data
collection templates and the like for specific production line
robotic assembly system installations. In embodiments, major
components of a production line robotic assembly system including
motors, linkages, tool handlers, positioning systems and the like
along with corresponding sensors for the particular installation of
the production line robotic assembly system may be depicted in a
user interface. A user may select one or more of these components
in the user interface for configuring a system for smart band data
collection. In response to the user selecting, for example, robotic
linkage sensors associated with the robotic linkages may be
automatically identified in the user interface. The user may be
presented with a recommended data collection template to perform
smart band data collection for the selected component.
Alternatively, the user may request a candidate collection template
from a community of smart band users, such as through a smart band
template sharing panel of the user interface. Once a template is
selected, the user interface may offer the user customization
options, such as frequency of collection, degree of reliability to
monitor, and the like. Upon final acceptance of the template, the
user interface may interact with a data collection system of the
installed production line robotic assembly system (if such a system
is available) to implement the data collection template and provide
an indication to the user of the result of implementing the
template. In response thereto, the user may make a final approval
of the template for use with the production line robotic assembly
system.
[0829] In embodiments, a smart bands graphical user interface
associated with a system for data collection in an industrial
environment may be deployed for automotive production line robotic
assembly systems. A user interface of a system for data collection
for smart band analysis of production line robotic assembly systems
may facilitate graphical configuration of smart band data
collection templates and the like for specific production line
robotic assembly system installations. In embodiments, major
components of construction site boring machinery, such as the
cutter head, which itself is a subsystem that may have many
components, control systems, debris handling and conveying
components, precast concrete delivery and installation subsystems
and the like along with corresponding sensors for the particular
installation of the production line robotic assembly system may be
depicted in a user interface. A user may select one or more of
these components in the user interface for configuring a system for
smart band data collection. In response to the user selecting, for
example, debris handling components sensors associated with the
debris handling components, such as a conveyer may be automatically
identified in the user interface. The user may be presented with a
recommended data collection template to perform smart band data
collection for the selected component. Alternatively, the user may
request a candidate collection template from a community of smart
band users, such as through a smart band template sharing panel of
the user interface. Once a template is selected, the user interface
may offer the user customization options, such as frequency of
collection, degree of reliability to monitor, and the like. Upon
final acceptance of the template, the user interface may interact
with a data collection system of the installed production line
robotic assembly system (if such a system is available) to
implement the data collection template and provide an indication to
the user of the result of implementing the template. In response
thereto, the user may make a final approval of the template for use
with the production line robotic assembly system.
[0830] Referring to FIG. 75, an exemplary user interface for smart
band configuration of a system for data collection in an industrial
environment is depicted. The user interface 11200 may include an
industrial environment visualization portion 11202 in which may be
depicted one or more sensors, machines, and the like. Each sensor,
machine, or portion thereof (e.g., motor, compressor, and the like)
may be selectable as part of a smart-band configuration process.
Likewise, each sensor, machine or portion thereof may be visually
highlighted during the smart-band configuration process, such as in
response to user selection, or automated identification as being
part of a group of smart band sensors. The user interface may also
include a smart band selection portion 11204 or panel in which
smart band indicators, failure modes, and the like may be depicted
in selectable elements. User selection of a symptom, failure mode
and the like may cause corresponding components, sensors, machines,
and the like in the industrial visualization portion to be
highlighted. The user interface may also include a customization
panel 11206 in which attributes of a selected smart band, such as
acceptable ranges, frequency of monitoring and the like may be made
available for a user to adjust.
[0831] Cause 1. In embodiments, a system comprising: a user
interface comprising: a selectable graphical element that
facilitates selection of a representation of a component of an
industrial machine from an industrial environment in which a
plurality of sensors is deployed from which a data collection
system collects data for the system for which the user interface
enables interaction; and selectable graphical elements representing
a portion of the plurality of sensors that facilitate selection of
a sensors to form a data collection subset of sensors in the
industrial environment. 2. The system of clause 1, wherein
selection of sensors to form a data collection subset results in a
data collection template adapted to facilitate configuring the data
routing and collection system for collecting data from the data
collection subset of sensors. 3. The system of clause 1, wherein
the user interface comprises an expert system that analyzes a user
selection of a graphical element that facilitates selection of a
component and adjusts the selectable graphical elements
representing a portion of the plurality of sensors to activate only
sensors associated with a component associated with the selected
graphical element. 4. The system of clause 1, wherein the
selectable graphical element that facilitates selection of a
component of an industrial machine further facilitates presentation
of a plurality of data collection templates associated with the
component. 5. The system of clause 1, wherein the portion of the
plurality of sensors comprises a smart band group of sensors. 6.
The system of clause 5, wherein the smart band group of sensors
comprises sensors for a component of the industrial machine
selected by the selectable graphical element. 7. A system
comprising: an expert graphical user interface comprising
representations of a plurality of components of an industrial
machine from an industrial environment in which a plurality of
sensors is deployed from which a data collection system collects
data for the system for which the user interface enables
interaction, wherein at least one representation of the plurality
of components is selectable by a user in the user interface; a
database of industrial machine failure modes; and a database
searching facility that searches the database of failure modes for
modes that correspond to a user selection of a component of the
plurality of components. 8. The system of clause 7, comprising a
database of conditions associated with the failure modes. 9. The
system of clause 8, wherein the database of conditions includes a
list of sensors in the industrial environment associated with the
condition. 10. The system of clause 9, wherein the database
searching facility further searches the database of conditions for
sensors that correspond to at least one condition and indicates the
sensors in the graphical user interface. 11. The system of clause
7, wherein the user selection of a component of the plurality of
components causes a data collection template for configuring the
data routing and collection system to automatically collect data
from sensors associated with the selected component. 12. A method
comprising: presenting in an expert graphical user interface a list
of reliability measures of an industrial machine; facilitating user
selection of one reliability measure from the list; presenting a
representation of a smart band data collection template associated
with the selected reliability measure; and in response to a user
indication of acceptance of the smart band data collection
template, configuring a data routing and collection system to
collect data from a plurality of sensors in an industrial
environment in response to a data value from one of the plurality
of sensors being detected outside of an acceptable range of data
values. 13. The method of clause 12, wherein the reliability
measures include one or more of industry average data,
manufacturer's specifications, manufacturer's material
specifications, and manufacturer's recommendations. 14. The method
of clause 13, wherein include the manufacturer's specifications
include at least one of cycle count, working time, maintenance
recommendations, maintenance schedules, operational limits,
material limits, and warranty terms. 15. The method of clause 12,
wherein the reliability measures correlate to failures selected
from the list consisting of stress, vibration, heat, wear,
ultrasonic signature, and operational deflection shape effect. 16.
The method of clause 12, further comprising correlating sensors in
the industrial environment to manufacturer's specifications. 17.
The method of clause 16, wherein correlating comprises matching a
duty cycle specification to a sensor that detects revolutions of a
moving part. 18. The method of clause 16, wherein correlating
comprises matching a temperature specification with a thermal
sensor disposed to sense an ambient temperature proximal to the
industrial machine. 19. The method of clause 16, further comprising
dynamically setting the acceptable range of data values based on a
result of the correlating. 20. The method of clause 16, further
comprising automatically determining the one of the plurality of
sensors for detecting the data value outside of the acceptable
range based on a result of the correlating.
[0832] Back calculation, such as for determining possible root
causes of failures and the like, may benefit from a graphical
approach that facilitates visualizing an industrial environment,
machine, or portion thereof marked with indications of information
sources that may provide data such as sensors and the like related
to the failure. A failed part, such as a bearing, may be associated
with other parts, such as shaft, motor, and the like. Sensors for
monitoring conditions of the bearing and the associated parts may
provide information that could indicate a potential source of
failure. Such information may also be useful to suggest indicators,
such as changes in sensor output, to monitor or avoid the failure
in the future. A system that facilitates a graphical approach for
back-calculation may interact with sensor data collection and
analysis systems to at least partially automate aspects related to
data collection and processing determined from a back-calculation
process.
[0833] In embodiments, a system for data collection in an
industrial environment may include a user interface in which
portions of an industrial machine associated with a condition of
interest, such as a failure condition, are presented on an
electronic display along with sensor data types contributing to the
condition of interest, data collection points (e.g., sensors)
associated with the machine portions that monitor the data types, a
set of data from the data collection points that was collected and
used to determine the condition of interest, and an annotation of
sensors that delivered exceptional data, such as data that is out
of an acceptable range, and the like, that may have been used to
determine the condition of interest. The user interface may access
a description of the machine that facilitates determining and
visualizing related components, such as bearing, shafts, brakes,
rotors, motor housings, and the like that contribute to a function,
such as rotating a turbine. The user interface may also access a
data set that relates sensors disposed in and about the machine
with the components. Information in the data set may include
descriptions of the sensors, their function, a condition that each
senses, typical or acceptable ranges of values output from the
sensors, and the like. The information in the data set may also
identify a plurality of potential pathways in a system for data
collection in an industrial environment for sensor data to be
delivered to a data collector. The user interface may also access a
data set that may include data collection templates used to
configure a data collection system for collecting data from the
sensors to meet specific purposes (e.g., to collect data from
groups of sensors into a sensor data set suitable for determining a
condition of the machine, such as a degree of slippage of the shaft
relative to the motor, and the like).
[0834] In embodiments, a method of back-calculation for determining
candidate sources of data collection for data that contributes to a
condition of an industrial machine may include following routes of
data collection determined from a configuration and operational
template of a data collection system for collecting data from
sensors deployed in the industrial machine that was in place when
the contributing data was collected. A configuration and
operational template may describe signal path switching,
multiplexing, collection timing, and the like for data from a group
of sensors. The group of sensors may be local to a component, such
as a bearing, or more regionally distributed, such as sensors that
capture information about the bearing and its related components.
In embodiments, a data collection template may be configured for
collecting and processing data to detect a particular condition of
the industrial machine. Therefore, templates may be correlated to
conditions so that performing back-calculation of a condition of
interest can be guided by the correlated template. Data collected
based on the template may be examined and compared to acceptable
ranges of data for various sensors. Data that is outside of an
acceptable range may indicate potential root causes of an
unacceptable condition. In embodiments, a suspect source of data
collection may be determined from the candidate sources of data
collection based on a comparison of data collected from the
candidate data sources with an acceptable range of data collected
from each candidate data source. Visualizing these back-calculation
based signal paths, candidate sensors, and suspect data sources
provides a user with valuable insights into possible root causes of
failures and the like.
[0835] In embodiments, a method for back-calculation may include
visualizing route(s) of data that contribute to a fault condition
detected in an industrial environment by applying back-calculation
to determine sources of the contributed data with the visualizing
appearing as highlighted data paths in a visual representation of
the data collection system in the industrial machine. In
embodiments, determining sources of data may be based on a data
collection and processing template for the fault condition. The
template may include a configuration of a data collection system
when data from the determined sources was collected with the
system.
[0836] When failures occur, or conditions of a portion of a machine
in an industrial environment reach a critical point prior to
failure, such as may be detected during preventive maintenance and
the like, back-calculation may be useful in determining information
to gather that might help avoid the failure and/or improve system
performance--for example, by avoiding substantive degradation in
component operation. Visualizing data collection sources,
components related to a condition, algorithms that may determine
the potential onset of the condition and the like may facilitate
preparation of data collection templates for configuring data
sensing, routing, and collection resources in a system for data
collection in an industrial environment. In embodiments,
configuring a data collection template for a system for collecting
data in an industrial environment may be based on back-calculations
applied to machine failures that identify candidate conditions to
monitor for avoiding the machine failures. The resulting template
may identify sensors to monitor, sensor data collection path
configuration, frequency, and amount of data to collect, acceptable
levels of sensor data, and the like. With access to information
about the machine, such as which parts closely relate to others and
sensors that collected data from parts in the machine, a data
collection system configuration template may be automatically
generated when a target component is identified.
[0837] In embodiments, a user interface may include a graphical
display of data sources as a logical arrangement of sensors that
may contribute data to a calculation of a condition of a machine in
an industrial environment. A logical arrangement may be based on
sensor type, data collection template, condition, algorithm for
determining a condition, and the like. In an example, a user may
wish to view all temperature sensors that may contribute to a
condition, such as a failure of a part in an industrial
environment. A user interface may communicate with a database of
machine related information, such as parts that relate to a
condition, sensors for those parts, and types of those sensors to
determine the subset of sensors that measure temperature. The user
interface may highlight those sensors. The user interface may
activate selectable graphical elements for those sensors that, when
selected by the user, may present data associated with those
sensors, such as sensor type, ranges of data collected, acceptable
ranges, actual data values collected for a given condition, and the
like, such as in a pop-up panel or the like. Similar functionality
of the user interface may apply to physical arrangements of
sensors, such as all sensors associated with a motor, boring
machine cutting head, wind turbine, and the like.
[0838] In embodiments, third-parties, such as component
manufacturers, remote maintenance organizations and the like may
benefit from access to back-calculation visualization Permitting
third parties to have access to back-calculation information, such
as sensors that contributed unacceptable data values to a
calculation of a condition, visualization of sensor positioning,
and the like may be an option that a user can exercise in a user
interface for a graphical approach to back-calculations as
described herein. A list of manufacturers of machines, sub-systems,
individual components, sensors, data collection systems, and the
like may be presented along with remote maintenance organizations,
and the like in a portion of a user interface. A user of the
interface may select one or more of these third-parties to grant
access to at least a portion of the available data and
visualizations. Selecting one or more of these third-parties may
also present statistical information about the party, such
occurrences and frequency of access to data to which the party is
granted access, request from the party for access, and the
like.
[0839] In embodiments, visualization of back-calculation analysis
may be combined with machine learning so that back-calculations and
their visualizations may be used to learn potential new diagnoses
for conditions, such as failure conditions, to learn new conditions
to monitor, and the like. A user may interact with the user
interface to provide the machine learning techniques feedback to
improve results, such as indicating a success or failure of an
attempt to prevent failures through specific data collection and
processing solutions (e.g., templates), and the like.
[0840] In embodiments, methods and systems of back-calculation of
data collected with a system for data collection in an industrial
environment may be applied to concrete pouring equipment in a
construction site application. Concrete pouring equipment may
comprise several active components including mixers that may
include water and aggregate supply systems, mixing control systems,
mixing motors, directional controllers, concrete sensors and the
like, concrete pumps, delivery systems, flow control as well as
on/off controls, and the like. Back-calculation of failure or other
conditions of active or passive components of a concrete pouring
equipment may benefit from visualization of the equipment, its
components, sensors, and other points where data is collected
(e.g., controllers and the like). Visualizing data/conditions
collected from sensors associated with concrete pumps and the like
when performing back-calculation of a flow rate failure condition
may inform the user of a conditions of the pump that may contribute
to the flow rate failure. Flow rate may decrease contemporaneously
with an increase in temperature of the pump. This may be visualized
by, for example, presenting the flow rate sensor data and the pump
temperature sensor data in the user interface. This correlation may
be noted by an expert system or by a user observing the
visualization and corrective action may be taken.
[0841] In embodiments, methods and systems of back-calculation of
data collected with a system for data collection in an industrial
environment may be applied to digging and extraction systems in a
mining application. Digging and extraction systems may comprise
several active sub-systems including cutting heads, pneumatic
drills, jack hammers, excavators, transport systems, and the like.
Back-calculation of failure or other conditions of active or
passive components of digging and extraction systems may benefit
from visualization of the equipment, its components, sensors, and
other points where data is collected (e.g., controllers and the
like). Visualizing data/conditions collected from sensors
associated with pneumatic drills and the like when performing
back-calculation of a pneumatic line failure condition may inform
the user of a conditions of the drill that may contribute to the
line failure. Line pressure may increase contemporaneously with a
change of a condition of the drill. This may be visualized by, for
example, presenting the line pressure sensor data and data from
sensors associated with the drill in the user interface. This
correlation may be noted by an expert system or by a user observing
the visualization and corrective action may be taken.
[0842] In embodiments, methods and systems of back-calculation of
data collected with a system for data collection in an industrial
environment may be applied to cooling towers in an oil and gas
production environment. Cooling towers may comprise several active
components including feedwater systems, pumps, valves,
temperature-controlled operation, storage systems, mixing systems,
and the like. Back-calculation of failure or other conditions of
active or passive components of cooling towers may benefit from
visualization of the equipment, its components, sensors and other
points where data is collected (e.g., controllers and the like).
Visualizing data/conditions collected from sensors associated with
the cooling towers and the like when performing back-calculation of
a circulation pump failure condition may inform the user of a
conditions of the cooling towers that may contribute to the pump
failure. Temperature of the feedwater may increase
contemporaneously with a decrease in output of the circulation
pump. This may be visualized by, for example, presenting the feed
water temperature sensor data and the pump output rate sensor data
in the user interface. This correlation may be noted by an expert
system or by a user observing the visualization and corrective
action may be taken.
[0843] In embodiments, methods and systems of back-calculation of
data collected with a system for data collection in an industrial
environment may be applied to circulation water systems in a power
generation application. Circulation water systems may comprise
several active components including, pumps, storage systems, water
coolers, and the like. Back-calculation of failure or other
conditions of active or passive components of circulation water
systems may benefit from visualization of the equipment, its
components, sensors and other points where data is collected (e.g.,
controllers and the like). Visualizing data/conditions collected
from sensors associated with water coolers and the like when
performing back-calculation of a circulation water temperature
failure condition may inform the user of a conditions of the cooler
that may contribute to the temperature condition failure.
Circulation temperature may increase contemporaneously with an
increase of core water cooler temperature. This may be visualized
by, for example, presenting the circulation water temperature
sensor data and the water cooler temperature sensor data in the
user interface. This correlation may be noted by an expert system
or by a user observing the visualization and corrective action may
be taken.
[0844] Referring to FIG. 76 a graphical approach 11300 for
back-calculation is depicted. Components of an industrial
environment may be depicted in a map of the environment 11302.
Components that may have a history of failure (with this
installation or others) may be highlighted. In response to a
selection of one of these components (such as by a user making the
selection), related components and sensors for the selected part
and related components may be highlighted, including signal routing
paths for the data from their relevant sensors to a data collector.
Additional highlighting may be added to sensors from which
unacceptable data has been collected, thereby indicating potential
root causes of a failure of the selected part. The relationships
among the parts may be based at least in part on machine
configuration metadata. The relationship between specific sensors
and the failure condition may be based at least in part on a data
collection template associated with the part and/or associated with
the failure condition.
