U.S. patent application number 13/729289 was filed with the patent office on 2014-07-03 for predicting a time of failure of a device.
This patent application is currently assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION. The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Amadou Ba.
Application Number | 20140188405 13/729289 |
Document ID | / |
Family ID | 51018150 |
Filed Date | 2014-07-03 |
United States Patent
Application |
20140188405 |
Kind Code |
A1 |
Ba; Amadou |
July 3, 2014 |
PREDICTING A TIME OF FAILURE OF A DEVICE
Abstract
The present disclosure relates generally to the field of
predicting a time of failure of a device. In various examples,
predicting a time of failure of a device may be implemented in the
form of systems, methods and/or algorithms.
Inventors: |
Ba; Amadou; (Dublin,
IE) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
MACHINES CORPORATION; INTERNATIONAL BUSINESS |
|
|
US |
|
|
Assignee: |
INTERNATIONAL BUSINESS MACHINES
CORPORATION
Armonk
NY
|
Family ID: |
51018150 |
Appl. No.: |
13/729289 |
Filed: |
December 28, 2012 |
Current U.S.
Class: |
702/34 ;
702/182 |
Current CPC
Class: |
G06F 11/22 20130101;
G05B 19/4065 20130101; G06F 11/3058 20130101; G06F 11/3495
20130101; G05B 23/0283 20130101; G06F 11/008 20130101; G06F 17/00
20130101 |
Class at
Publication: |
702/34 ;
702/182 |
International
Class: |
G06F 17/00 20060101
G06F017/00 |
Claims
1-10. (canceled)
11. A computer readable storage medium, tangibly embodying a
program of instructions executable by a device predicting a time of
failure of the device, the program of instructions, when executing,
performing the following steps: receiving by the device at least
one laboratory test result; generating by the device at least one
physical measurement associated with the device; determining by the
device a difference between information associated with the at
least one received laboratory test result and information
associated with the at least one generated physical measurement;
generating by the device a predicted time of failure of the device,
wherein the generation of the predicted time of failure of the
device is based at least in part upon the determined difference
between the information associated with the at least one received
laboratory test result and the information associated with the at
least one generated physical measurement; and outputting by the
device the predicted time of failure of the device.
12. The computer readable storage medium of claim 11, wherein the
at least one laboratory test result is a test result of a
laboratory stress test associated with the device.
13. The computer readable storage medium of claim 11, wherein the
at least one physical measurement is selected from the group
comprising: a temperature measurement; a current measurement; a
voltage measurement; an acceleration measurement; a tension
measurement; a compression measurement; a force measurement; a
magnetic field measurement; and an electro-magnetic field
measurement.
14. The computer readable storage medium of claim 13, wherein the
at least one physical measurement is made in real-time.
15. The computer readable storage medium of claim 11, wherein the
output is made to a display screen associated with the device.
16. The computer readable storage medium of claim 11, wherein the
program of instructions, when executing, further performs the steps
of: receiving by the device a plurality of laboratory test results;
generating by the device a plurality of physical measurements
associated with the device; and determining by the device a
difference between information associated with the plurality of
received laboratory test results and information associated with
the plurality of generated physical measurements; wherein the
generation of the predicted time of failure of the device is based
at least in part upon the determined difference between information
associated with the plurality of received laboratory test results
and information associated with the plurality of generated physical
measurements.
17. The computer readable storage medium of claim 16, wherein: each
of the plurality of laboratory test results is received at
different times; and each of the plurality of physical measurements
is generated at different times.
18. The computer readable storage medium of claim 11, wherein the
device is selected from the group comprising: a smart phone; a
tablet; a laptop computer; a desktop computer; a refrigerator; a
freezer; a stove; a range; a hot water heater; a boiler; a furnace;
and a vehicle mounted device.
19. The computer readable storage medium of claim 11, wherein the
information associated with the at least one generated physical
measurement comprises a difference between the at least one
generated physical measurement and at least one value from a
model.
20. The computer readable storage medium of claim 11, wherein the
information associated with the at least one generated physical
measurement is the at least one generated physical measurement
itself.