[0845] Clause 1. In embodiments, a system comprising: a user
interface of a system adapted to collect data in an industrial
environment; the user interface comprising: a plurality of
graphical elements representing mechanical portions of an
industrial machine, wherein the plurality of graphical elements is
associated with a condition of interest generated by a processor
executing a data analysis algorithm; a plurality of graphical
elements representing data collectors in a system adapted for
collecting data in an industrial environment that collected data
used in the data analysis algorithm; and a plurality of graphical
elements representing sensors used to capture the data used in the
data analysis algorithm, wherein graphical elements for sensors
that provided data that was outside of an acceptable range of data
values are indicated through a visual highlight in the user
interface. 2. The system of clause 1, wherein the condition of
interest is selected from a list of conditions of interest
presented in the user interface. 3. The system of clause 1, wherein
the condition of interest is a mechanical failure of at least one
of the mechanical portions of the industrial machine. 4. The system
of clause 1, wherein the mechanical portions comprise at least one
of a bearing, shaft, rotor, housing, and linkage of the industrial
machine. 5. The system of clause 1, wherein the acceptable range of
data values is available for each sensor. 6. The system of clause
1, further comprising highlighting data collectors that collected
the data that was outside of the acceptable range of data values.
7. The system of clause 1, further comprising a data collection
system configuration template that facilitates configuring the data
collection system to collect the data for calculating the condition
of interest. 8. A method of determining candidate sources of a
condition of interest comprising: identifying a data collection
template for configuring data routing and collection resources in a
system adapted to collect data in an industrial environment,
wherein the template was used to collect data that contributed to a
calculation of the condition of interest; determining paths from
data collectors for the collected data to sensors that produced the
collected data by analyzing the data collection template; comparing
data collected by the sensors with acceptable ranges of data values
for data collected by the sensors; and highlighting, in an
electronic user interface that depicts the industrial environment
and at least one of the sensors, at least one sensor that produced
data that contributed to the calculation of the condition of
interest that is outside of the acceptable range of data for that
sensor. 9. The method of clause 8, wherein the condition of
interest is a failure condition. 10. The method of clause 8,
wherein the data collection template comprises configuration
information for at least one of an analog crosspoint switch, a
multiplexer, a hierarchical multiplexer, a sensor, a collector, and
a data storage facility of the system adapted to collect data in
the industrial environment. 11. The method of clause 8, wherein the
highlighting in the industrial environment comprises highlighting
he at least one sensor, and at least one route of data from the
sensor to a data collector of the system for data collection in the
industrial environment. 12. The method of clause 8, wherein
comparing data collected by the sensors with acceptable ranges of
data values comprises comparing data collected by each sensor with
an acceptable range of data values that is specific to each sensor.
13. The method of clause 8, wherein the calculation of the
condition of interest comprises calculating a trend of data from at
least one sensor. 14. The method of clause 8, wherein the
acceptable range of values comprises a trend of data values. 15. A
method of visualizing routes of data that contribute to a condition
of interest that is detected in an industrial environment, the
method comprising: applying back calculation to the condition of
interest to determine a data collection system configuration
template associated with the condition of interest; analyzing the
template to determine a configuration of the data collection system
for collecting data for detecting the condition of interest;
presenting, in an electronic user interface, a map of the data
collection configured by the template; and highlighting, in the
electronic user interface, routes in the data collection system
that reflect paths of data from at least one sensor to at least one
data collector for data that contributes to calculating the
condition of interest. 16. The method of clause 15 wherein the data
collection system configuration template comprises configuration
information for at least one resource deployed in the data
collection system selected from the list consisting of an analog
crosspoint switch, a multiplexer, a hierarchical multiplexer, a
data collector, and a sensor. 17. The method of clause 15, further
comprising generating a target diagnosis for the condition of
interest by applying machine learning to the back calculation. 18.
The method of clause 15, further comprising highlighting in the
electronic user interface, sensors that produce data used in
calculating the condition of interest that is outside of an
acceptable range of data values for the sensor. 19. The method of
clause 15, wherein the condition of interest is selected from a
list of conditions of interest presented in the user interface. 20.
The system of clause 15, wherein the condition of interest is a
mechanical failure of at least one mechanical portion of the
industrial environment. 21. The system of clause 15, wherein the
mechanical portions comprise at least one of a bearing, shaft,
rotor, housing, and linkage of the industrial environment.
[0846] In embodiments, a system for data collection in an
industrial environment may route data from a plurality of sensors
in the industrial environment to wearable haptic stimulators that
present the data from the sensors as human detectable stimuli
including at least one of tactile, vibration, heat, sound, and
force. In embodiments, the haptic stimulus represents an effect on
the machine resulting from the sensed data. In embodiments, a
bending effect may be presented as bending a finger of a haptic
glove. In embodiments, a vibrating effect may be presented as
vibrating a haptic arm band. In embodiments, a heating effect may
be presented as an increase in temperature of a haptic wrist band.
In embodiments, an electrical effect (e.g., over voltage, current,
and others) may be presented as a change in sound of a phatic audio
system.
[0847] In embodiments, an industrial machine operator haptic user
interface may be adapted to provide haptic stimuli to the operator
that is responsive to the operator's control of the machine,
wherein the stimuli indicate an impact on the machine as a result
of the operator's control and interaction with objects in the
environment as a result thereof. In embodiments, sensed conditions
of the machine that exceed an acceptable range may be presented to
the operator through the haptic user interface. In embodiments, the
sensed conditions of the machine that are within an acceptable
range may not be presented to the operator through the haptic user
interface. In embodiments, the sensed conditions of the machine
that are within an acceptable range may presented as natural
language representations of confirmation of the operator control.
In embodiments, at least a portion of the haptic user interface is
worn by the operator. In embodiments, a wearable haptic user
interface device may include force exerting devices along the outer
legs of a device operator's uniform. When a vehicle that the
operator is controlling approaches an obstacle along a lateral side
of the vehicle, an inflatable bellows may be inflated, exerting
pressure against the leg of the operator closest to the side of the
vehicle approaching the obstacle. The bellows may continue to be
inflated, thereby exerting additional pressure on the operator's
leg that is consistent with the proximity of the obstacle. The
pressure may be pulsed when contact with the obstacle is imminent
In another example, an arm band of an operator may vibrate in
coordination with vibration being experienced by a portion of the
vehicle that the operator is controlling. These are merely examples
and not intended to be limiting or restrictive of the ways in which
a wearable haptic feedback user device may be controlled to
indicate conditions that are sensed by a system for data collection
in an industrial environment.
[0848] In embodiments, a haptic user interface safety system worn
by a user in an industrial environment may be adapted to indicate
proximity to the user of equipment in the environment by
stimulating a portion of the user with at least one of pressure,
heat, impact, electrical stimuli and the like, the portion of the
user being stimulated may be closest to the equipment. In
embodiments, at least one of the type, strength, duration, and
frequency of the stimuli is indicative of a risk of injury to the
user.
[0849] In embodiments, a wearable haptic user interface device,
that may be worn by a user in an industrial environment, may
broadcast its location and related information upon detection of an
alert condition in the industrial environment. The alert condition
may be proximal to the user wearing the device, or not proximal but
related to the user wearing the device. A user may be an emergency
responder, so the detection of a situation requiring an emergency
responded, the user's haptic device may broadcast the user's
location to facilitate rapid access to the user or by the user to
the emergency location. In embodiments, an alert condition may be
determined from monitoring industrial machine sensors may be
presented to the user as haptic stimuli, with the severity of the
alert corresponding to a degree of stimuli. In embodiments, the
degree of stimuli may be based on the severity of the alert, the
corresponding stimuli may continue, be repeated, or escalate,
optionally including activating multiple stimuli concurrently, send
alerts to additional haptic users, and the like until an acceptable
response is detected, e.g., through the haptic UI. The wearable
haptic user device may be adapted to communicate with other haptic
user devices to facilitate detecting the acceptable response.
[0850] In embodiments, a wearable haptic user interface for use in
an industrial environment may include gloves, rings, wrist bands,
watches, arm bands, head gear, belts, necklaces, shirts (e.g.,
uniform shirt), foot wear, pants, ear protectors, safety glasses,
vests, overalls, coveralls, and any other article of clothing or
accessory that can be adapted to provide haptic stimuli.
[0851] In embodiments, wearable haptic device stimuli may be
correlated to a sensor in an industrial environment. Non-limiting
examples include a vibration of a wearable haptic device in
response to vibration detected in an industrial environment;
increasing or decreasing the temperature of a wearable haptic
device in response to a detected temperature in an industrial
environment; producing sound that changes in pitch responsively to
changes in a sensed electrical signal, and the like. In
embodiments, a severity of wearable haptic device stimuli may
correlate to an aspect of a sensed condition in the industrial
environment. Non-limiting examples include moderate or short-term
vibration for a low degree of sensed vibration; strong or long-term
vibration stimulation for an increase in sensed vibration;
aggressive, pulsed, and/or multi-mode stimulation for a high amount
of sensed vibration. Wearable haptic device stimuli may also
include lighting (e.g., flashing, color changes, and the like),
sound, odor, tactile output, motion of the haptic device (e.g.,
inflating/deflating a balloon, extension/retraction of an
articulated segment, and the like), force/impact, and the like.
[0852] In embodiments, a system for data collection in an
industrial environment may interface with wearable haptic feedback
user devices to relay data collected from fuel handling systems in
a power generation application to the user via haptic stimulation
Fuel handling for power generation may include solid fuels, such as
woodchips, stumps, forest residue, sticks, energy willow, peat,
pellets, bark, straw, agro biomass, coal, and solid recovery fuel.
Handling systems may include receiving stations that may also
sample the fuel, preparation stations that may crush or chip
wood-based fuel or shred waste-based fuel. Fuel handling systems
may include storage and conveying systems, feed and ash removal
systems and the like. Wearable haptic user interface devices may be
used with fuel handling systems by providing an operator feedback
on conditions in the handling environment that the user is
otherwise isolated from. Sensors may detect operational aspects of
a solid fuel feed screw system. Conditions like screw rotational
rate, weight of the fuel, type of fuel, and the like may be
converted into haptic stimuli to a user while allowing the user to
use his hands and provide his attention to operate the fuel feed
system.
[0853] In embodiments, a system for data collection in an
industrial environment may interface with wearable haptic feedback
user devices to relay data collected from suspension systems of a
truck and/or vehicle application to the user via haptic
stimulation. Haptic simulation may be correlated with conditions
being sensed by the vehicle suspension system. In embodiments, road
roughness may be detected and converted into vibration-like stimuli
of a wearable haptic arm band. In embodiments, suspension forces
(contraction and rebound) may be converted into stimuli that
present a scaled down version of the forces to the user through a
wearable haptic vest.
[0854] In embodiments, a system for data collection in an
industrial environment may interface with wearable haptic feedback
user devices to relay data collected from hydroponic systems in an
agriculture application to the user via haptic stimulation In
embodiments, sensors in hydroponic systems, such as temperature,
humidity, water level, plant size, carbon dioxide/oxygen levels,
and the like may be converted to wearable device haptic stimuli. As
an operator wearing haptic feedback clothing walks through a
hydroponic agriculture facility, sensors proximal to the operator
may signal to the haptic feedback clothing relevant information,
such as temperature or a measure of actual temperature versus
desired temperature that the haptic clothing may convert into
haptic stimuli. In an example, a wrist band may include a thermal
stimulator that can change temperature quickly to track temperature
data or a derivative thereof from sensors in the agriculture
environment. As a user walks through the facility, the haptic
feedback wristband may change temperature to indicate a degree to
which proximal temperatures are complying with expected
temperatures.
[0855] In embodiments, a system for data collection in an
industrial environment may interface with wearable haptic feedback
user devices to relay data collected from robotic positioning
systems in an automated production line application to the user via
haptic stimulation Haptic feedback may include receiving a
positioning system indicator of accuracy and converting it to an
audible signal when the accuracy is acceptable, and another type of
stimuli when the accuracy is not acceptable.
[0856] Referring to FIG. 77, a wearable haptic user interface
device for providing haptic stimuli to a user that is responsive to
data collected in an industrial environment by a system adapted to
collect data in the industrial environment is depicted. A system
for data collection 11402 in an industrial environment 11400 may
include a plurality of sensors. Data from those sensors may be
collected and analyzed by a computing system. A result of the
analysis may be communicated wirelessly to one or more wearable
haptic feedback stimulators 11404 worn by a user associated with
the industrial environment. The wearable haptic feedback
stimulators may interpret the result, convert it into a form of
stimuli based on a haptic stimuli-to-sensed condition mapping, and
produce the stimuli.
[0857] Clause 1. In embodiments, a system for data collection in an
industrial environment, comprising: a plurality of wearable haptic
stimulators that produce stimuli selected from the list of stimuli
consisting of tactile, vibration, heat, sound, force, odor, and
motion; a plurality of sensors deployed in the industrial
environment to sense conditions in the environment; a processor
logically disposed between the plurality of sensors and the
wearable haptic stimulators, the processor receiving data from the
sensors representative of the sensed condition, determining at
least one haptic stimulation that corresponds to the received data,
and sending at least one signal for instructing the wearable haptic
stimulators to produce the at least one stimulation 2. The system
of clause 1, wherein the haptic stimulation represents an effect on
a machine in the industrial environment resulting from the
condition. 3. The system of clause 2, wherein a bending effect is
presented as bending a haptic device. 4. The system of clause 2,
wherein a vibrating effect is presented as vibrating a haptic
device. 5. The system of clause 2, wherein a heating effect is
presented as an increase in temperature of a haptic device. 6. The
system of clause 2, wherein an electrical effect is presented as a
change in sound produced by a haptic device. 7. The system of
clause 2, wherein at least one of the plurality of wearable haptic
stimulators are selected from the list consisting of a glove, ring,
wrist band, wrist watch, arm band, head gear, belt, necklace,
shirt, foot wear, pants, overalls, coveralls, and safety goggles.
8. The system of clause 2, wherein the at least one signal
comprises an alert of a condition of interest in the industrial
environment. 9. The system of clause 8, wherein the at least one
stimulation produced in response to the alert signal is repeated by
at least one of the plurality of wearable haptic stimulators until
an acceptable response is detected. 10. An industrial machine
operator haptic user interface that is adapted to provide the
operator haptic stimuli responsive to the operator's control of the
machine based on at least one sensed condition of the machine that
indicates an impact on the machine as a result of the operator's
control and interaction with objects in the environment as a result
thereof. 11. The user interface of clause 10, wherein a sensed
condition of the machine that exceeds an acceptable range of data
values for the condition is presented to the operator through the
haptic user interface. 12. The user interface of clause 10, wherein
a sensed condition of the machine that is within an acceptable
range of data values for the condition is presented as natural
language representations of confirmation of the operator control
via an audio haptic stimulator. 13. The user interface of clause
10, wherein at least a portion of the haptic user interface is worn
by the operator. 14. The system of clause 10, wherein a vibrating
sensed condition is presented as vibrating stimulation by the
haptic user interface. 15. The system of clause 10, wherein a
temperature-based sensed condition is presented as heat stimulation
by the haptic user interface. 16. A haptic user interface safety
system worn by a user in an industrial environment, wherein the
interface is adapted to indicate proximity to the user of equipment
in the environment by haptic stimulation via a portion of the
haptic user interface that is closest to the equipment, wherein at
least one of the type, strength, duration, and frequency of the
stimulation is indicative of a risk of injury to the user. 17. The
haptic user interface of clause 16, wherein the haptic stimulation
is selected from a list consisting of pressure, heat, impact, and
electrical stimulation. 18. The haptic user interface of clause 16
wherein the haptic user interface further comprises a wireless
transmitter that broadcasts a location of the user. 19. The haptic
user interface of clause 18, wherein the wireless transmitter
broadcasts a location of the user in response to indicating
proximity of the user to the equipment. 20. The haptic user
interface of clause 16, wherein the proximity to the user of
equipment in the environment is based on sensor data provided to
the haptic user interface from a system adapted to collect data in
an industrial environment, wherein the system is adapted based on a
data collection template associated with a user safety condition in
the industrial environment.
[0858] In embodiments, a system for data collection in an
industrial environment may facilitate presenting a graphical
element indicative of industrial machine sensed data on an
augmented reality (AR) display. The graphical element may be
adapted to represent a position of the sensed data on a scale of
acceptable values of the sensed data. The graphical element may be
positioned proximal to a sensor detected in the field of view being
augmented that captured the sensed data in the AR display. The
graphical element may be a color and the scale may be a color scale
ranging from cool colors (e.g., greens, blues) to hot colors (e.g.,
yellow, red) and the like. Cool colors may represent data values
closer to the midpoint of the acceptable range and the hot colors
representing data values close to or outside of a maximum or
minimum value of the range.
[0859] In embodiments, a system for data collection in an
industrial environment may present, in an AR display, data being
collected from a plurality of sensors in the industrial environment
as one of a plurality graphical effects (e.g., colors in a range of
colors) that correlate the data being collected from each sensor to
a scale of values within an acceptable range compared to values
outside of the acceptable range. In embodiments, the plurality of
graphical effects may overlay a view of the industrial environment
and placement of the plurality of graphical effects may correspond
to locations in the view of the environment at which a sensor is
located that is producing the corresponding sensor data. In
embodiments, a first set of graphical effects (e.g., hot colors)
represent components for which multiple sensors indicate values
outside acceptable ranges.
[0860] In embodiments, a system for data collection in an
industrial environment may facilitate presenting, in an AR display
information being collected by sensors in the industrial
environment as a heat map overlaying a visualization of the
environment so that regions of the environment with sensor data
suggestive of a greater potential of failure are overlaid with a
graphic effect that is different than regions of the environment
with sensor data suggestive of a lesser potential of failure. In
embodiments, the heat map is based on data currently being sensed.
In embodiments, the heat map is based on data from prior failures.
In embodiments, the heat map is based on changes in data from an
earlier period, such as data that suggest an increased likelihood
of machine failure. In embodiments, the heat map is based on a
preventive maintenance plan and a record of preventive maintenance
in the industrial environment.
[0861] In embodiments, a system for data collection in an
industrial environment may facilitate presenting information being
collected by sensors in the industrial environment as a heat map
overlaying a view of the environment, such as a live view as may be
presented in an AR display. Such a system may include presenting an
overlay that facilitates a call to action, wherein the overlay is
associated with a region of the heat map. The overlay may comprise
a visual effect of a part or subsystem of the environment on which
the action is to be performed. In embodiments, the action to be
performed is maintenance related and may be part-specific.
[0862] In embodiments, a system for data collection in an
industrial environment may facilitate updating, in an AR view of a
portion of the environment, a heat map of aspects of the industrial
environment based on a change to operating instructions for at
least one aspect of a machine in the industrial environment. The
heat map may represent compliance with operational limits for
portions of machines in the industrial environment. In embodiments,
the heat map may represent a likelihood of component failure as a
result of the change to operation instructions.
[0863] In embodiments, a system for data collection in an
industrial environment may facilitate presenting, as a heat map in
an AR view of a portion of the environment, a degree or measure of
coverage of sensors in the industrial environment for a data
collection template that identifies select sensors in the
industrial environment for a data collection activity.
[0864] In embodiments, a system for data collection in an
industrial environment may facilitate displaying a heat map
overlaying a view, such as a live view, of an industrial
environment of failure-related data for various portions of the
environment. The failure-related data may comprise a difference
between an actual failure rate of the various portions and another
failure rate. Another failure rate may be a rate of failure of
comparable portions elsewhere in the environment, and/or average
failure rate of comparable portions across a plurality of
environments, such as an industry average, manufacturer failure
rate estimate, and the like.
[0865] In embodiments, a system for data collection in an
industrial environment may facilitate displaying a heat map related
to data collected from robotic arms and hands for production line
robotic handling in an augmented reality view of a portion of the
environment. A heat map related to data collected from robotic arms
and hands may represent data from sensors disposed in--for example,
the fingers of a robotic hand Sensor may collect data, such as
applied pressure when pinching an object, resistance (e.g.,
responsive to a robotic touch) of an object, multi-axis forces
presented to the finger as it performs an operation, such as
holding a tool and the like, temperature of the object, total
movement of the finger from initial point of contact until a
resistance threshold is met, and other hand position/use
conditions. Heat maps of this data may be presented in an augmented
reality view of a robotic production environment so that a user may
make a visual assessment of, for example, how the relative
positioning of the robotic fingers impacts the object being
handled
[0866] In embodiments, a system for data collection in an
industrial environment may facilitate displaying a heat map related
to data collected from linear bearings for production line robotic
handling in an augmented reality view of a portion of the
environment. Linear bearings, as with most bearings, may not be
visible while in use. However, assessing their operation may
benefit from representing data from sensors that capture
information about the bearings while in use in an augmented reality
display. In embodiments, sensors may be placed to detect forces
being placed on portions of the bearings by the rotating member or
elements that the bearings support. These forces may be presented
as heat maps that correspond to relative forces, on a visualization
of the bearings in an augmented reality view of a robot handling
machine that uses linear bearings.
[0867] In embodiments, a system for data collection in an
industrial environment may facilitate displaying a heat map related
to data collected from boring machinery for mining in an augmented
reality view of a portion of the environment. Boring machinery, and
in particular multi-tip circular boring heads may experience a
range of rock formations at the same time. Sensors may be placed
proximal to each boring tip that may detect forces experienced by
the tips. The data may be collected by a system adapted to collect
data in an industrial environment and provided to an augmented
reality system that may display the data as heat maps or the like
in a view of the boring machine.
[0868] Referring to FIG. 78, an augmented reality display of heat
maps based on data collected in an industrial environment by a
system adapted to collect data in the environment is depicted. An
augmented reality view of an industrial environment 11500 may
include heat maps 11502 that depict data received from or derived
from data received from sensors 11504 in the industrial
environment. Sensor data may be captured and processed by a system
adapted for data collection and analysis in an industrial
environment. The data may be converted into a form that is suitable
for use in an augmented reality system for displaying heat maps.
The heat maps 11502 may be aligned in the augmented reality view
with a sensor from which the underlying data was sourced.
[0869] Clause 1. In embodiments, an augmented reality (AR) system
in which industrial machine sensed data is presented in a view of
the industrial machine as heat maps of data collected from sensors
in the view, wherein the heat maps are positioned proximal to a
sensor capturing the sensed data that is visible in the AR display.
2. The system of clause 1, wherein the heat maps are based on a
comparison of real time data collected from sensors with an
acceptable range of values for the data. 3. The system of clause 1,
wherein the heat maps are based on trends of sensed data. 4. The
system of clause 1, wherein the heat maps represent a measure of
coverage of sensors in the industrial environment in response to a
condition of interest that is calculated from data collected by
sensors in the industrial environment. 5. The system of clause 1,
wherein the heat maps of data collected from sensors in the view is
based on data collected by a system adapted to collect data in the
industrial environment by routing data from a plurality of sensors
to a plurality of data collectors via at least one of an analog
crosspoint switch, a multiplexer, and a hierarchical multiplexer.
6. The system of clause 1, wherein the heat maps present different
collected data values as different colors. 7. The system of clause
1, wherein data collected from a plurality of sensors is combined
to produce a heat map. 8. A system for data collection in an
industrial environment, comprising: an augmented reality display
that presents data being collected from a plurality of sensors in
the industrial environment as one of a plurality of colors, wherein
the colors correlate the data being collected from each sensor to a
color scale with cool colors mapping to values of the data within
an acceptable range and hot colors mapping to values of the data
outside of the acceptable range, wherein the plurality of colors
overlay a view of the industrial environment and placement of the
plurality of colors corresponds to locations in the view of the
environment at which a sensor is located that is producing the
corresponding sensor data. 9. The system of clause 8, wherein hot
colors represent components for which multiple sensors indicate
values outside typical ranges. 10. The system of clause 8, wherein
the plurality of colors is based on a comparison of real time data
collected from sensors with an acceptable range of values for the
data. 11. The system of clause 8, wherein the plurality of colors
is based on trends of sensed data. 12. The system of clause 8,
wherein the plurality of colors represents a measure of coverage of
sensors in the industrial environment in response to a condition of
interest that is calculated from data collected by sensors in the
industrial environment. 13. A method comprising, presenting
information being collected by sensors in an industrial environment
as a heat map overlaying a view of the environment so that regions
of the environment with sensor data suggestive of a greater
potential of failure are overlaid with a heat map that is different
than regions of the environment with sensor data suggestive of a
lesser potential of failure. 14. The method of clause 13, wherein
the heat map is based on data currently being sensed. 15. The
method of clause 13, wherein the heat map is based on data from
prior failure data. 16. The method of clause 13, wherein the heat
map is based on changes in data from an earlier period that suggest
an increased likelihood of machine failure. 17. The method of
clause 13, wherein the heat map is based on a preventive
maintenance plan and a record of preventive maintenance in the
industrial environment. 18. The method of clause 13, wherein the
heat map represents an actual failure rate versus a reference
failure rate. 19. The method of clause 18, wherein the reference
failure rate is an industry average failure rate. 20. The method of
clause 18, wherein the reference failure rate is a manufacturer's
failure rate estimate.
[0870] In embodiments, a system for data collection and
visualization thereof in an industrial environment may include an
augmented reality and/or virtual reality (AR/VR) display in which
data values output by sensors disposed in a field of view in the
AR/VR display are displayed with visual attributes that indicate a
degree of compliance of the data to an acceptable range or values
for the sensed data. In embodiments, the visual attributes may
provide near real-time portrayal of trends of the sensed data
and/or of derivatives thereof. In embodiments, the visual
attributes may be the actual data being captured, or the derived
data, such as a trend of the data and the like.
[0871] In embodiments, a system for data collection and
visualization thereof in an industrial environment may include an
AR/VR display in which trends of data values output by sensors
disposed in a field of view in the AR/VR are displayed with visual
attributes that indicate a degree of severity of the trend. In
embodiments, other data or analysis that could be displayed may
include: data from sensors that exceed an acceptable range, data
from sensors that are part of a smart band selected by the user,
data from sensors that are monitored for triggering a smart band
collection action, data from sensors that sense an aspect of the
environment that meets preventive maintenance criteria, such as a
PM action is upcoming soon, a PM action was recently performed or
is overdue for PM. Other data for such AR/VR visualization may
include data from sensors for which an acceptable range has
recently been changed, expanded, narrowed and the like. Other data
for such AR/VR visualization that may be particularly useful for an
operator of an industrial machine (digging, drilling, and the like)
may include analysis of data from sensors, such as for example
impact on an operating element (torque, force, strain, and the
like).
[0872] In embodiments, a system for data collection and
visualization thereof in an industrial environment that may include
presentation of visual attributes that represent collected data in
an AR/VR environment may do so for pumps in a mining application.
Mining application pumps may provide water and remove liquefied
waste from a mining site. Pump performance may be monitored by
sensors detecting pump motors, regulators, flow meters, and the
like. Pump performance monitoring data may be collected and
presented as a set of visual attributes in an augmented reality
display. In an example, pump motor power consumption, efficiency,
and the like may be displayed proximal to a pump viewed through an
augmented reality display.
[0873] In embodiments, a system for data collection and
visualization thereof in an industrial environment that may include
presentation of visual attributes that represent collected data in
an AR/VR environment may do so for energy storage in a power
generation application. Power generation energy storage may be
monitored with sensors that capture data related to storage and use
of stored energy. Information such as utilization of individual
energy storage cells, energy storage rate (e.g., battery charging
and the like), stored energy consumption rate (e.g., KWH being
supplied by an energy storage system), storage cell status, and the
like may be captured and converted into augmented reality viewable
attributes that may be presented in an augmented reality view of an
energy storage system.
[0874] In embodiments, a system for data collection and
visualization thereof in an industrial environment that may include
presentation of visual attributes that represent collected data in
an AR/VR environment may do so for feed water systems in a power
generation application. Sensors may be disposed in an industrial
environment, such as power generation for collecting data about
feed water systems. Data from those sensors may be captured and
processed by the system for data collection. Results of this
processing may include trends of the data, such as feed water
cooling rates, flow rates, pressure and the like. These trends may
be presented on an augmented reality view of a feed water system by
applying a map of sensors with physical elements visible in the
view and then retrieving data from the mapped sensors. The
retrieved data (and derivatives thereof) may be presented in the
augmented reality view of the feed water system.
[0875] Referring to FIG. 79, an augmented reality display 11600
comprising real time data 11602 overlaying a view of an industrial
environment is depicted. Sensors 11604 in the environment may be
recognized by the augmented reality system, such as by first
detecting an industrial machine, system, or part thereof with which
the sensors are associated. Data from the sensors 11604 may be
retrieved from a data repository, processed into trends, and
presented in the augmented reality view 11600 proximal to the
sensors from which the data originates
[0876] Clause 1 In embodiments, a system for data collection and
visualization thereof in an industrial environment in which data
values output by sensors disposed in a field of view in an
electronic display are displayed in the electronic display with
visual attributes that indicate a degree of compliance of the data
to an acceptable range or values for the sensed data. 2. The system
of clause 1, wherein the view in the electronic display is a view
in an augmented reality display of the industrial environment. 3.
The system of clause 1, wherein the visual attributes are
indicative of a trend of the sensed data over time relative to the
acceptable range. 4. The system of clause 1, wherein the data
values are disposed in the electronic display proximal to the
sensors from which the data values are output. 5. The system of
clause 1, wherein the visual attributes further comprise an
indication of a smart band set of sensors associated with the
sensor from which the data values are output. 6. A system for data
collection and visualization thereof in an industrial environment
in which data values output by select sensors disposed in an
augmented reality view of the industrial environment are displayed
with visual attributes that indicate a degree of compliance of the
data to an acceptable range or values for the sensed data. 7. The
system of clause 6, wherein the sensors are selected based on a
data collection template that facilitates configuring sensor data
routing resources in the system. 8. The system of clause 7, wherein
the select sensors are indicated in the template as part of a group
of smart band sensors. 9. The system of clause 7, wherein the
select sensors are sensors that are monitored for triggering a
smart band data collection action. 10. The system of clause 6,
wherein the select sensors are sensors that sense an aspect of the
environment associated with preventive maintenance criteria. 11.
The system of clause 6, wherein the visual attributes further
indicate if the acceptable range has been expanded or narrowed
within the past 72 hours. 12. A system for data collection and
visualization thereof in an industrial environment in which trends
of data values output by select sensors disposed in a field of view
of the industrial environment depicted in an augmented reality
display are displayed with visual attributes that indicate a degree
of severity of the trend. 13. The system of clause 12, wherein
sensors are selected when data from the sensors exceed an
acceptable range of values. 14. The system of clause 14, wherein
sensors are selected based on the sensors being part of a smart
band group of sensors. 15. The system of clause 12, wherein the
visual attributes further indicate a compliance of the trend with
an acceptable range of data values. 16. The system of clause 12,
wherein the system for data collection is adapted to route data
from the select sensors to a controller of the augmented reality
display based on a data collection template that facilitates
configuring routing resources of the system for data collection.
17. The system of clause 12, wherein the sensors are selected in
response to the sensor data being configured in a smart band data
collection template as an indication for triggering a smart band
data collection action. 18. The system of clause 12, wherein the
sensors are selected in response to preventive maintenance
criteria. 19. The system of clause 18, wherein the preventive
maintenance criteria are selected from the list consisting of a
preventive maintenance action is scheduled, a preventive
maintenance action has been completed in the last 72 hours, a
preventive maintenance action is overdue.
[0877] In an aspect, and as illustrated in FIG. 80, a data
collection system 12004 can be arranged to collect data in an
industrial environment 12000, e.g., from one or more targets 12002.
In the illustrated embodiments, the data collection system 12004
includes a group or "swarm" 12006 of data collectors 12008, a
network 12010, a computing system 12012, and a database or data
pool 12014. Each of the data collectors 12008 can include one or
more input sensors and be communicatively coupled to any and all of
the other components of the data collection system 12004, as is
partially illustrated by the connecting arrows between
components.
[0878] The targets 12002 can be any form of machinery or component
thereof in an industrial environment 12000. Examples of such
industrial environments 12000 include but are not limited to
factories, pipelines, construction sites, ocean oil rigs, ships,
airplanes or other aircraft, mining environments, drilling
environments, refineries, distribution environments, manufacturing
environments, energy source extraction environments, offshore
exploration sites, underwater exploration sites, assembly lines,
warehouses, power generation environments, and hazardous waste
environments, each of which may include one or more targets 12002.
Targets 12002 can take any form of item or location at which a
sensor can obtain data. Examples of such targets 12002 include but
are not limited to machines, pipelines, equipment, installations,
tools, vehicles, turbines, speakers, lasers, automatons, computer
equipment, industrial equipment, and switches.
[0879] The self-organization functionality of the data collection
system 12004 can be performed at or by any of the components of the
data collection system 12004. In embodiments, a data collector
12008 or the swarm 12006 of data collectors 12008 can self-organize
without assistance from other components and based on, e.g., the
data sensed by its associated sensors and other knowledge. In
embodiments, the network 12010 can self-organize without assistance
from other components and based on, e.g., the data sensed by the
data collectors 12008 or other knowledge. Similarly, the computing
system 12012 and/or the data pool 12014 without assistance from
other components and based on, e.g., the data sensed by the data
collectors 12008 or other knowledge. It should be appreciated that
any combination or hybrid-type self-organization system can also be
implemented.
[0880] For example only, the data collection system 12004 can
perform or enable various methods or systems for data collection
having self-organization functionality in an industrial environment
12000. These methods and systems can include analyzing a plurality
of sensor inputs, e.g., received from or sensed by sensors at the
data collector(s) 12008. The methods and systems can also include
sampling the received data and self-organizing at least one of: (i)
a storage operation of the data; (ii) a collection operation of
sensors that provide the plurality of sensor inputs, and (iii) a
selection operation of the plurality of sensor inputs.
[0881] In aspects, the storage operation can include storing the
data in a local database, e.g., of a data collector 12008, a
computing system 12012, and/or a data pool 12014. The data can also
be summarized over a given time period to reduce a size of the
sensed data. The summarized data can be sent to one or more data
acquisition boxes, to one or more data centers, and/or to other
components of the system or other, separate systems Summarizing the
data over a given time period to reduce the size of the data, in
some aspects, can include determining a speed at which data can be
sent via a network (e.g., network 12010), wherein the size of the
summarized data corresponds to the speed at which data can be sent
continuously in real time via the network. In such aspects, or
others, the summarized data can be continuously sent, e.g., to an
external device via the network.
[0882] In various implementations, the methods and systems can
include committing the summarized data to a local ledger,
identifying one or more other accessible signal acquisition
instruments on an accessible network, and/or synchronizing the
summarized data at the local ledger with at least one of the other
accessible signal acquisition instruments (e.g., data collectors
12008). In embodiments, receiving a remote stream of sensor data
from one or more other accessible signal acquisition instruments
via a network can be included. An advertisement message to a
potential client indicating availability of at least one of the
locally stored data, the summarized data, and the remote stream of
sensor data can also or alternatively be sent.
[0883] The methods and systems can include identifying one or more
other accessible signal acquisition instruments (e.g., data
collectors 12008) on an accessible network (e.g., 12010),
nominating at least one of the one or more other accessible signal
acquisition instruments as a logical communication hub, and
providing the logical communication hub with a list of available
data and their associated sources. The list of available data and
their associated sources can be provided to the logical
communication hub utilizing a hybrid peer-to-peer communications
protocol.
[0884] In some aspects, the storage operation can include storing
the data in a local database and automatically organizing at least
one parameter of the data pool utilizing machine learning. The
organizing can be based at least in part on receiving information
regarding at least one of an accuracy of classification and an
accuracy of prediction of an external machine learning system that
uses data from the data pool (e.g., data pool 12014).
[0885] The present disclosure describes a method for data
collection in an industrial environment having self-organization
functionality, the method according to one disclosed non-limiting
embodiment of the present disclosure can include analyzing a
plurality of sensor inputs, sampling data received from the sensor
inputs and self-organizing at least one of: (i) a storage operation
of the data; (ii) a collection operation of sensors that provide
the plurality of sensor inputs, and (iii) a selection operation of
the plurality of sensor inputs.
[0886] The present disclosure describes a system for data
collection in an industrial environment having self-organization
functionality, the system according to one disclosed non-limiting
embodiment of the present disclosure can include a data collector
for handling a plurality of sensor inputs from sensors in the
industrial environment and for generating data associated with the
plurality of sensor inputs, and a self-organizing system for
self-organizing at least one of (i) a storage operation of the
data, (ii) a data collection operation of sensors that provide the
plurality of sensor inputs, and (iii) a selection operation of the
plurality of sensor inputs.
[0887] The present disclosure describes a method for data
collection in an industrial environment having self-organization
functionality, the method according to one disclosed non-limiting
embodiment of the present disclosure can include analyzing a
plurality of sensor inputs, [0888] sampling data received from the
sensor inputs; and self-organizing at least one of: (i) a storage
operation of the data; (ii) a collection operation of sensors that
provide the plurality of sensor inputs, and (iii) a selection
operation of the plurality of sensor inputs, wherein the storage
operation includes storing the data in a local database, and
summarizing the data over a given time period to reduce a size of
the data.
[0889] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the method
further includes sending the summarized data to one or more data
acquisition boxes.
[0890] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the method
further includes sending the summarized data to one or more data
centers.