21. A system for predicting a time of failure of a device, the
system being integrated into the device, the system comprising: a
receiving element configured to receive at least one laboratory
test result; a first generating element configured to generate at
least one physical measurement associated with the device; a
determining element in operative communication with the receiving
element and the first generating element configured to determine a
difference between information associated with the at least one
received laboratory test result and information associated with the
at least one generated physical measurement; a second generating
element in operative communication with the determining element
configured to generate a predicted time of failure of the device,
wherein the generation of the predicted time of failure of the
device is based at least in part upon the determined difference
between the information associated with the at least one received
laboratory test result and the information associated with the at
least one generated physical measurement; and an outputting element
in operative communication with the second generating element
configured to output the predicted time of failure of the
device.
22. The system of claim 21, wherein the at least one laboratory
test result is a test result of a laboratory stress test associated
with the device.
23. The system of claim 21, wherein the at least one physical
measurement is selected from the group comprising: a temperature
measurement; a current measurement; a voltage measurement; an
acceleration measurement; a tension measurement; a compression
measurement; a force measurement; a magnetic field measurement; and
an electro-magnetic field measurement.
24. The system of claim 23, wherein the at least one physical
measurement is made in real-time.
25. The system of claim 21, wherein the outputting element
comprises a display screen associated with the device.
26. The system of claim 21, wherein: the receiving element is
configured to receive a plurality of laboratory test results; the
first generating element is configured to generate a plurality of
physical measurements associated with the device; and the
determining element is further configured to determine a difference
between information associated with the plurality of received
laboratory test results and information associated with the
plurality of generated physical measurements; wherein the
generation of the predicted time of failure of the device is based
at least in part upon the determined difference between information
associated with the plurality of received laboratory test results
and information associated with the plurality of generated physical
measurements.
27. The system of claim 26, wherein: each of the plurality of
laboratory test results is received at different times; and each of
the plurality of physical measurements is generated at different
times.
28. The system of claim 21, wherein the device is selected from the
group comprising: a smart phone; a tablet; a laptop computer; a
desktop computer; a refrigerator; a freezer; a stove; a range; a
hot water heater; a boiler; a furnace; and a vehicle mounted
device.
29. The system of claim 21, wherein the information associated with
the at least one generated physical measurement comprises a
difference between the at least one generated physical measurement
and at least one value from a model.
30. The system of claim 21, wherein the information associated with
the at least one generated physical measurement is the at least one
generated physical measurement itself.
Description
BACKGROUND
[0001] The present disclosure relates generally to the field of
predicting a time of failure of a device.
[0002] In various examples, predicting a time of failure of a
device may be implemented in the form of systems, methods and/or
algorithms.
DESCRIPTION OF RELATED ART
[0003] Devices are becoming increasingly complex. Various devices
change dynamically in response to their mobility, changing
execution environments and frequency of use. As a result, existing
devices are typically prone to failures.
[0004] Certain existing failure prediction methods are based on,
for example, on-line monitoring that considers both the actual
state and the estimated (modeled) state of a system. Typically,
those methods incorporate measurements of actual system parameters
in order to evaluate the difference between the observed and the
estimated (modeled) state which gives a residual. Then, the
residual is monitored for the purpose of detecting significant
deviation, that is, a failure indicator of the system.
[0005] However, such conventional failure prediction methods using
continuous measurements to evaluate the state of systems are based
on models reflecting the state of systems in their healthy case.
This allows for assessing the presence or the absence of failure,
but does not traditionally provide information about a time of
failure.
[0006] Another conventional approach is based on laboratory
accelerated tests. In this context, several stress tests and
computer based simulations are performed and the failure of
components and devices are consequently assessed.
[0007] During the stress tests the components making up the devices
are subjected to tests in order to assess their performances and as
a result predict their time of failure for a given environment.
This is often used as a threshold to fix the duration of warranties
when the devices are purchased.
[0008] However, a major issue with the conventional failure
prediction methods based on stress tests is the fact that the
stress tests are performed with fixed parameters under controlled
environments. Those particular situations do not necessarily
reflect the conditions in which the devices will operate; therefore
the outcome of the stress tests is typically insufficient in
assessing the actual status of devices in use (particularly when
the devices are used in environments which are different from those
in which the stress tests were conducted (that is, the change in
operating condition adversely affects the predicted time of
failure)).
SUMMARY
[0009] The present disclosure relates generally to the field of
predicting a time of failure of a device.
[0010] In various examples, predicting a time of failure of a
device may be implemented in the form of systems, methods and/or
algorithms.