[0891] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein summarizing
the data over a given time period to reduce the size of the data
includes determining a speed at which data can be sent via a
network, wherein the size of the summarized data corresponds to the
speed at which data can be sent continuously in real time via the
network.
[0892] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the method
further includes continuously sending the summarized data to an
external device via the network.
[0893] The present disclosure describes a method for data
collection in an industrial environment having self-organization
functionality, the method according to one disclosed non-limiting
embodiment of the present disclosure can include analyzing a
plurality of sensor inputs, sampling data received from the sensor
inputs and self-organizing at least one of: (i) a storage operation
of the data; (ii) a collection operation of sensors that provide
the plurality of sensor inputs, and (iii) a selection operation of
the plurality of sensor inputs, wherein the storage operation
includes storing the data in a local database, summarizing the data
over a given time period to reduce a size of the data, committing
the summarized data to a local ledger, identifying one or more
other accessible signal acquisition instruments on an accessible
network, and synchronizing the summarized data at the local ledger
with at least one of the other accessible signal acquisition
instruments. A further embodiment of any of the foregoing
embodiments of the present disclosure may include situations
wherein the method further includes receiving a remote stream of
sensor data from one or more other accessible signal acquisition
instruments via a network.
[0894] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the method
further includes sending an advertisement message to a potential
client indicating availability of at least one of the locally
stored data, the summarized data, and the remote stream of sensor
data.
[0895] The present disclosure describes a method for data
collection in an industrial environment having self-organization
functionality, the method according to one disclosed non-limiting
embodiment of the present disclosure can include analyzing a
plurality of sensor inputs; [0896] sampling data received from the
sensor inputs, self-organizing at least one of: (i) a storage
operation of the data (ii) a collection operation of sensors that
provide the plurality of sensor inputs, and (iii) a selection
operation of the plurality of sensor inputs, wherein the storage
operation includes storing the data in a local database, and
summarizing the data over a given time period to reduce a size of
the data, identifying one or more other accessible signal
acquisition instruments on an accessible network, nominating at
least one of the one or more other accessible signal acquisition
instruments as a logical communication hub, and providing the
logical communication hub with a list of available data and their
associated sources.
[0897] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the list of
available data and their associated sources is provided to the
logical communication hub utilizing a hybrid peer-to-peer
communications protocol. [0898] The present disclosure describes a
method for data collection in an industrial environment having
self-organization functionality, the method according to one
disclosed non-limiting embodiment of the present disclosure can
include analyzing a plurality of sensor inputs, sampling data
received from the sensor inputs, and self-organizing at least one
of (i) a storage operation of the data, (ii) a collection operation
of sensors that provide the plurality of sensor inputs, and (iii) a
selection operation of the plurality of sensor inputs, wherein the
storage operation includes storing the data in a local database,
summarizing the data over a given time period to reduce a size of
the data, storing the data in a local database, and automatically
organizing at least one parameter of the database utilizing machine
learning, wherein the organizing is based at least in part on
receiving information regarding at least one of an accuracy of
classification and an accuracy of prediction of an external machine
learning system that uses data from the database.
[0899] In aspects, the collection operation of sensors that provide
the plurality of sensor inputs can include receiving instructions
directing a mobile data collector unit (e.g., data collector 12008)
to operate sensors at a target (e.g., 12002), wherein at least one
of the plurality of sensors is arranged in the mobile data
collector unit. A communication can be transmitted to one or more
other mobile data collector units (12008) regarding the
instructions. The swarm 12006 or portion thereof can self-organize
a distribution of the mobile data collector unit and the one or
more other mobile data collector units (e.g., data collectors
12008) at the target 12002.
[0900] In aspects, self-organizing the distribution of the mobile
data collector units at the target 12002 comprises utilizing a
machine learning algorithm to determine a respective target
location for each of the mobile data collector units. The machine
learning algorithm can utilize one or more of a plurality of
features to determine the respective target locations. Examples of
the features can include: battery life of the mobile data collector
units (data collectors 12008), a type of the target 12002 being
sensed, a type of signal being sensed, a size of the target 12002,
a number of mobile data collector units (data collectors 12008)
needed to cover the target 12002, a number of data points needed
for the target 12002, a success in prior accomplishment of signal
capture, information received from a headquarters or other
components from which the instructions are received, and historical
information regarding the sensors operated at the target 12002.
[0901] In implementations, self-organizing the distribution of the
mobile data collector unit and the one or more other mobile data
collector units at the target location can include proposing a
target location for the mobile data collector unit(s), transmitting
the target location to at least one other mobile data collector
units, receiving confirmation that there is no contention for the
target location, directing one of the mobile data collector units
to the target location, and collecting sensor data at the target
location from the directed mobile data collector unit.
[0902] Self-organizing the distribution of the mobile data
collector unit and the one or more other mobile data collector
units at the target location can also include, in certain
embodiments, proposing a target location for the mobile data
collector unit, transmitting the target location to at least one of
the one or more other mobile data collector units, receiving a
proposal for a new target location, directing the mobile data
collector unit to the new target location, and collecting sensor
data at the new target location from the mobile data collector
unit.
[0903] In additional or alternative aspects, self-organizing the
distribution of the mobile data collector unit and the one or more
other mobile data collector units at the target location can
comprise proposing a target location for the mobile data collector
unit, determining that at least one of the one or more other mobile
data collector units is at or moving to the target location,
determining a new target location based on the at least one of the
one or more other mobile data collector units being at or moving to
the target location, directing the mobile data collector unit to
the new target location, and collecting sensor data at the new
target location from the mobile data collector unit.
[0904] Self-organizing the distribution of the mobile data
collector unit and the one or more other mobile data collector
units at the target location can further comprise determining a
type of the sensors to operate at the target 12002, receiving
confirmation that there is no contention for the type of sensors,
directing the mobile data collector unit to operate the type of
sensors at the target 12002, and collecting sensor data from the
type of sensors at the target 12002 from the mobile data collector
unit.
[0905] In aspects, self-organizing the distribution of the mobile
data collector unit and the one or more other mobile data collector
units at the target location can include determining a type of the
sensors to operate at the target, transmitting the type of the
sensors to at least one of the one or more other mobile data
collector units, receiving a proposal for a new type of the
sensors, directing the mobile data collector unit to operate the
new type of sensors at the target, and collecting sensor data from
the new type of sensors at the target from the mobile data
collector unit.
[0906] Self-organizing the distribution of the mobile data
collector unit and the one or more other mobile data collector
units at the target location can include determining a type of the
sensors to operate at the target, determining that at least one of
the one or more other mobile data collector units is operating or
can operate the type of the sensors at the target, determining a
new type of the sensors based on the at least one of the one or
more other mobile data collector units operating or being capable
of operating the type of the sensors at the target, directing the
mobile data collector unit to operate the new type of sensors at
the target, and collecting sensor data from the new type of sensors
at the target from the mobile data collector unit.
[0907] Self-organizing the distribution of the mobile data
collector unit and the one or more other mobile data collector
units at the target location, in some implementations, can comprise
utilizing a swarm optimization algorithm to allocate areas of
sensor responsibility amongst the mobile data collector unit and
the one or more other mobile data collector units. Examples of the
swarm optimization algorithm include but are not limited to Genetic
Algorithms (GA), Ant Colony Optimization (ACO), Particle Swarm
Optimization (PSO), Differential Evolution (DE), Artificial Bee
Colony (ABC), Glowworm Swarm Optimization (GSO), and Cuckoo Search
Algorithm (CSA), Genetic Programming (GP), Evolution Strategy (ES),
Evolutionary Programming (EP), Firefly Algorithm (FA), Bat
Algorithm (BA) and Grey Wolf Optimizer (GWO), or combinations
thereof.
[0908] The present disclosure describes a method for data
collection in an industrial environment having self-organization
functionality, the method according to one disclosed non-limiting
embodiment of the present disclosure can include analyzing a
plurality of sensor inputs, sampling data received from the sensor
inputs, and self-organizing at least one of (i) a storage operation
of the data, (ii) a collection operation of sensors that provide
the plurality of sensor inputs, and (iii) a selection operation of
the plurality of sensor inputs.
[0909] The present disclosure describes a system for data
collection in an industrial environment having automated
self-organization, the system according to one disclosed
non-limiting embodiment of the present disclosure can include a
data collector for handling a plurality of sensor inputs from
sensors in the industrial environment and for generating data
associated with the plurality of sensor inputs, and a
self-organizing system for self-organizing at least one of (i) a
storage operation of the data, (ii) a data collection operation of
sensors that provide the plurality of sensor inputs, and (iii) a
selection operation of the plurality of sensor inputs.
[0910] The present disclosure describes a method for data
collection in an industrial environment having self-organization
functionality, the method according to one disclosed non-limiting
embodiment of the present disclosure can include analyzing a
plurality of sensor inputs; [0911] sampling data received from the
sensor inputs and self-organizing at least one of (i) a storage
operation of the data, (ii) a collection operation of sensors that
provide the plurality of sensor inputs, and (iii) a selection
operation of the plurality of sensor inputs, wherein the collection
operation of sensors that provide the plurality of sensor inputs
includes receiving instructions directing a mobile data collector
unit to operate sensors at a target, wherein at least one of the
plurality of sensors is arranged in the mobile data collector unit,
transmitting a communication to one or more other mobile data
collector units regarding the instructions, and self-organizing a
distribution of the mobile data collector unit and the one or more
other mobile data collector units at the target.
[0912] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein
self-organizing the distribution of the mobile data collector unit
and the one or more other mobile data collector units at the target
includes utilizing a machine learning algorithm to determine a
respective target location for each of the mobile data collector
units.
[0913] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the machine
learning algorithm utilizes one or more of a plurality of features
to determine the respective target locations, the plurality of
features including: battery life of the mobile data collector
units, a type of the target being sensed, a type of signal being
sensed, a size of the target, a number of mobile data collector
units needed to cover the target, a number of data points needed
for the target, a success in prior accomplishment of signal
capture, information received from a headquarters from which the
instructions are received, and historical information regarding the
sensors operated at the target.
[0914] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein
self-organizing the distribution of the mobile data collector unit
and the one or more other mobile data collector units at the target
location includes proposing a target location for the mobile data
collector unit, transmitting the target location to at least one of
the one or more other mobile data collector units, receiving
confirmation that there is no contention for the target location,
directing the mobile data collector unit to the target location,
and collecting sensor data at the target location from the mobile
data collector unit.
[0915] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein
self-organizing the distribution of the mobile data collector unit
and the one or more other mobile data collector units at the target
location includes proposing a target location for the mobile data
collector unit, transmitting the target location to at least one of
the one or more other mobile data collector units, receiving a
proposal for a new target location, directing the mobile data
collector unit to the new target location and collecting sensor
data at the new target location from the mobile data collector
unit.
[0916] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein
self-organizing the distribution of the mobile data collector unit
and the one or more other mobile data collector units at the target
location includes proposing a target location for the mobile data
collector unit, determining that at least one of the one or more
other mobile data collector units is at or moving to the target
location, determining a new target location based on the at least
one of the one or more other mobile data collector units being at
or moving to the target location, directing the mobile data
collector unit to the new target location and collecting sensor
data at the new target location from the mobile data collector
unit.
[0917] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein
self-organizing the distribution of the mobile data collector unit
and the one or more other mobile data collector units at the target
location includes determining a type of the sensors to operate at
the target, receiving confirmation that there is no contention for
the type of sensors, directing the mobile data collector unit to
operate the type of sensors at the target, and [0918] collecting
sensor data from the type of sensors at the target from the mobile
data collector unit.
[0919] The present disclosure describes a method for data
collection in an industrial environment having self-organization
functionality, the method according to one disclosed non-limiting
embodiment of the present disclosure can include analyzing a
plurality of sensor inputs, sampling data received from the sensor
inputs, and self-organizing at least one of (i) a storage operation
of the data (ii) a collection operation of sensors that provide the
plurality of sensor inputs, and (iii) a selection operation of the
plurality of sensor inputs, wherein the collection operation of
sensors that provide the plurality of sensor inputs includes
receiving instructions directing a mobile data collector unit to
operate sensors at a target, wherein at least one of the plurality
of sensors is arranged in the mobile data collector unit,
transmitting a communication to one or more other mobile data
collector units regarding the instructions, self-organizing a
distribution of the mobile data collector unit and the one or more
other mobile data collector units at the target, wherein
self-organizing the distribution of the mobile data collector unit
and the one or more other mobile data collector units at the target
location includes determining a type of the sensors to operate at
the target, transmitting the type of the sensors to at least one of
the one or more other mobile data collector units, receiving a
proposal for a new type of the sensors, directing the mobile data
collector unit to operate the new type of sensors at the target and
collecting sensor data from the new type of sensors at the target
from the mobile data collector unit.
[0920] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein
self-organizing the distribution of the mobile data collector unit
and the one or more other mobile data collector units at the target
location includes determining a type of the sensors to operate at
the target, determining that at least one of the one or more other
mobile data collector units is operating or can operate the type of
the sensors at the target, determining a new type of the sensors
based on the at least one of the one or more other mobile data
collector units operating or being capable of operating the type of
the sensors at the target, directing the mobile data collector unit
to operate the new type of sensors at the target, and collecting
sensor data from the new type of sensors at the target from the
mobile data collector unit.
[0921] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein
self-organizing the distribution of the mobile data collector unit
and the one or more other mobile data collector units at the target
location includes utilizing a swarm optimization algorithm to
allocate areas of sensor responsibility amongst the mobile data
collector unit and the one or more other mobile data collector
units.
[0922] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the swarm
optimization algorithm is one or more types of Genetic Algorithms
(GA), Ant Colony Optimization (ACO), Particle Swarm Optimization
(PSO), Differential Evolution (DE), Artificial Bee Colony (ABC),
Glowworm Swarm Optimization (GSO), and Cuckoo Search Algorithm
(CSA), Genetic Programming (GP), Evolution Strategy (ES),
Evolutionary Programming (EP), Firefly Algorithm (FA), Bat
Algorithm (BA) and Grey Wolf Optimizer (GWO).
[0923] In aspects, the selection operation can comprise receiving a
signal relating to at least one condition of the industrial
environment 12000 and, based on the signal, changing at least one
of the sensor inputs analyzed and a frequency of the sampling. The
at least one condition of the industrial environment can be a
signal-to-noise ratio of the sampled data. The selection operation
can include identifying a target signal to be sensed. Additionally,
the selection operation further can include identifying one or more
non-target signals in a same frequency band as the target signal to
be sensed and, based on the identified one or more non-target
signals, changing at least one of the sensor inputs analyzed and a
frequency of the sampling.
[0924] The selection operation can comprise identifying other data
collectors sensing in a same signal band as the target signal to be
sensed, and, based on the identified other data collectors,
changing at least one of the sensor inputs analyzed and a frequency
of the sampling. In implementations, the selection operation can
further comprise identifying a level of activity of a target
associated with the target signal to be sensed and, based on the
identified level of activity, changing at least one of the sensor
inputs analyzed and a frequency of the sampling.
[0925] The selection operation can further comprise receiving data
indicative of environmental conditions near a target associated
with the target signal, comparing the received environmental
conditions of the target with past environmental conditions near
the target or another target similar to the target, and, based on
the comparison, changing at least one of the sensor inputs analyzed
and a frequency of the sampling. At least a portion of the received
sampling data can be transmitted to another data collector
according to a predetermined hierarchy of data collection.
[0926] The selection operation further comprises, in some aspects,
receiving data indicative of environmental conditions near a target
associated with the target signal, transmitting at least a portion
of the received sampling data to another data collector according
to a predetermined hierarchy of data collection, receiving feedback
via a network connection relating to a quality or sufficiency of
the transmitted data, analyzing the received feedback, and, based
on the analysis of the received feedback, changing at least one of
the sensor inputs analyzed, the frequency of sampling, the data
stored, and the data transmitted.
[0927] Additionally, or alternatively, the selection operation can
comprise receiving data indicative of environmental conditions near
a target associated with the target signal, transmitting at least a
portion of the received sampling data to another data collector
according to a predetermined hierarchy of data collection,
receiving feedback via a network connection relating to one or more
yield metrics of the transmitted data, analyzing the received
feedback, and, based on the analysis of the received feedback,
changing at least one of the sensor inputs analyzed, the frequency
of sampling, the data stored, and the data transmitted.
[0928] In implementations, the selection operation can include
receiving data indicative of environmental conditions near a target
associated with the target signal, transmitting at least a portion
of the received sampling data to another data collector according
to a predetermined hierarchy of data collection, receiving feedback
via a network connection relating to power utilization, analyzing
the received feedback, and based on the analysis of the received
feedback, changing at least one of the sensor inputs analyzed, the
frequency of sampling, the data stored, and the data
transmitted.
[0929] The selection operation can also or alternatively comprise
receiving data indicative of environmental conditions near a target
associated with the target signal, transmitting at least a portion
of the received sampling data to another data collector according
to a predetermined hierarchy of data collection, receiving feedback
via a network connection relating to a quality or sufficiency of
the transmitted data, analyzing the received feedback, and, based
on the analysis of the received feedback, executing a
dimensionality reduction algorithm on the sensed data. The
dimensionality reduction algorithm can be one or more of a Decision
Tree, Random Forest, Principal Component Analysis, Factor Analysis,
Linear Discriminant Analysis, Identification based on correlation
matrix, Missing Values Ratio, Low Variance Filter, Random
Projections, Nonnegative Matrix Factorization, Stacked
Auto-encoders, Chi-square or Information Gain, Multidimensional
Scaling, Correspondence Analysis, Factor Analysis, Clustering, and
Bayesian Models. The dimensionality reduction algorithm can be
performed at a data collector 12008, a swarm 12006 of data
collectors 12008, a network 12010, a computing system 12012, a data
pool 12014, or combination thereof. In aspects, executing the
dimensionality reduction algorithm can comprise sending the sensed
data to a remote computing device.
[0930] In aspects, a system for self-organizing collection and
storage of data collection in a power generation environment can
include a data collector for handling a plurality of sensor inputs
from various sensors. Such sensors can be a component of the data
collector, external to the data collector (e.g., external sensors
or components of different data collector(s)), or a combination
thereof. The plurality of sensor inputs can be configured to sense
at least one of an operational mode, a fault mode, and a health
status of at least one target system. Examples of such target
systems include but are not limited to a fuel handling system, a
power source, a turbine, a generator, a gear system, an electrical
transmission system, a transformer, a fuel cell, and an energy
storage device/system. The system can also include a
self-organizing system that can be configured for self-organizing
at least one of: (i) a storage operation of the data; (ii) a data
collection operation of the sensors that provide the plurality of
sensor inputs, and (iii) a selection operation of the plurality of
sensor input, as is described herein.