[0011] In one embodiment, a method implemented in a device for
predicting a time of failure of the device is provided, the method
comprising: receiving by the device at least one laboratory test
result; generating by the device at least one physical measurement
associated with the device; determining by the device a difference
between information associated with the at least one received
laboratory test result and information associated with the at least
one generated physical measurement; generating by the device a
predicted time of failure of the device, wherein the generation of
the predicted time of failure of the device is based at least in
part upon the determined difference between the information
associated with the at least one received laboratory test result
and the information associated with the at least one generated
physical measurement; and outputting by the device the predicted
time of failure of the device.
[0012] In another embodiment, a computer readable storage medium,
tangibly embodying a program of instructions executable by a device
predicting a time of failure of the device is provided, the program
of instructions, when executing, performing the following steps:
receiving by the device at least one laboratory test result;
generating by the device at least one physical measurement
associated with the device; determining by the device a difference
between information associated with the at least one received
laboratory test result and information associated with the at least
one generated physical measurement; generating by the device a
predicted time of failure of the device, wherein the generation of
the predicted time of failure of the device is based at least in
part upon the determined difference between the information
associated with the at least one received laboratory test result
and the information associated with the at least one generated
physical measurement; and outputting by the device the predicted
time of failure of the device.
[0013] In another embodiment, a system for predicting a time of
failure of a device, the system being integrated into the device is
provided, the system comprising: a receiving element configured to
receive at least one laboratory test result; a first generating
element configured to generate at least one physical measurement
associated with the device; a determining element in operative
communication with the receiving element and the first generating
element configured to determine a difference between information
associated with the at least one received laboratory test result
and information associated with the at least one generated physical
measurement; a second generating element in operative communication
with the determining element configured to generate a predicted
time of failure of the device, wherein the generation of the
predicted time of failure of the device is based at least in part
upon the determined difference between the information associated
with the at least one received laboratory test result and the
information associated with the at least one generated physical
measurement; and an outputting element in operative communication
with the second generating element configured to output the
predicted time of failure of the device.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] Various objects, features and advantages of the present
invention will become apparent to one skilled in the art, in view
of the following detailed description taken in combination with the
attached drawings, in which:
[0015] FIG. 1 depicts a flowchart of a method according to an
embodiment of the present invention.
[0016] FIG. 2 depicts a block diagram of a system according to an
embodiment of the present invention.
[0017] FIG. 3 depicts a flowchart of a method according to an
embodiment of the present invention.
[0018] FIG. 4 depicts a flowchart of a method according to an
embodiment of the present invention.
[0019] FIG. 5 depicts a block diagram of a system according to an
embodiment of the present invention.
DETAILED DESCRIPTION
[0020] For the purposes of description, the term "real-time" is
intended to refer to cause and effect occurring approximately
contemporaneously in time (e.g., without significant time lag
between cause and effect but not necessarily instantaneously).
[0021] For the purposes of description, the term "sensor" is
intended to refer to a mechanism for acquiring one or more
real-time measurements of one or more physical parameters. In
various examples, a sensor may acquire measurements for
temperature, current, voltage, acceleration, tension and/or
electro-magnetic field.
[0022] For the purposes of description, the term "device" is
intended to refer to any mechanism operating while using physical
components (e.g., electrical, electronic, acoustic, hydraulic
and/or mechanical components). Various examples of a "device"
include (but are not limited to) the following: a smart phone (a
mobile phone, a cellular phone); a tablet; a laptop computer; a
desktop computer; a computer system, a household appliance (e.g., a
refrigerator; a freezer; a washing machine, a dryer, a stove; a
range; a hot water heater; a boiler; a furnace), a large commercial
or industrial system.
[0023] For the purposes of description, the term "subsystem" is
intended to refer to any one or more physical elements that form
part of a larger physical system (which is comprised of a group of
two or more independent but related subsystems).
[0024] For the purposes of description, the term "physical system"
is intended to refer to a group of two or more independent but
related subsystems.
[0025] For the purposes of description, the term "model" is
intended to refer to a representation of a physical system or
subsystem(s) as defined by one or more mathematical equations
and/or as represented in one or more databases.
[0026] For the purposes of description, the term "stress test" is
intended to refer to collecting one or more measurements related to
a given subsystem when the subsystem is operated beyond normal
operating conditions to a breaking point. This process permits
gradual monitoring of performance degradation of the given
subsystem to establish one or more thresholds (e.g., maximum life
duration of the given subsystem).