[0931] In aspects, the system can include a swarm 12006 of mobile
data collectors (e.g., data collectors 12008). Further, in
additional or alternative aspects, the self-organizing system can
generate, iterate, optimize, etc. a storage specification for
organizing storage of the data. The storage specification, e.g.,
can specify which data will be stored for local storage in the
power generation environment, and which data will be output for
streaming via a network connection (e.g., network 12010) from the
power generation environment. Other data collection, generation,
and/or storage operations can be performed or enabled by the
system, as is described herein.
[0932] In a non-limiting example, the system can include a
plurality of sensors configured to sense various parameters in the
environment of a turbine as a target system. Vibration sensors,
temperature sensors, acoustic sensors, strain gauges, and
accelerometers, and the like may be utilized by the system to
generate data regarding the operation of the turbine. As mentioned
herein, any and all of the storage operation, the data collection
operation, and the selection operation of the plurality of sensor
inputs may be adapted, optimized, learned, or otherwise
self-organized by the system.
[0933] In aspects, a system for self-organizing collection and
storage of data collection in energy source extraction environment
can include a data collector for handling a plurality of sensor
inputs from various sensors. Examples of such energy source
extraction environments include a coal mining environment, a metal
mining environment, a mineral mining environment, and an oil
drilling environment, although other extraction environments are
contemplated by the present disclosure. The sensors utilized can be
a component of the data collector, external to the data collector
(e.g., external sensors or components of different data
collector(s)), or a combination thereof. The plurality of sensor
inputs can be configured to sense at least one of an operational
mode, a fault mode, and a health status of at least one target
system. Examples of such target systems include but are not limited
to a hauling system, a lifting system, a drilling system, a mining
system, a digging system, a boring system, a material handling
system, a conveyor system, a pipeline system, a wastewater
treatment system, and a fluid pumping system.
[0934] The system can also include a self-organizing system that
can be configured for self-organizing at least one of: (i) a
storage operation of the data; (ii) a data collection operation of
the sensors that provide the plurality of sensor inputs, and (iii)
a selection operation of the plurality of sensor input, as is
described herein. In aspects, the system can include a swarm 12006
of mobile data collectors (e.g., data collectors 12008). Further,
in additional or alternative aspects, the self-organizing system
can generate, iterate, optimize, etc. a storage specification for
organizing storage of the data. The storage specification, e.g.,
can specify which data will be stored for local storage in the
power generation environment, and which data will be output for
streaming via a network connection (e.g., network 12010) from the
power generation environment. Other data collection, generation,
and/or storage operations can be performed or enabled by the
system, as is described herein.
[0935] In a non-limiting example, the system can include a
plurality of sensors configured to sense various parameters in the
environment of a fluid pumping system as a target system. Vibration
sensors, flow sensors, pressure sensors, temperature sensors,
acoustic sensors, and the like may be utilized by the system to
generate data regarding the operation of the fluid pumping system.
As mentioned herein, any and all of the storage operation, the data
collection operation, and the selection operation of the plurality
of sensor inputs may be adapted, optimized, learned, or otherwise
self-organized by the system.
[0936] In implementations, a system for self-organizing collection
and storage of data collection in a manufacturing environment can
include a data collector for handling a plurality of sensor inputs
from various sensors. Such sensors can be a component of the data
collector, external to the data collector (e.g., external sensors
or components of different data collector(s)), or a combination
thereof. The plurality of sensor inputs can be configured to sense
at least one of an operational mode, a fault mode, and a health
status of at least one target system. Examples of such target
systems include but are not limited to a power system, a conveyor
system, a generator, an assembly line system, a wafer handling
system, a chemical vapor deposition system, an etching system, a
printing system, a robotic handling system, a component assembly
system, an inspection system, a robotic assembly system, and a
semi-conductor production system. The system can also include a
self-organizing system that can be configured for self-organizing
at least one of: (i) a storage operation of the data; (ii) a data
collection operation of the sensors that provide the plurality of
sensor inputs, and (iii) a selection operation of the plurality of
sensor input, as is described herein.
[0937] In aspects, the system can include a swarm 12006 of mobile
data collectors (e.g., data collectors 12008). Further, in
additional or alternative aspects, the self-organizing system can
generate, iterate, optimize, etc. a storage specification for
organizing storage of the data. The storage specification, e.g.,
can specify which data will be stored for local storage in the
power generation environment, and which data will be output for
streaming via a network connection (e.g., network 12010) from the
power generation environment. Other data collection, generation,
and/or storage operations can be performed or enabled by the
system, as is described herein.
[0938] In a non-limiting example, the system can include a
plurality of sensors configured to sense various parameters in the
environment of a wafer handling system as a target system.
Vibration sensors, fluid flow sensors, pressure sensors, gas
sensors, temperature sensors, and the like may be utilized by the
system to generate data regarding the operation of the wafer
handling system. As mentioned herein, any and all of the storage
operation, the data collection operation, and the selection
operation of the plurality of sensor inputs may be adapted,
optimized, learned, or otherwise self-organized by the system.
[0939] Also disclosed are embodiments of an additional or
alternative system for self-organizing collection and storage of
data collection in refining environment. Such system(s) can include
a data collector for handling a plurality of sensor inputs from
various sensors. Examples of such refining environments include a
chemical refining environment, a pharmaceutical refining
environment, a biological refining environment, and a hydrocarbon
refining environment, although other refining environments are
contemplated by the present disclosure. The sensors utilized can be
a component of the data collector, external to the data collector
(e.g., external sensors or components of different data
collector(s)), or a combination thereof. The plurality of sensor
inputs can be configured to sense at least one of an operational
mode, a fault mode, and a health status of at least one target
system. Examples of such target systems include but are not limited
to a power system, a pumping system, a mixing system, a reaction
system, a distillation system, a fluid handling system, a heating
system, a cooling system, an evaporation system, a catalytic
system, a moving system, and a container system.
[0940] The system can also include a self-organizing system that
can be configured for self-organizing at least one of: (i) a
storage operation of the data; (ii) a data collection operation of
the sensors that provide the plurality of sensor inputs, and (iii)
a selection operation of the plurality of sensor input, as is
described herein. In aspects, the system can include a swarm 12006
of mobile data collectors (e.g., data collectors 12008). Further,
in additional or alternative aspects, the self-organizing system
can generate, iterate, optimize, etc. a storage specification for
organizing storage of the data. The storage specification, e.g.,
can specify which data will be stored for local storage in the
power generation environment, and which data will be output for
streaming via a network connection (e.g., network 12010) from the
power generation environment. Other data collection, generation,
and/or storage operations can be performed or enabled by the
system, as is described herein.
[0941] In a non-limiting example, the system can include a
plurality of sensors configured to sense various parameters in the
refining environment of a heating system as a target system.
Temperature sensors, fluid flow sensors, pressure sensors, and the
like may be utilized by the system to generate data regarding the
operation of the heating system. As mentioned herein, any and all
of the storage operation, the data collection operation, and the
selection operation of the plurality of sensor inputs may be
adapted, optimized, learned, or otherwise self-organized by the
system.
[0942] In aspects, a system for self-organizing collection and
storage of data collection in a distribution environment can
include a data collector for handling a plurality of sensor inputs
from various sensors. Such sensors can be a component of the data
collector, external to the data collector (e.g., external sensors
or components of different data collector(s)), or a combination
thereof. The plurality of sensor inputs can be configured to sense
at least one of an operational mode, a fault mode, and a health
status of at least one target system. Examples of such target
systems include but are not limited to a power system, a conveyor
system, a robotic transport system, a robotic handling system, a
packing system, a cold storage system, a hot storage system, a
refrigeration system, a vacuum system, a hauling system, a lifting
system, an inspection system, and a suspension system. The system
can also include a self-organizing system that can be configured
for self-organizing at least one of: (i) a storage operation of the
data; (ii) a data collection operation of the sensors that provide
the plurality of sensor inputs, and (iii) a selection operation of
the plurality of sensor input, as is described herein.
[0943] In aspects, the system can include a swarm 12006 of mobile
data collectors (e.g., data collectors 12008). Further, in
additional or alternative aspects, the self-organizing system can
generate, iterate, optimize, etc. a storage specification for
organizing storage of the data. The storage specification, e.g.,
can specify which data will be stored for local storage in the
power generation environment, and which data will be output for
streaming via a network connection (e.g., network 12010) from the
power generation environment. Other data collection, generation,
and/or storage operations can be performed or enabled by the
system, as is described herein.
[0944] In a non-limiting example, the system can include a
plurality of sensors configured to sense various parameters in the
distribution environment of a refrigeration system as a target
system. Power sensors, temperature sensors, vibration sensors,
strain gauges, and the like may be utilized by the system to
generate data regarding the operation of the turbine. As mentioned
herein, any and all of the storage operation, the data collection
operation, and the selection operation of the plurality of sensor
inputs may be adapted, optimized, learned, or otherwise
self-organized by the system.
[0945] The present disclosure describes a method for data
collection in an industrial environment having self-organization
functionality, the method according to one disclosed non-limiting
embodiment of the present disclosure can include analyzing a
plurality of sensor inputs, sampling data received from the sensor
inputs, and self-organizing at least one of (i) a storage operation
of the data, (ii) a collection operation of sensors that provide
the plurality of sensor inputs, and (iii) a selection operation of
the plurality of sensor inputs.
[0946] The present disclosure describes a system for data
collection in an industrial environment having automated
self-organization, the system according to one disclosed
non-limiting embodiment of the present disclosure can include a
data collector for handling a plurality of sensor inputs from
sensors in the industrial environment and for generating data
associated with the plurality of sensor inputs, and a
self-organizing system for self-organizing at least one of (i) a
storage operation of the data, (ii) a data collection operation of
sensors that provide the plurality of sensor inputs, and (iii) a
selection operation of the plurality of sensor inputs.
[0947] The present disclosure describes a method for data
collection in an industrial environment having self-organization
functionality, the method according to one disclosed non-limiting
embodiment of the present disclosure can include analyzing a
plurality of sensor inputs, sampling data received from the sensor
inputs, and self-organizing at least one of (i) a storage operation
of the data, (ii) a collection operation of sensors that provide
the plurality of sensor inputs, and (iii) a selection operation of
the plurality of sensor inputs, wherein the selection operation
includes receiving a signal relating to at least one condition of
the industrial environment, based on the signal, changing at least
one of the sensor inputs analyzed and a frequency of the
sampling.
[0948] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the at least
one condition of the industrial environment is a signal-to-noise
ratio of the sampled data.
[0949] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the selection
operation includes identifying a target signal to be sensed.
[0950] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the selection
operation further includes identifying one or more non-target
signals in a same frequency band as the target signal to be sensed,
and based on the identified one or more non-target signals,
changing at least one of the sensor inputs analyzed and a frequency
of the sampling.
[0951] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the selection
operation further includes identifying other data collectors
sensing in a same signal band as the target signal to be sensed,
and based on the identified other data collectors, changing at
least one of the sensor inputs analyzed and a frequency of the
sampling.
[0952] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the selection
operation further includes identifying a level of activity of a
target associated with the target signal to be sensed, and based on
the identified level of activity, changing at least one of the
sensor inputs analyzed and a frequency of the sampling.
[0953] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the selection
operation further includes receiving data indicative of
environmental conditions near a target associated with the target
signal, comparing the received environmental conditions of the
target with past environmental conditions near the target or
another target similar to the target, and based on the comparison,
changing at least one of the sensor inputs analyzed and a frequency
of the sampling.
[0954] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the selection
operation further includes transmitting at least a portion of the
received sampling data to another data collector according to a
predetermined hierarchy of data collection.
[0955] The present disclosure describes a method for data
collection in an industrial environment having self-organization
functionality, the method according to one disclosed non-limiting
embodiment of the present disclosure can include analyzing a
plurality of sensor inputs, sampling data received from the sensor
inputs, and self-organizing at least one of (i) a storage operation
of the data, (ii) a collection operation of sensors that provide
the plurality of sensor inputs, and (iii) a selection operation of
the plurality of sensor inputs, wherein the selection operation
includes identifying a target signal to be sensed, receiving a
signal relating to at least one condition of the industrial
environment, based on the signal, changing at least one of the
sensor inputs analyzed and a frequency of the sampling, receiving
data indicative of environmental conditions near a target
associated with the target signal, transmitting at least a portion
of the received sampling data to another data collector according
to a predetermined hierarchy of data collection, receiving feedback
via a network connection relating to a quality or sufficiency of
the transmitted data, analyzing the received feedback, and based on
the analysis of the received feedback, changing at least one of the
sensor inputs analyzed, the frequency of sampling, the data stored,
and the data transmitted.
[0956] The present disclosure describes a method for data
collection in an industrial environment having self-organization
functionality, the method according to one disclosed non-limiting
embodiment of the present disclosure can include analyzing a
plurality of sensor inputs, sampling data received from the sensor
inputs, and self-organizing at least one of (i) a storage operation
of the data, (ii) a collection operation of sensors that provide
the plurality of sensor inputs, and (iii) a selection operation of
the plurality of sensor inputs, wherein the selection operation
includes identifying a target signal to be sensed, receiving a
signal relating to at least one condition of the industrial
environment, based on the signal, changing at least one of the
sensor inputs analyzed and a frequency of the sampling, receiving
data indicative of environmental conditions near a target
associated with the target signal, transmitting at least a portion
of the received sampling data to another data collector according
to a predetermined hierarchy of data collection, receiving feedback
via a network connection relating to one or more yield metrics of
the transmitted data, analyzing the received feedback, and based on
the analysis of the received feedback, changing at least one of the
sensor inputs analyzed, the frequency of sampling, the data stored,
and the data transmitted.
[0957] The present disclosure describes a method for data
collection in an industrial environment having self-organization
functionality, the method according to one disclosed non-limiting
embodiment of the present disclosure can include analyzing a
plurality of sensor inputs, sampling data received from the sensor
inputs, and self-organizing at least one of (i) a storage operation
of the data, (ii) a collection operation of sensors that provide
the plurality of sensor inputs, and (iii) a selection operation of
the plurality of sensor inputs, wherein the selection operation
includes identifying a target signal to be sensed, receiving a
signal relating to at least one condition of the industrial
environment, based on the signal, changing at least one of the
sensor inputs analyzed and a frequency of the sampling, receiving
data indicative of environmental conditions near a target
associated with the target signal, transmitting at least a portion
of the received sampling data to another data collector according
to a predetermined hierarchy of data collection, receiving
feedback, via a network connection relating to power utilization,
analyzing the received feedback, and based on the analysis of the
received feedback, changing at least one of the sensor inputs
analyzed, the frequency of sampling, the data stored, and the data
transmitted.
[0958] The present disclosure describes a method for data
collection in an industrial environment having self-organization
functionality, the method according to one disclosed non-limiting
embodiment of the present disclosure can include analyzing a
plurality of sensor inputs, sampling data received from the sensor
inputs, and self-organizing at least one of (i) a storage operation
of the data, (ii) a collection operation of sensors that provide
the plurality of sensor inputs, and (iii) a selection operation of
the plurality of sensor inputs, wherein the selection operation
includes identifying a target signal to be sensed, receiving a
signal relating to at least one condition of the industrial
environment, based on the signal, changing at least one of the
sensor inputs analyzed and a frequency of the sampling, receiving
data indicative of environmental conditions near a target
associated with the target signal, transmitting at least a portion
of the received sampling data to another data collector according
to a predetermined hierarchy of data collection, receiving feedback
via a network connection relating to a quality or sufficiency of
the transmitted data, analyzing the received feedback, and based on
the analysis of the received feedback, executing a dimensionality
reduction algorithm on the sensed data.
[0959] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the
dimensionality reduction algorithm is one or more of a Decision
Tree, Random Forest, Principal Component Analysis, Factor Analysis,
Linear Discriminant Analysis, Identification based on correlation
matrix, Missing Values Ratio, Low Variance Filter, Random
Projections, Nonnegative Matrix Factorization, Stacked
Auto-encoders, Chi-square or Information Gain, Multidimensional
Scaling, Correspondence Analysis, Factor Analysis, Clustering, and
Bayesian Models.
[0960] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the
dimensionality reduction algorithm is performed at a data
collector.
[0961] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein executing the
dimensionality reduction algorithm includes sending the sensed data
to a remote computing device.
[0962] The present disclosure describes a method for data
collection in an industrial environment having self-organization
functionality, the method according to one disclosed non-limiting
embodiment of the present disclosure can include analyzing a
plurality of sensor inputs, sampling data received from the sensor
inputs, and self-organizing at least one of (i) a storage operation
of the data, (ii) a collection operation of sensors that provide
the plurality of sensor inputs, and (iii) a selection operation of
the plurality of sensor inputs, wherein the selection operation
includes identifying a target signal to be sensed, receiving a
signal relating to at least one condition of the industrial
environment, based on the signal, changing at least one of the
sensor inputs analyzed and a frequency of the sampling, receiving
data indicative of environmental conditions near a target
associated with the target signal, transmitting at least a portion
of the received sampling data to another data collector according
to a predetermined hierarchy of data collection, receiving feedback
via a network connection relating to at least one of a bandwidth
and a quality or of the network connection, analyzing the received
feedback, and based on the analysis of the received feedback,
changing at least one of the sensor inputs analyzed, the frequency
of sampling, the data stored, and the data transmitted.
[0963] The present disclosure describes a system for
self-organizing collection and storage of data collection in a
power generation environment, the system according to one disclosed
non-limiting embodiment of the present disclosure can include a
data collector for handling a plurality of sensor inputs from
sensors in the power generation environment, wherein the plurality
of sensor inputs is configured to sense at least one of an
operational mode, a fault mode, and a health status of at least one
target system selected from a group consisting of a fuel handling
system, a power source, a turbine, a generator, a gear system, an
electrical transmission system, and a transformer, and a
self-organizing system for self-organizing at least one of (i) a
storage operation of the data, (ii) a data collection operation of
the sensors that provide the plurality of sensor inputs, and (iii)
a selection operation of the plurality of sensor inputs.
[0964] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the
self-organizing system organizes a swarm of mobile data collectors
to collect data from a plurality of target systems.
[0965] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the
self-organizing system generates a storage specification for
organizing storage of the data, the storage specification
specifying data for local storage in the power generation
environment and specifying data for streaming via a network
connection from the power generation environment.