[0027] For the purposes of description, the term "time of failure"
is intended to refer to a specific time (e.g., a specific hour, a
specific minute and/or a specific second) and/or a specific date
(e.g., a specific year, a specific month and/or a specific day). In
one example, time of failure may be provided as a specific time in
the future (e.g., Jan. 1, 2018). In another example, time of
failure may be provided as an offset from a current time (e.g., 22
days from now).
[0028] As described herein, mechanisms are provided for predicting
and displaying a time of failure of a device by using a set of
measurements. The failure prediction obtained from a processing
system that establishes the diagnosis and the prognosis may be
transferred to a display system that displays the predicted time of
failure. In the case where the device does not have a display
system, a display system may be added to those devices.
[0029] In another example, mechanisms may be provided to allow
frequent updating of the predicted time of failure of the device
and notifying the user of the outcome and the reasons for the
failure by indicating the component(s) predicted as leading to the
failure.
[0030] The devices may be embedded with sensors recording their
actual dynamics. Also used in the prediction may be measurement
data obtained from laboratory stress test(s). The prediction of the
time of failure may be performed also by using various models and
methods. Each model may be a physical based model and/or a data
based model. The model selection may depend on the most appropriate
one in representing the features of the system in question.
Whatever the selected modeling approach, the complexity of the
device and its highly integrated nature may require a
multi-modeling method--where a device will be considered as a
combination of different subsystems, and each subsystem behaves in
accordance with a specific dynamic, and the overall model of a
given device is the combination of each model of a subsystem.
[0031] Referring now to FIG. 1, a method implemented in a device,
wherein the method is for predicting a time of failure of the
device, is shown. As seen in this FIG. 1, the method may comprise:
at step 101--receiving by the device at least one laboratory test
result; step 103--generating by the device at least one physical
measurement associated with the device; step 105--determining by
the device a difference between information associated with the at
least one received laboratory test result and information
associated with the at least one generated physical measurement;
step 107--generating by the device a predicted time of failure of
the device, wherein the generation of the predicted time of failure
of the device is based at least in part upon the determined
difference between the information associated with the at least one
received laboratory test result and the information associated with
the at least one generated physical measurement; and step
109--outputting by the device the predicted time of failure of the
device.
[0032] In one example, any steps may be carried out in the order
recited or the steps may be carried out in another order.
[0033] Still referring to FIG. 1, any communications (e.g.,
receiving by the device at least one laboratory test result) may be
carried out via a network. Such a network may comprise the
Internet, an intranet, a local area network, a wide area network
and/or any other desired communication channel(s).
[0034] Referring now to FIG. 2, a system 201 for predicting a time
of failure of a device 200 (the system being integrated in a
centralized manner into the device) is shown. As seen in this FIG.
2, the system 201 may comprise: a receiving element 203 configured
to receive at least one laboratory test result; a first generating
element 205 configured to generate at least one physical
measurement associated with the device; a determining element 207
in operative communication with the receiving element and the first
generating element configured to determine a difference between
information associated with the at least one received laboratory
test result and information associated with the at least one
generated physical measurement; a second generating element 209 in
operative communication with the determining element configured to
generate a predicted time of failure of the device, wherein the
generation of the predicted time of failure of the device is based
at least in part upon the determined difference between the
information associated with the at least one received laboratory
test result and the information associated with the at least one
generated physical measurement; and an outputting element 211 in
operative communication with the second generating element
configured to output the predicted time of failure of the device
(in various examples, the outputting element may comprise a visual
outputting element (such as a display screen or the like) and/or an
audio outputting element (such as a speaker or the like).
[0035] In one example, any steps may be carried out in the order
recited or the steps may be carried out in another order.
[0036] Still referring to FIG. 2, any communications (e.g.,
receiving by the receiving element at least one laboratory test
result) may be carried out via a network. Such a network may
comprise the Internet, an intranet, a local area network, a wide
area network and/or any other desired communication channel(s). In
another example, some or all of the elements of FIG. 2 may be
implemented in a computer system of the type shown in FIG. 5.
Further, any element may be in operative communication with any
other element(s).