[0966] The present disclosure describes a system for
self-organizing collection and storage of data collection in an
energy source extraction environment, the system according to one
disclosed non-limiting embodiment of the present disclosure can
include a data collector for handling a plurality of sensor inputs
from sensors in the energy extraction environment, wherein the
plurality of sensor inputs is configured to sense at least one of
an operational mode, a fault mode, and a health status of at least
one target system selected from a group consisting of a hauling
system, a lifting system, a drilling system, a mining system, a
digging system, a boring system, a material handling system, a
conveyor system, a pipeline system, a wastewater treatment system,
and a fluid pumping system, and a self-organizing system for
self-organizing at least one of (i) a storage operation of the
data, (ii) a data collection operation of the sensors that provide
the plurality of sensor inputs, and (iii) a selection operation of
the plurality of sensor inputs.
[0967] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the
self-organizing system organizes a swarm of mobile data collectors
to collect data from a plurality of target systems.
[0968] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the
self-organizing system generates a storage specification for
organizing storage of the data, the storage specification
specifying data for local storage in the energy extraction
environment and specifying data for streaming via a network
connection from the energy extraction environment.
[0969] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the energy
source extraction environment is a coal mining environment.
[0970] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the energy
source extraction environment is a metal mining environment.
[0971] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the energy
source extraction environment is a mineral mining environment.
[0972] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the energy
source extraction environment is an oil drilling environment.
[0973] The present disclosure describes a system for
self-organizing collection and storage of data collection in a
manufacturing environment, the system according to one disclosed
non-limiting embodiment of the present disclosure can include a
data collector for handling a plurality of sensor inputs from
sensors in the power generation environment, wherein the plurality
of sensor inputs is configured to sense at least one of an
operational mode, a fault mode, and a health status of at least one
target system selected from a group consisting of a power system, a
conveyor system, a generator, an assembly line system, a wafer
handling system, a chemical vapor deposition system, an etching
system, a printing system, a robotic handling system, a component
assembly system, an inspection system, a robotic assembly system,
and a semi-conductor production system, and a self-organizing
system for self-organizing at least one of (i) a storage operation
of the data, (ii) a data collection operation of the sensors that
provide the plurality of sensor inputs, and (iii) a selection
operation of the plurality of sensor inputs.
[0974] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the
self-organizing system organizes a swarm of mobile data collectors
to collect data from a plurality of target systems.
[0975] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the
self-organizing system generates a storage specification for
organizing the storage of the data, the storage specification
specifying data for local storage in the manufacturing environment
and specifying data for streaming via a network connection from the
manufacturing environment.
[0976] The present disclosure describes a system for
self-organizing collection and storage of data collection in a
refining environment, the system according to one disclosed
non-limiting embodiment of the present disclosure can include a
data collector for handling a plurality of sensor inputs from
sensors in the power generation environment, wherein the plurality
of sensor inputs is configured to sense at least one of an
operational mode, a fault mode and a health status of at least one
target system selected from a group consisting of a power system, a
pumping system, a mixing system, a reaction system, a distillation
system, a fluid handling system, a heating system, a cooling
system, an evaporation system, a catalytic system, a moving system,
and a container system, and a self-organizing system for
self-organizing at least one of (i) a storage operation of the
data, (ii) a data collection operation of the sensors that provide
the plurality of sensor inputs, and (iii) a selection operation of
the plurality of sensor inputs.
[0977] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the
self-organizing system organizes a swarm of mobile data collectors
to collect data from a plurality of target systems.
[0978] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the
self-organizing system generates a storage specification for
organizing the storage of the data, the storage specification
specifying data for local storage in the refining environment and
specifying data for streaming via a network connection from the
refining environment.
[0979] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the refining
environment is a chemical refining environment.
[0980] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the refining
environment is a pharmaceutical refining environment.
[0981] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the refining
environment is a biological refining environment.
[0982] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the refining
environment is a hydrocarbon refining environment.
[0983] The present disclosure describes a system for
self-organizing collection and storage of data collection in a
distribution environment, the system according to one disclosed
non-limiting embodiment of the present disclosure can include a
data collector for handling a plurality of sensor inputs from
sensors in the distribution environment, wherein the plurality of
sensor inputs is configured to sense at least one of an operational
mode, a fault mode and a health status of at least one target
system selected from a group consisting of a power system, a
conveyor system, a robotic transport system, a robotic handling
system, a packing system, a cold storage system, a hot storage
system, a refrigeration system, a vacuum system, a hauling system,
a lifting system, an inspection system, and a suspension system,
and a self-organizing system for self-organizing at least one of
(i) a storage operation of the data, (ii) a data collection
operation of the sensors that provide the plurality of sensor
inputs, and (iii) a selection operation of the plurality of sensor
inputs.
[0984] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the
self-organizing system organizes a swarm of mobile data collectors
to collect data from a plurality of target systems.
[0985] A further embodiment of any of the foregoing embodiments of
the present disclosure may include situations wherein the
self-organizing system generates a storage specification for
organizing the storage of the data, the storage specification
specifying data for local storage in the distribution environment
and specifying data for streaming via a network connection from the
distribution environment.
[0986] Referencing FIG. 83, various aspects of an example data
storage profile 12532 are depicted. The example data storage
profile 12532 includes aspects of the data storage profile 12532
that may be included as additional or alternative aspects of the
data storage profile 12532 relative to the storage location
definition 12534, the storage time definition, and/or the storage
time definition 12536, data resolution description 12540, and/or
may be included as aspects of these. Any one or more of the factors
or parameters relating to storage depicted in FIG. 179 may be
included in a data storage profile 12532 and/or managed by a
self-organizing storage system (e.g., system 12500 and/or
controller 12532). The self-organizing storage system may manage or
optimize any such parameters or factors noted throughout this
disclosure, individually or in combination, using an expert system,
which may involve a rule-based optimization, optimization based on
a model of performance, and/or optimization using machine
learning/artificial intelligence, optionally including deep
learning approaches, or a hybrid or combination of the above. In
embodiments, an example data storage profile 12532 includes a
storage type plan 12576 or profile that accounts for or specifies a
type of storage, such as based on the underlying physical media
type of the storage, the type of device or system on which storage
resides, the mechanism by which storage can be accessed for reading
or writing data, or the like. For example, a storage media plan
12578 may specify or account for use of tape media, hard disk drive
media, flash memory media, non-volatile memory, optical media,
one-time programmable memory, or the like. The storage media plan
may account for or specify parameters relating to the media,
including capabilities such as storage duration, power usage,
reliability, redundancy, thermal performance factors, robustness to
environmental conditions (such as radiation or extreme
temperatures), input/output speeds and capabilities, writing
speeds, reading speeds, and the like, or other media specific
parameters such as data file organization, operating system,
read-write life cycle, data error rates, and/or data compression
aspects related to or inherent to the media or media controller. A
storage access plan 12580 or profile may specify or account for the
nature of the interface to available storage, such as database
storage (including relational, object-oriented, and other
databases, as well as distributed databases, virtual machines,
cloud-based databases, and the like), cloud storage (such as S3.TM.
buckets and other simple storage formats), stream-based storage,
cache storage, edge storage (e.g., in edge-based network nodes),
on-device storage, server-based storage, network-attached storage
or the like. The storage access plan or profile may specify or
account for factors such as the cost of different storage types,
input/output performance, reliability, complexity, size, and other
factors. A storage protocol plan 12582 or profile may specify or
account for a protocol by which data will be transmitted or
written, such as a streaming protocol, an IP-based protocol, a
non-volatile memory express protocol, a SATA protocol or other
network-attached storage protocol, a disk-attached storage
protocol, an Ethernet protocol, a peered storage protocol, a
distributed ledger protocol, a packet-based storage protocol, a
batch-based storage protocol, a metadata storage protocol, a
compressed storage protocol (using various compression types, such
as for packet-based media, streaming media, lossy or lossless
compression types, and the like), or others. The storage protocol
plan may account for or specify factors relating to the storage
protocol, such as input/output performance, compatibility with
available network resources, cost, complexity, data processing
required to implement the protocol, network utilization to support
the protocol, robustness of the protocol to support system noise
(e.g., EM, competing network traffic, interruption frequency of
network availability), memory utilization to implement the protocol
(such as: as-stored memory utilization, and/or intermediate memory
utilization in creating or transferring the data), and the like. A
storage writing protocol 12584 plan or profile may specify or
account for how data will be written to storage, such as in file
form, in streaming form, in batch form, in discrete chunks, to
partitions, in stripes or bands across different storage locations,
in streams, in packets or the like. The storage writing protocol
may account for or specify parameters and factors relating to
writing, such as input speed, reliability, redundancy, security,
and the like. A storage security plan 12586 or profile may account
for or specify how storage will be secured, such as availability or
type of password protection, authentication, permissioning, rights
management, encryption (of the data, of the storage media, and/or
of network traffic on the system), physical isolation, network
isolation, geographic placement, and the like. A storage location
plan 12588 or profile may account for or specify a location for
storage, such as a geolocation, a network location (e.g., at the
edge, on a given server, or within a given cloud platform or
platforms), or a location on a device, such as a location on a data
collector, a location on a handheld device (such as a smart phone,
tablet, or personal computer of an operator within an environment),
a location within or across a group of devices (such as a mesh, a
peer-to-peer group, a ring, a hub-and-spoke group, a set of
parallel devices, a swarm of devices (such as a swarm of
collectors), or the like), a location in an industrial environment
(such as or within an storage element of an instrumentation system
of or for a machine, a location on an information technology system
for the environment, or the like), or a dedicated storage system,
such as a disk, dongle, USB device, or the like. A storage backup
plan 12590 or profile may account for or specify a plan for backup
or redundancy of stored data, such as indicating redundant
locations and managing any or all of the above factors for a backup
storage location. In certain embodiments, the storage security plan
12586 and/or storage backup plan 12590 may specify parameters such
as data retention, long-term storage plans (e.g., migrate the
stored data to a different storage media after a period of time
and/or after certain operations in the system are performed on the
data), physical risk management of the data and/or storage media
(e.g., provision of the data in multiple geographic regions having
distinct physical risk parameters, movement of the data when a
storage location experiences a physical risk, refreshing the data
according to a predicted life cycle of a long-term storage media,
etc.).
[0987] The example controller 12512 further includes a sensor data
storage implementation circuit 12526 that stores at least a portion
of the number of sensor data values in response to the data storage
profile 12532. An example controller 12512 includes the data
storage profile 12532 having a storage location definition 12534
corresponding to at least one of the number of sensor data values
12542, including at least one location such as: a sensor storage
location (e.g., data stored for a period of time on the sensor,
and/or on a portable device for a user 12518 in proximity to the
industrial system 12502 where the portable device is adapted by the
system as a sensor), a sensor communication device storage location
(e.g., a data controller 12508, MUX device, smart sensor in
communication with other sensors, and/or on a portable device for a
user 12518 in proximity to the industrial system 12502 or a network
of the industrial system 12502 where the portable device is adapted
by the system as a communication device to transfer sensor data
between components in the system, etc.), a regional network storage
location (e.g., on a plant computer 12510 and/or controller 12512),
and/or a global network storage location (e.g., on a cloud
computing device 12514).
[0988] An example controller 12512 includes the data storage
profile 12532 including a storage time definition 12536
corresponding to at least one of the number of sensor data values
12542, including at least one time value such as: a time domain
description over which the corresponding at least one of the number
of sensor data values is to be stored (e.g., times and locations
for the data, which may include relative time to some aspect such
as the time of data sampling, a process stage start or stop time,
etc., or an absolute time such as midnight, Saturday, the first of
the month, etc.); a time domain storage trajectory including a
number of time values corresponding to a number of storage
locations over which the corresponding at least one of the number
of sensor data values is to be stored (e.g., the flow of the sensor
data through the system across a number of devices, with the time
for each storage transfer including a relative or absolute time
description); a process description value over which the
corresponding at least one of the number of sensor data values is
to be stored (e.g., including a process description and the planned
storage location for data values during the described process
portion; the process description can include stages of a process,
and identification of which process is related to the storage plan,
and the like); and/or a process description trajectory including a
number of process stages corresponding to a number of storage
locations over which the corresponding at least one of the number
of sensor data values is to be stored (e.g., the flow of the sensor
data through the system across a number of devices, with process
stage and/or process identification for each storage transfer).
[0989] An example controller 12512 includes the data storage
profile 12532 including a data resolution description 12540
corresponding to at least one of the number of sensor data values
12544, where the data resolution description 12540 includes a value
such as: a detection density value corresponding to the at least
one of the number of sensor data values (e.g., detection density
may be time sampling resolution, spatial sampling resolution,
precision of the sampled data, and/or a processing operation to be
applied that may affect the available resolution, such as filtering
and/or lossy compression of the data); a detection density value
corresponding to a more than one of the number of the sensor data
values (e.g., a group of sensors having similar detection density
values, a secondary data value determined from a group of sensors
having a specified detection density value, etc.); a detection
density trajectory including a number of detection density values
of the at least one of the number of sensor data values, each of
the number of detection density values corresponding to a time
value (e.g., any of the detection density concepts combined with
any of the time domain concepts); a detection density trajectory
including a number of detection density values of the at least one
of the number of sensor data values, each of the number of
detection density values corresponding to a process stage value
(e.g., any of the detection density concepts combined with any of
the process description or stage concepts); and/or a detection
density trajectory comprising a number of detection density values
of the at least one of the number of sensor data values, each of
the number of detection density values corresponding to a storage
location value (e.g., detection density can be varied according to
the device storing the data).
[0990] An example sensor data storage profile circuit 12524 further
updates the data storage profile 12532 after the operations of the
sensor data storage implementation circuit 12526, where the sensor
data storage implementation circuit 12526 further stores the
portion of the number of sensor data values 12544 in response to
the updated data storage profile 12532. For example, during
operations of a system at a first point in time, the sensor data
storage implementation circuit 12526 utilizes a currently existing
data storage profile sensor data storage implementation circuit
12526, which may be based on initial estimates of the system
performance, desired data from an operator of the system, and/or
from a previous operation of the sensor data storage profile
circuit 12524. During operations of the system, the sensor data
storage implementation circuit 12526 stores data according to the
data storage profile 12532, and the sensor data storage profile
circuit 12524 determines parameters for the data storage profile
12532 which may result in improved performance of the system. An
example sensor data storage profile circuit 12524 tests various
parameters for the data storage profile 12532, for example
utilizing a machine learning optimization routine, and upon
determining that an improved data storage profile 12532 is
available, the sensor data storage profile circuit 12524 provides
the updated data storage profile 12532 which is utilized by the
sensor data storage implementation circuit 12526. In certain
embodiments, the sensor data storage profile circuit 12524 may
perform various operations such as supplying an intermediate data
storage profile 12532 which is utilized by the sensor data storage
implementation circuit 12526 to produce real-world results, applies
modeling to the system (either first principles modeling based on
system characteristics, a model utilizing actual operating data for
the system, a model utilizing actual operating data for an offset
system, and/or combinations of these) to determine what an outcome
of a given data storage profile 12532 will be or would have been
(including, for example, taking extra sensor data beyond what is
utilized to support a process operated by the system), and/or
applying randomized changes to the data storage profile 12532 to
ensure that an optimization routine does not settle into a local
optimum or non-optimal condition.
[0991] An example sensor data storage profile circuit 12524 further
updates the data storage profile 12532 in response to external data
12544 and/or cloud-based data 12538, including data such as: an
enhanced data request value (e.g., an operator, model, optimization
routine, and/or other process requests enhanced data resolution for
one or more parameters); a process success value (e.g., indicating
that current storage practice provides for sufficient data
availability and/or system performance; and/or that current storage
practice may be over-capable, and one or more changes to reduce
system utilization may be available); a process failure value
(e.g., indicating that current storage practices may not provide
for sufficient data availability and/or system performance, which
may include additional operations or alerts to an operator to
determine whether the data transmission and/or availability
contributed to the process failure); a component service value
(e.g., an operation to adjust the data storage to ensure higher
resolution data is available to improve a learning algorithm
predicting future service events, and/or to determine which factors
may have contributed to premature service); a component maintenance
value (e.g., an operation to adjust the data storage to ensure
higher resolution data is available to improve a learning algorithm
predicting future maintenance events, and/or to determine which
factors may have contributed to premature maintenance); a network
description value (e.g., a change in the network, for example by
identification of devices, determination of protocols, and/or as
entered by a user or operator, where the network change results in
a capability change and potentially a distinct optimal storage plan
for sensor data); a process feedback value (e.g., one or more
process conditions detected); a network feedback value (e.g., one
or more network changes as determined by actual operations of the
network--e.g., a loss or reduction in communication of one or more
devices, a network communication volume change, a transmission
noise value change on the network, etc.); a sensor feedback value
(e.g., metadata such as a sensor fault, capability change; and/or
based on the detected data from the system, for example an
anomalous reading, rate of change, or off-nominal condition
indicating that enhanced or reduced resolution, sampling time, etc.
should change the storage plan); and/or a second data storage
profile, where the second data storage profile was generated for an
offset system.
[0992] An example storage planning circuit 12528 determines a data
configuration plan 12546 and updates the data storage profile 12532
in response to the data configuration plan 12546, where the sensor
data storage implementation circuit 12526 further stores at least a
portion of the number of sensor data values in response to the
updated data storage profile 12532. An example data configuration
plan 12546 includes a value such as: a data storage structure value
(e.g., a data type, such as integer, string, a comma delimited
file, how many bits are committed to the values, etc.); a data
compression value (e.g., whether to compress data, a compression
model to use, and/or whether segments of data can be replaced with
summary information, polynomial or other curve fit summarizations,
etc.); a data write strategy value (e.g., whether to store values
in a distributed manner or on a single device, which network
communication and/or operating system protocols to utilize); a data
hierarchy value (e.g., which data is favored over other data where
storage constraints and/or communication constraints will limit the
stored data--the limits may be temporal, such as data will not be
in the intended location at the intended time, or permanent, such
as some data will need to be compressed in a lossy manner, and/or
lost); an enhanced access value determined for the data (e.g., the
data is of a type for reports, searching, modeling access, and/or
otherwise tagged, where enhanced access includes where the data is
stored for scope of availability, indexing of data, summarization
of data, topical reports of data, which may be stored in addition
to the raw or processed sensor data); and/or an instruction value
corresponding to the data (e.g., a placeholder indicating where
data can be located, an interface to access the data, metadata
indicating units, precision, time frames, processes in operation,
faults present, outcomes, etc.).
[0993] It can be seen that the provision of control over data flow
and storage through the system allows for improvement generally,
and movement toward optimization over time, of data management
throughout the system. Accordingly, more data of a higher
resolution can be accumulated, and in a more readily accessible
manner, than previously known systems with fixed or manually
configurable data storage and flow for a given utilization of
resources such as storage space, communication bandwidth, power
consumption, and/or processor execution cycles. Additionally, the
system can respond to process variations that affect the optimal or
beneficial parameters for controlling data flow and storage. One of
skill in the art, having the benefit of the disclosures herein,
will recognize that combinations of control of data storage schemes
with data type control and knowledge about process operations for a
system create powerful combinations in certain contemplated
embodiments. For example, data of a higher resolution can be
maintained for a longer period and made available if a need for the
data arises, without incurring the full cost of storing the data
permanently and/or communicating the data throughout every layer of
the system.
[0994] Referencing FIG. 81, an example storage time definition
12536 is depicted. The example storage time definition 12536
depicts a number of storage locations 12556 corresponding to a
number of time values 12558. It is understood that any values such
as storage types, storage media, storage access, storage protocols,
storage writing values, storage security, and/or storage backup
values, may be included in the storage time definition 12536.
Additionally or alternatively, an example storage time definition
12536 may include process operations, events, and/or other values
in addition to or as an alternative to time values 12558. The
example storage time definition 12536 depicts movement of related
sensor data to a first storage location 12550 over a first time
interval, to a second storage location 12552 over a second time
internal, and to a third storage location 12554 over a third time
interval. The storage location values 12550, 12552, 12554 are
depicted as an integral selection corresponding to planned storage
locations, but additionally or alternatively the values may be
continuous or discrete, but not necessarily integral values. For
example, a storage location value 12550 of "1" may be associated
with a first storage location, and a storage location value 12550
of "2" may be associated with a second storage location, where a
value between "1" and "2" has an understood meaning--such as a
prioritization to move the data (e.g., a "1.1" indicates that the
data should be moved from "2" to "1" with a relatively high
priority compared to a "1.4"), a percentage of the data to be moved
(e.g., to control network utilization, memory utilization, or the
like during a transfer operation), and/or a preference for a
storage location with alternative options (e.g., to allow for
directing storage location, and inclusion in a cost function such
that storage location can be balanced with other constraints in the
system). Additionally or alternatively, the storage time definition
12536 can include additional dimensions (e.g., changing protocols,
media, security plans, etc.) and/or can include multiple options
for the storage plan (e.g., providing a weighted value between 2,
3, 4, or more storage locations, protocols, media, etc. in a
triangulated or multiple-dimension definition space).
[0995] Referencing FIG. 82 an example data resolution description
12540 is depicted. The example data resolution description 12540
depicts a number of data resolution values 12562 corresponding to a
number of time values 12564. It is understood that any values such
as storage types, storage media, storage access, storage protocols,
storage writing values, storage security, and/or storage backup
values, may be included in the data resolution description 12540.
Additionally or alternatively, an example data resolution
description 12540 may include process operations, events, and/or
other values in addition to or as an alternative to time values
12558. The example data resolution description 12540 depicts
changes in the resolution of stored related sensor data resolution
values 12560 over time intervals, for example operating at a low
resolution initially, stepping up to a higher resolution (e.g.,
corresponding to a process start time), to a high resolution value
(e.g., during a process time where the process is significantly
improved by high resolution of the related sensor data), and to a
low resolution value (e.g., after a completion of the process). The
example depicts a higher resolution before the process starts than
after the process ends as an illustrative example, but the data
resolution description 12540 may include any data resolution
trajectory. The data resolution values 12560 are depicted as
integral selections corresponding to planned data resolutions, but
additionally or alternatively the values may be continuous or
discrete, but not necessarily integral values. For example, data
resolution values 12560 of "1" may be associated with a first data
resolution (e.g., a specific sampling time, byte resolution, etc.),
and a data resolution values 12560 of "2" may be associated with a
second data resolution, where a value between "1" and "2" has an
understood meaning--such as a prioritization to sample at the
defined resolution (e.g., a "1.1" indicates the data should be
taken at a sampling rate corresponding to "1" with a relatively
high priority compared to a "1.3", and/or at a sampling rate 10% of
the way between the rate between "1" and "2"), and/or a preference
for a data resolution with alternative options (e.g., to allow for
sensor or network limitations, available sensor communication
devices such as a data controller, smart sensor, or portable device
taking the data from the sensor, and/or inclusion in a cost
function such that data resolution can be balanced with other
constraints in the system). Additionally or alternatively, the data
resolution description 12540 can include additional dimensions
(e.g., changing protocols, media, security plans, etc.) and/or can
include multiple options for the data resolution plan (e.g.,
providing a weighted value between 2, 3, 4, or more data resolution
values, protocols, media, etc. in a triangulated or
multiple-dimension definition space).
[0996] An example system 12500 further includes a haptic feedback
circuit 12530 that determines a haptic feedback instruction 12548
in response to at least one of the number of sensor values 12542
and/or the data storage profile 12532, and a haptic feedback device
12516 responsive to the haptic feedback instruction 12548. Example
and non-limiting haptic feedback instructions 12548 include an
instruction such as: a vibration command; a temperature command; a
sound command; an electrical command; and/or a light command
Example and non-limiting operations of the haptic feedback circuit
12530 include feedback that data is stored or being stored on the
haptic feedback device 12516 and/or on a portable device associated
with the user 12518 in communication with the haptic feedback
device 12516 (e.g., user 12518 traverses through the system 12500
with a smart phone, which the system 12500 utilizes to store sensor
data, and provides a haptic feedback instructions 12548 to notify
the user 12518 that the smart phone is currently being utilized by
the system 12500, for example allowing the user 12518 to remain in
communication with the sensor, data controller, or other
transmitting device, and/or allowing the user to actively cancel or
enable the data transfer). Additionally or alternatively, the
haptic feedback device 12516 may be the smart phone (e g ,
utilizing vibration, sound, light, or other haptic aspects of the
smart phone), and/or the haptic feedback device 12516 may include
data storage and/or communication capabilities.
[0997] In certain embodiments, the haptic feedback circuit 12530
provides a haptic feedback instruction 12548 as an alert or
notification to the user 12518, for example to alert or notify the
user 12518 that a process has commenced or is about to start, that
an off-nominal operation is detected or predicted, that a component
of the system requires or is predicted to require maintenance, that
an aspect of the system is in a condition that the user 12518 may
want to be aware of (e.g., a component is still powered, has high
potential energy of any type, is at a high pressure, and/or is at a
high temperature where the user 12518 may be in proximity to the
component), that a data storage related aspect of the system is in
a noteworthy condition (e.g., a data storage component of the
system is at capacity, out of communication, is in a fault
condition, has lost contact with a sensor, etc.), to request a
response from the user 12518 (e.g., an approval to start a process,
data transfer, process rate change, clear a fault, etc.) In certain
embodiments, the haptic feedback circuit 12530 configures the
haptic feedback instruction 12548 to provide an intuitive feedback
to the user 12518. For example, an alert value may provide a more
rapid, urgent, and/or intermittent vibration mode relative to an
informational notification; a temperature based alert or
notification may utilize a temperature based haptic feedback (e.g.,
an overtemperature vessel notification may provide a warm or cold
haptic feedback) and/or flashing a color that is associated with
the temperature (e.g., flashing red for an overtemperature or blue
for an under-temperature); an electrically based notification may
provide an electrically associated haptic feedback (e.g., a sound
associated with electricity such as a buzzing or sparking sound, or
even a mild electrical feedback such as when a user is opening a
panel for a component that is still powered); providing a vibration
feedback for a bearing, motor, or other rotating or vibrating
component that is operating off-nominally; and/or providing a
requested feedback to the user based upon sensed data (e.g.,
transmitting a vibration profile to the haptic feedback device that
is analogous to the detected vibration in a requested component for
example allowing an expert user to diagnose the component without
physical contact; providing a haptic feedback for a requested
component for example if the user is double checking a
lockout/tagout operation before entering a component, opening a
panel, and/or entering a potentially hazardous area). The provided
examples for operations of the haptic feedback circuit 12530 are
non-limiting illustrations.
[0998] While the foregoing written description enables one skilled
in the art to make and use what is considered presently to be the
best mode thereof, those skilled in the art will understand and
appreciate the existence of variations, combinations, and
equivalents of the specific embodiment, method, and examples
herein. The disclosure should therefore not be limited by the above
described embodiment, method, and examples, but by all embodiments
and methods within the scope and spirit of the disclosure.
[0999] The methods and systems described herein may be deployed in
part or in whole through a machine that executes computer software,
program codes, and/or instructions on a processor. The present
disclosure may be implemented as a method on the machine, as a
system or apparatus as part of or in relation to the machine, or as
a computer program product embodied in a computer readable medium
executing on one or more of the machines. In embodiments, the
processor may be part of a server, cloud server, client, network
infrastructure, mobile computing platform, stationary computing
platform, or other computing platform. A processor may be any kind
of computational or processing device capable of executing program
instructions, codes, binary instructions, and the like. The
processor may be or may include a signal processor, digital
processor, embedded processor, microprocessor, or any variant such
as a co-processor (math co-processor, graphic co-processor,
communication co-processor, and the like) and the like that may
directly or indirectly facilitate execution of program code or
program instructions stored thereon. In addition, the processor may
enable execution of multiple programs, threads, and codes. The
threads may be executed simultaneously to enhance the performance
of the processor and to facilitate simultaneous operations of the
application. By way of implementation, methods, program codes,
program instructions, and the like described herein may be
implemented in one or more thread. The thread may spawn other
threads that may have assigned priorities associated with them; the
processor may execute these threads based on priority or any other
order based on instructions provided in the program code. The
processor, or any machine utilizing one, may include non-transitory
memory that stores methods, codes, instructions, and programs as
described herein and elsewhere. The processor may access a
non-transitory storage medium through an interface that may store
methods, codes, and instructions as described herein and elsewhere.
The storage medium associated with the processor for storing
methods, programs, codes, program instructions, or other type of
instructions capable of being executed by the computing or
processing device may include but may not be limited to one or more
of a CD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache,
and the like.
[1000] A processor may include one or more cores that may enhance
speed and performance of a multiprocessor. In embodiments, the
process may be a dual core processor, quad core processors, other
chip-level multiprocessor and the like that combine two or more
independent cores (called a die).
[1001] The methods and systems described herein may be deployed in
part or in whole through a machine that executes computer software
on a server, client, firewall, gateway, hub, router, or other such
computer and/or networking hardware. The software program may be
associated with a server that may include a file server, print
server, domain server, internet server, intranet server, cloud
server, and other variants such as secondary server, host server,
distributed server, and the like. The server may include one or
more of memories, processors, computer readable transitory and/or
non-transitory media, storage media, ports (physical and virtual),
communication devices, and interfaces capable of accessing other
servers, clients, machines, and devices through a wired or a
wireless medium, and the like. The methods, programs, or codes as
described herein and elsewhere may be executed by the server. In
addition, other devices required for execution of methods as
described in this application may be considered as a part of the
infrastructure associated with the server.
[1002] The server may provide an interface to other devices
including, without limitation, clients, other servers, printers,
database servers, print servers, file servers, communication
servers, distributed servers, social networks, and the like.
Additionally, this coupling and/or connection may facilitate remote
execution of program across the network. The networking of some or
all of these devices may facilitate parallel processing of a
program or method at one or more locations without deviating from
the scope of the disclosure. In addition, any of the devices
attached to the server through an interface may include at least
one storage medium capable of storing methods, programs, code,
and/or instructions. A central repository may provide program
instructions to be executed on different devices. In this
implementation, the remote repository may act as a storage medium
for program code, instructions, and programs.
[1003] The software program may be associated with a client that
may include a file client, print client, domain client, internet
client, intranet client, and other variants such as secondary
client, host client, distributed client, and the like. The client
may include one or more of memories, processors, computer readable
transitory and/or non-transitory media, storage media, ports
(physical and virtual), communication devices, and interfaces
capable of accessing other clients, servers, machines, and devices
through a wired or a wireless medium, and the like. The methods,
programs, or codes as described herein and elsewhere may be
executed by the client. In addition, other devices required for
execution of methods as described in this application may be
considered as a part of the infrastructure associated with the
client.
[1004] The client may provide an interface to other devices
including, without limitation, servers, other clients, printers,
database servers, print servers, file servers, communication
servers, distributed servers, and the like. Additionally, this
coupling and/or connection may facilitate remote execution of a
program across the network. The networking of some or all of these
devices may facilitate parallel processing of a program or method
at one or more location without deviating from the scope of the
disclosure. In addition, any of the devices attached to the client
through an interface may include at least one storage medium
capable of storing methods, programs, applications, code, and/or
instructions. A central repository may provide program instructions
to be executed on different devices. In this implementation, the
remote repository may act as a storage medium for program code,
instructions, and programs.
[1005] In embodiments, one or more of the controllers, circuits,
systems, data collectors, storage systems, network elements,
components, or the like as described throughout this disclosure may
be embodied in or on an integrated circuit, such as an analog,
digital, or mixed signal circuit, such as a microprocessor, a
programmable logic controller, an application-specific integrated
circuit, a field programmable gate array, or other circuit, such as
embodied on one or more chips disposed on one or more circuit
boards, such as to provide in hardware (with potentially
accelerated speed, energy performance, input-output performance, or
the like) one or more of the functions described herein. This may
include setting up circuits with up to billions of logic gates,
flip-flops, multiplexers, and other circuits in a small space,
facilitating high speed processing, low power dissipation, and
reduced manufacturing cost compared with board-level integration.
In embodiments, a digital IC, typically a microprocessor, digital
signal processor, microcontroller, or the like may use Boolean
algebra to process digital signals to embody complex logic, such as
involved in the circuits, controllers, and other systems described
herein. In embodiments, a data collector, an expert system, a
storage system, or the like may be embodied as a digital integrated
circuit ("IC"), such as a logic IC, memory chip, interface IC
(e.g., a level shifter, a serializer, a deserializer, and the
like), a power management IC and/or a programmable device; an
analog integrated circuit, such as a linear IC, RF IC, or the like,
or a mixed signal IC, such as a data acquisition IC (including A/D
converters, D/A converter, digital potentiometers) and/or a
clock/timing IC.
[1006] The methods and systems described herein may be deployed in
part or in whole through network infrastructures. The network
infrastructure may include elements such as computing devices,
servers, routers, hubs, firewalls, clients, personal computers,
communication devices, routing devices and other active and passive
devices, modules and/or components as known in the art. The
computing and/or non-computing device(s) associated with the
network infrastructure may include, apart from other components, a
storage medium such as flash memory, buffer, stack, RAM, ROM, and
the like. The processes, methods, program codes, instructions
described herein and elsewhere may be executed by one or more of
the network infrastructural elements. The methods and systems
described herein may be configured for use with any kind of
private, community, or hybrid cloud computing network or cloud
computing environment, including those which involve features of
software as a service ("SaaS"), platform as a service ("PaaS"),
and/or infrastructure as a service ("IaaS").
[1007] The methods, program codes, and instructions described
herein and elsewhere may be implemented on a cellular network
having multiple cells. The cellular network may either be frequency
division multiple access ("FDMA") network or code division multiple
access ("CDMA") network. The cellular network may include mobile
devices, cell sites, base stations, repeaters, antennas, towers,
and the like. The cell network may be a GSM, GPRS, 3G, EVDO, mesh,
or other networks types.
[1008] The methods, program codes, and instructions described
herein and elsewhere may be implemented on or through mobile
devices. The mobile devices may include navigation devices, cell
phones, mobile phones, mobile personal digital assistants, laptops,
palmtops, netbooks, pagers, electronic books readers, music players
and the like. These devices may include, apart from other
components, a storage medium such as a flash memory, buffer, RAM,
ROM and one or more computing devices. The computing devices
associated with mobile devices may be enabled to execute program
codes, methods, and instructions stored thereon. Alternatively, the
mobile devices may be configured to execute instructions in
collaboration with other devices. The mobile devices may
communicate with base stations interfaced with servers and
configured to execute program codes. The mobile devices may
communicate on a peer-to-peer network, mesh network, or other
communications network. The program code may be stored on the
storage medium associated with the server and executed by a
computing device embedded within the server. The base station may
include a computing device and a storage medium. The storage device
may store program codes and instructions executed by the computing
devices associated with the base station.
[1009] The computer software, program codes, and/or instructions
may be stored and/or accessed on machine readable transitory and/or
non-transitory media that may include: computer components,
devices, and recording media that retain digital data used for
computing for some interval of time; semiconductor storage known as
random access memory ("RAM"); mass storage typically for more
permanent storage, such as optical discs, forms of magnetic storage
like hard disks, tapes, drums, cards and other types; processor
registers, cache memory, volatile memory, non-volatile memory;
optical storage such as CD, DVD; removable media such as flash
memory (e.g., USB sticks or keys), floppy disks, magnetic tape,
paper tape, punch cards, standalone RAM disks, zip drives,
removable mass storage, off-line, and the like; other computer
memory such as dynamic memory, static memory, read/write storage,
mutable storage, read only, random access, sequential access,
location addressable, file addressable, content addressable,
network attached storage, storage area network, bar codes, magnetic
ink, and the like.
[1010] The methods and systems described herein may transform
physical and/or or intangible items from one state to another. The
methods and systems described herein may also transform data
representing physical and/or intangible items from one state to
another.
[1011] The elements described and depicted herein, including in
flow charts and block diagrams throughout the Figures, imply
logical boundaries between the elements. However, according to
software or hardware engineering practices, the depicted elements
and the functions thereof may be implemented on machines through
computer executable transitory and/or non-transitory media having a
processor capable of executing program instructions stored thereon
as a monolithic software structure, as standalone software modules,
or as modules that employ external routines, code, services, and so
forth, or any combination of these, and all such implementations
may be within the scope of the present disclosure. Examples of such
machines may include, but may not be limited to, personal digital
assistants, laptops, personal computers, mobile phones, other
handheld computing devices, medical equipment, wired or wireless
communication devices, transducers, chips, calculators, satellites,
tablet PCs, electronic books, gadgets, electronic devices, devices
having artificial intelligence, computing devices, networking
equipment, servers, routers, and the like. Furthermore, the
elements depicted in the flow chart and block diagrams or any other
logical component may be implemented on a machine capable of
executing program instructions. Thus, while the foregoing drawings
and descriptions set forth functional aspects of the disclosed
systems, no particular arrangement of software for implementing
these functional aspects should be inferred from these descriptions
unless explicitly stated or otherwise clear from the context.
Similarly, it will be appreciated that the various steps identified
and described above may be varied, and that the order of steps may
be adapted to particular applications of the techniques disclosed
herein. All such variations and modifications are intended to fall
within the scope of this disclosure. As such, the depiction and/or
description of an order for various steps should not be understood
to require a particular order of execution for those steps, unless
required by a particular application, or explicitly stated or
otherwise clear from the context.
[1012] The methods and/or processes described above, and steps
associated therewith, may be realized in hardware, software or any
combination of hardware and software suitable for a particular
application. The hardware may include a general-purpose computer
and/or dedicated computing device or specific computing device or
particular aspect or component of a specific computing device. The
processes may be realized in one or more microprocessors,
microcontrollers, embedded microcontrollers, programmable digital
signal processors or other programmable device, along with internal
and/or external memory. The processes may also, or instead, be
embodied in an application specific integrated circuit, a
programmable gate array, programmable array logic, or any other
device or combination of devices that may be configured to process
electronic signals. It will further be appreciated that one or more
of the processes may be realized as a computer executable code
capable of being executed on a machine-readable medium.
[1013] The computer executable code may be created using a
structured programming language such as C, an object oriented
programming language such as C++, or any other high-level or
low-level programming language (including assembly languages,
hardware description languages, and database programming languages
and technologies) that may be stored, compiled or interpreted to
run on one of the above devices, as well as heterogeneous
combinations of processors, processor architectures, or
combinations of different hardware and software, or any other
machine capable of executing program instructions.
[1014] Thus, in one aspect, methods described above and
combinations thereof may be embodied in computer executable code
that, when executing on one or more computing devices, performs the
steps thereof. In another aspect, the methods may be embodied in
systems that perform the steps thereof, and may be distributed
across devices in a number of ways, or all of the functionality may
be integrated into a dedicated, standalone device or other
hardware. In another aspect, the means for performing the steps
associated with the processes described above may include any of
the hardware and/or software described above. All such permutations
and combinations are intended to fall within the scope of the
present disclosure.
[1015] While the disclosure has been disclosed in connection with
the preferred embodiments shown and described in detail, various
modifications and improvements thereon will become readily apparent
to those skilled in the art. Accordingly, the spirit and scope of
the present disclosure is not to be limited by the foregoing
examples, but is to be understood in the broadest sense allowable
by law.
[1016] The use of the terms "a" and "an" and "the" and similar
referents in the context of describing the disclosure (especially
in the context of the following claims) is to be construed to cover
both the singular and the plural, unless otherwise indicated herein
or clearly contradicted by context. The terms "comprising,"
"having," "including," and "containing" are to be construed as
open-ended terms (i.e., meaning "including, but not limited to,")
unless otherwise noted. Recitation of ranges of values herein are
merely intended to serve as a shorthand method of referring
individually to each separate value falling within the range,
unless otherwise indicated herein, and each separate value is
incorporated into the specification as if it were individually
recited herein. All methods described herein can be performed in
any suitable order unless otherwise indicated herein or otherwise
clearly contradicted by context. The use of any and all examples,
or exemplary language (e.g., "such as") provided herein, is
intended merely to better illuminate the disclosure, and does not
pose a limitation on the scope of the disclosure unless otherwise
claimed. No language in the specification should be construed as
indicating any non-claimed element as essential to the practice of
the disclosure.
[1017] Implementations of approaches described above may include
software implementations, which use software instructions stored on
non-transitory machine-readable media. The procedures and protocols
as described above in the text and figures are sufficient for one
skilled in the art to implement them in such software
implementations. In some examples, the software may execute on a
client node (e.g., a smartphone) using a general-purpose processor
that implements a variety of functions on the client node. Software
that executes on end nodes or intermediate network nodes may use
processors that are dedicated to processing network traffic, for
example, being embedded in network processing devices. In some
implementations, certain functions may be implemented in hardware,
for example, using Application-Specific Integrated Circuits
(ASICs), and/or Field Programmable Gate Arrays (FPGAs), thereby
reducing the load on a general purpose processor.
[1018] Note that in some diagrams and figures in this disclosure,
networks such as the internet, carrier networks, internet service
provider networks, local area networks (LANs), metro area networks
(MANs), wide area networks (WANs), storage area networks (SANs),
backhaul networks, cellular networks, satellite networks and the
like, may be depicted as clouds. Also note, that certain processes
may be referred to as taking place in the cloud and devices may be
described as accessing the cloud. In these types of descriptions,
the cloud should be understood to be some type of network
comprising networking equipment and wireless and/or wired links
[1019] The description above may refer to a client device
communicating with a server, but it should be understood that the
technology and techniques described herein are not limited to those
exemplary devices as the end-points of communication connections or
sessions. The end-points may also be referred to as, or may be,
senders, transmitters, transceivers, receivers, servers, video
servers, content servers, proxy servers, cloud storage units,
caches, routers, switches, buffers, mobile devices, tablets, smart
phones, handsets, computers, set-top boxes, modems, gaming systems,
nodes, satellites, base stations, gateways, satellite ground
stations, wireless access points, and the like. The devices at any
of the end-points or intermediate nodes of communication
connections or sessions may be commercial media streaming boxes
such as those implementing Apple TV, Roku, Chromecast, Amazon Fire,
Slingbox, and the like, or they may be custom media streaming
boxes. The devices at the any of the end-points or intermediate
nodes of communication connections or sessions may be smart
televisions and/or displays, smart appliances such as hubs,
refrigerators, security systems, power panels and the like, smart
vehicles such as cars, boats, busses, trains, planes, carts, and
the like, and may be any device on the Internet of Things (IoT).
The devices at any of the end-points or intermediate nodes of
communication connections or sessions may be single-board computers
and/or purpose built computing engines comprising processors such
as ARM processors, video processors, system-on-a-chip (SoC), and/or
memory such as random access memory (RAM), read only memory (ROM),
or any kind of electronic memory components.
[1020] Communication connections or sessions may exist between two
routers, two clients, two network nodes, two servers, two mobile
devices, and the like, or any combination of potential nodes and/or
end-point devices. In many cases, communication sessions are
bi-directional so that both end-point devices may have the ability
to send and receive data. While these variations may not be stated
explicitly in every description and exemplary embodiment in this
disclosure, it should be understood that the technology and
techniques we describe herein are intended to be applied to all
types of known end-devices, network nodes and equipment and
transmission links, as well as to future end-devices, network nodes
and equipment and transmission links with similar or improved
performance.
[1021] The methods and systems described herein may be deployed in
part or in whole through a machine that executes computer software,
program codes, and/or instructions on a processor. The present
disclosure may be implemented as a method on the machine, as a
system or apparatus as part of or in relation to the machine, or as
a computer program product embodied in a computer readable medium
executing on one or more of the machines. In embodiments, the
processor may be part of a server, cloud server, client, network
infrastructure, mobile computing platform, stationary computing
platform, or other computing platforms. A processor may be any kind
of computational or processing device capable of executing program
instructions, codes, binary instructions, and the like. The
processor may be or may include a signal processor, digital
processor, embedded processor, microprocessor, or any variant such
as a co-processor (math co-processor, graphic co-processor,
communication co-processor, and the like) and the like that may
directly or indirectly facilitate execution of program code or
program instructions stored thereon. In addition, the processor may
enable execution of multiple programs, threads, and codes. The
threads may be executed simultaneously to enhance the performance
of the processor and to facilitate simultaneous operations of the
application. By way of implementation, methods, program codes,
program instructions and the like described herein may be
implemented in one or more thread. The thread may spawn other
threads that may have assigned priorities associated with them; the
processor may execute these threads based on priority or any other
order based on instructions provided in the program code. The
processor, or any machine utilizing one, may include non-transitory
memory that stores methods, codes, instructions, and programs as
described herein and elsewhere. The processor may access a
non-transitory storage medium through an interface that may store
methods, codes, and instructions as described herein and elsewhere.
The storage medium associated with the processor for storing
methods, programs, codes, program instructions or other type of
instructions capable of being executed by the computing or
processing device may include but may not be limited to one or more
of a CD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache,
and the like.
[1022] A processor may include one or more cores that may enhance
speed and performance of a multiprocessor. In embodiments, the
process may be a dual core processor, quad core processors, other
chip-level multiprocessor and the like that combine two or more
independent cores (called a die).
[1023] The methods and systems described herein may be deployed in
part or in whole through a machine that executes computer software
on a server, client, firewall, gateway, hub, router, or other such
computer and/or networking hardware. The software program may be
associated with a server that may include a file server, print
server, domain server, internet server, intranet server, cloud
server, and other variants such as secondary server, host server,
distributed server, and the like. The server may include one or
more of memories, processors, computer readable transitory and/or
non-transitory media, storage media, ports (physical and virtual),
communication devices, and interfaces capable of accessing other
servers, clients, machines, and devices through a wired or a
wireless medium, and the like. The methods, programs, or codes as
described herein and elsewhere may be executed by the server. In
addition, other devices required for execution of methods as
described in this application may be considered as a part of the
infrastructure associated with the server.
[1024] The server may provide an interface to other devices
including, without limitation, clients, other servers, printers,
database servers, print servers, file servers, communication
servers, distributed servers, social networks, and the like.
Additionally, this coupling and/or connection may facilitate remote
execution of program across the network. The networking of some or
all of these devices may facilitate parallel processing of a
program or method at one or more location without deviating from
the scope of the disclosure. In addition, any of the devices
attached to the server through an interface may include at least
one storage medium capable of storing methods, programs, code
and/or instructions. A central repository may provide program
instructions to be executed on different devices. In this
implementation, the remote repository may act as a storage medium
for program code, instructions, and programs.
[1025] The software program may be associated with a client that
may include a file client, print client, domain client, internet
client, intranet client and other variants such as secondary
client, host client, distributed client, and the like. The client
may include one or more of memories, processors, computer readable
transitory and/or non-transitory media, storage media, ports
(physical and virtual), communication devices, and interfaces
capable of accessing other clients, servers, machines, and devices
through a wired or a wireless medium, and the like. The methods,
programs, or codes as described herein and elsewhere may be
executed by the client. In addition, other devices required for
execution of methods as described in this application may be
considered as a part of the infrastructure associated with the
client.
[1026] The client may provide an interface to other devices
including, without limitation, servers, other clients, printers,
database servers, print servers, file servers, communication
servers, distributed servers, and the like. Additionally, this
coupling and/or connection may facilitate remote execution of
program across the network. The networking of some or all of these
devices may facilitate parallel processing of a program or method
at one or more location without deviating from the scope of the
disclosure. In addition, any of the devices attached to the client
through an interface may include at least one storage medium
capable of storing methods, programs, applications, code and/or
instructions. A central repository may provide program instructions
to be executed on different devices. In this implementation, the
remote repository may act as a storage medium for program code,
instructions, and programs.
[1027] The methods and systems described herein may be deployed in
part or in whole through network infrastructures. The network
infrastructure may include elements such as computing devices,
servers, routers, hubs, firewalls, clients, personal computers,
communication devices, routing devices and other active and passive
devices, modules and/or components as known in the art. The
computing and/or non-computing device(s) associated with the
network infrastructure may include, apart from other components, a
storage medium such as flash memory, buffer, stack, RAM, ROM, and
the like. The processes, methods, program codes, instructions
described herein and elsewhere may be executed by one or more of
the network infrastructural elements. The methods and systems
described herein may be adapted for use with any kind of private,
community, or hybrid cloud computing network or cloud computing
environment, including those which involve features of software as
a service ("SaaS"), platform as a service ("PaaS"), and/or
infrastructure as a service ("IaaS").
[1028] The methods, program codes, and instructions described
herein and elsewhere may be implemented on a cellular network
having multiple cells. The cellular network may either be frequency
division multiple access ("FDMA") network or code division multiple
access ("CDMA") network. The cellular network may include mobile
devices, cell sites, base stations, repeaters, antennas, towers,
and the like. The cell network may be a GSM, GPRS, 3G, EVDO, mesh,
or other networks types.
[1029] The methods, program codes, and instructions described
herein and elsewhere may be implemented on or through mobile
devices. The mobile devices may include navigation devices, cell
phones, mobile phones, mobile personal digital assistants, laptops,
palmtops, netbooks, pagers, electronic books readers, music players
and the like. These devices may include, apart from other
components, a storage medium such as a flash memory, buffer, RAM,
ROM and one or more computing devices. The computing devices
associated with mobile devices may be enabled to execute program
codes, methods, and instructions stored thereon. Alternatively, the
mobile devices may be configured to execute instructions in
collaboration with other devices. The mobile devices may
communicate with base stations interfaced with servers and
configured to execute program codes. The mobile devices may
communicate on a peer-to-peer network, mesh network, or other
communications network. The program code may be stored on the
storage medium associated with the server and executed by a
computing device embedded within the server. The base station may
include a computing device and a storage medium. The storage device
may store program codes and instructions executed by the computing
devices associated with the base station.
[1030] The computer software, program codes, and/or instructions
may be stored and/or accessed on machine readable transitory and/or
non-transitory media that may include: computer components,
devices, and recording media that retain digital data used for
computing for some interval of time; semiconductor storage known as
random access memory ("RAM"); mass storage typically for more
permanent storage, such as optical discs, forms of magnetic storage
like hard disks, tapes, drums, cards and other types; processor
registers, cache memory, volatile memory, non-volatile memory;
optical storage such as CD, DVD; removable media such as flash
memory (e.g. USB sticks or keys), floppy disks, magnetic tape,
paper tape, punch cards, standalone RAM disks, zip drives,
removable mass storage, off-line, and the like; other computer
memory such as dynamic memory, static memory, read/write storage,
mutable storage, read only, random access, sequential access,
location addressable, file addressable, content addressable,
network attached storage, storage area network, bar codes, magnetic
ink, and the like.
[1031] The methods and systems described herein may transform
physical and/or intangible items from one state to another. The
methods and systems described herein may also transform data
representing physical and/or intangible items from one state to
another.
[1032] The elements described and depicted herein, including in
flow charts and block diagrams throughout the figures, imply
logical boundaries between the elements. However, according to
software or hardware engineering practices, the depicted elements
and the functions thereof may be implemented on machines through
computer executable transitory and/or non-transitory media having a
processor capable of executing program instructions stored thereon
as a monolithic software structure, as standalone software modules,
or as modules that employ external routines, code, services, and so
forth, or any combination of these, and all such implementations
may be within the scope of the present disclosure. Examples of such
machines may include, but may not be limited to, personal digital
assistants, laptops, personal computers, mobile phones, other
handheld computing devices, medical equipment, wired or wireless
communication devices, transducers, chips, calculators, satellites,
tablet PCs, electronic books, gadgets, electronic devices, devices
having artificial intelligence, computing devices, networking
equipment, servers, routers, and the like. Furthermore, the
elements depicted in the flow chart and block diagrams or any other
logical component may be implemented on a machine capable of
executing program instructions. Thus, while the foregoing drawings
and descriptions set forth functional aspects of the disclosed
systems, no particular arrangement of software for implementing
these functional aspects should be inferred from these descriptions
unless explicitly stated or otherwise clear from the context.
Similarly, it will be appreciated that the various steps identified
and described above may be varied and that the order of steps may
be adapted to particular applications of the techniques disclosed
herein. All such variations and modifications are intended to fall
within the scope of this disclosure. As such, the depiction and/or
description of an order for various steps should not be understood
to require a particular order of execution for those steps, unless
required by a particular application, or explicitly stated or
otherwise clear from the context.
[1033] The methods and/or processes described above, and steps
associated therewith, may be realized in hardware, software or any
combination of hardware and software suitable for a particular
application. The hardware may include a general-purpose computer
and/or dedicated computing device or specific computing device or
particular aspect or component of a specific computing device. The
processes may be realized in one or more microprocessors,
microcontrollers, embedded microcontrollers, programmable digital
signal processors or other programmable devices, along with
internal and/or external memory. The processes may also, or
instead, be embodied in an application specific integrated circuit,
a programmable gate array, programmable array logic, or any other
device or combination of devices that may be configured to process
electronic signals. It will further be appreciated that one or more
of the processes may be realized as a computer executable code
capable of being executed on a machine-readable medium.
[1034] The computer executable code may be created using a
structured programming language such as C, an object oriented
programming language such as C++, or any other high-level or
low-level programming language (including assembly languages,
hardware description languages, and database programming languages
and technologies) that may be stored, compiled or interpreted to
run on one of the above devices, as well as heterogeneous
combinations of processors, processor architectures, or
combinations of different hardware and software, or any other
machine capable of executing program instructions.
[1035] Thus, in one aspect, methods described above and
combinations thereof may be embodied in computer executable code
that, when executing on one or more computing devices, performs the
steps thereof. In another aspect, the methods may be embodied in
systems that perform the steps thereof, and may be distributed
across devices in a number of ways, or all of the functionality may
be integrated into a dedicated, standalone device or other
hardware. In another aspect, the means for performing the steps
associated with the processes described above may include any of
the hardware and/or software described above. All such permutations
and combinations are intended to fall within the scope of the
present disclosure.
[1036] While the disclosure has been disclosed in connection with
the preferred embodiments shown and described in detail, various
modifications and improvements thereon will become readily apparent
to those skilled in the art. Accordingly, the spirit and scope of
the present disclosure is not to be limited by the foregoing
examples but is to be understood in the broadest sense allowable by
law.
[1037] The use of the terms "a," "an." and "the" and similar
referents in the context of describing the disclosure (especially
in the context of the following claims) is to be construed to cover
both the singular and the plural unless otherwise indicated herein
or clearly contradicted by context. The terms "comprising,"
"having," "including," and "containing" are to be construed as
open-ended terms (i.e., meaning "including, but not limited to,")
unless otherwise noted. Recitations of ranges of values herein are
merely intended to serve as a shorthand method of referring
individually to each separate value falling within the range,
unless otherwise indicated herein, and each separate value is
incorporated into the specification as if it were individually
recited herein. All methods described herein may be performed in
any suitable order unless otherwise indicated herein or otherwise
clearly contradicted by context. The use of any and all examples,
or exemplary language (e.g., "such as") provided herein, is
intended merely to better illuminate the disclosure and does not
pose a limitation on the scope of the disclosure unless otherwise
claimed. No language in the specification should be construed as
indicating any non-claimed element as essential to the practice of
the disclosure.
[1038] Any element in a claim that does not explicitly state "means
for" performing a specified function, or "step for" performing a
specified function, is not to be interpreted as a "means" or "step"
clause as specified in 35 U.S.C. .sctn. 112(f). In particular, any
use of "step of" in the claims is not intended to invoke the
provision of 35 U. S.C. .sctn. 112(f).
[1039] Persons of ordinary skill in the art may appreciate that
numerous design configurations may be possible to enjoy the
functional benefits of the inventive systems. Thus, given the wide
variety of configurations and arrangements of embodiments of the
present invention the scope of the invention is reflected by the
breadth of the claims below rather than narrowed by the embodiments
described above.
[1040] It is to be understood that the foregoing description is
intended to illustrate and not to limit the scope of the invention,
some aspects of which are defined by the scope of the appended
claims Furthermore, other embodiments are within the scope of the
following claims
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