[0037] Referring now to FIG. 3, a flowchart of another example
method for predicting a time of failure of a device is shown. In
one example, these steps may be applied to each of a plurality of
subsystems within the device and then the results aggregated and
analyzed as described below. In any case, at step 300 one or more
measurements are collected during one or more stress tests (e.g.,
laboratory stress tests of a given device type and/or field stress
tests of a given device type). Further, at step 301 a model for the
given subsystem is elaborated, that is, defined (the model may be
based on one or more mathematical equations and/or one or more
databases). In addition, at step 303, measurement(s) from one or
more sensors embedded in the device are collected (in various
examples, one or more sensors embedded in the device may acquire
measurements for temperature, current, voltage, acceleration,
tension and/or electro-magnetic field).
[0038] Still referring to FIG. 3, at step 305 for each measurement
a corresponding residual is computed. In this context, a residual
is a difference between a measurement (e.g., collected from an
embedded sensor) and its equivalent calculated from an elaborated
model. At step 307 a determination is made whether a given residual
differs from zero (in this example, the value of a residual is a
fault indicator such that when the residual is equal to zero there
is no fault and when the residual is not equal to zero there is a
fault). In the case of no residuals being different from zero,
processing may loop back to step 305. In the case of at least one
residual being different from zero, processing may proceed to step
309.
[0039] Still referring to FIG. 3, at step 309 a difference between
each residual and a counterpart recorded during the stress test(s)
is computed (e.g., real-time acceleration measurements at the
device vs. counterpart stress test acceleration measurements). In
this example, the amplitude of the difference provides a failure
prediction for the subsystem (and by extension, the device). In one
specific example, when the difference is equal to zero, this
corresponds to the actual time when the subsystem breaks down. At
step 311 an output (e.g., visual output on a display and/or audio
output via a speaker) of the predicted time of failure is made
(wherein the prediction is based, for example, on the magnitude of
the difference computed at step 309).
[0040] Referring now to FIG. 4, a block diagram according to one
embodiment depicting how results corresponding to each of a
plurality of subsystems are aggregated to produce a global
prediction of the time of failure of a device is shown. As seen in
this FIG. 4, in one example, a predicted time of failure for
subsystem 1 of a device (see block 401), a predicted time of
failure for subsystem 2 of the device (see block 403) and a
predicted time of failure for subsystem n (wherein n is any desired
integer) of a device (see block 405) are aggregated to determine a
predicted time of failure for the device (see block 407). In one
specific example, the predicted time of failure for the device will
be the earliest time of failure of any of the subsystems of the
device.
[0041] Reference will now be made to an example prediction of a
time of failure. In this example, the first step is to use Kalman
filtering to estimate the state and to compute residuals. We
consider the case where the model of each sub-system can be
represented under a state space:
{ x k = F k x k - 1 + G k u k + w k z k = H k x k + v k
##EQU00001##
[0042] where:
[0043] k is the discrete time instant
[0044] x.sub.k is the state of the sub-system
[0045] F.sub.k is the state transition matrix
[0046] G.sub.k is the control-input matrix, applied to the control
vector u.sub.k
[0047] w.sub.k is the sub-system Gaussian noise
[0048] v.sub.k is the sensors Gaussian noise
[0049] H.sub.k is the information matrix
[0050] z.sub.k is the sensors measurements
[0051] In this example, a linear Kalman filter can be used, however
based on the structure of the models various extensions of the
Kalman filter can be used as well. An example of the use of Kalman
filter for the time of failure prediction can be to determine first
the state estimate and the covariance estimate a priori, then
update the different variables:
{circumflex over (x)}.sub.k|k-1=F.sub.k{circumflex over
(x)}.sub.k-1|k-1+G.sub.k-1u.sub.k-1
P.sub.k|k-1=F.sub.kP.sub.k-1|k-1F.sub.k.sup.T+Q.sub.k
[0052] Then the update of the parameters is achieved as
follows:
{tilde over (y)}.sub.k=z.sub.k-H.sub.k{circumflex over
(x)}.sub.k|k-1
S.sub.k=H.sub.kP.sub.k|k-1H.sub.k.sup.T.+-.R.sub.k
K.sub.k=P.sub.k|k-1H.sub.k.sup.TS.sub.k.sup.-1
{circumflex over (x)}.sub.k|k={circumflex over
(x)}.sub.k|k-1+K.sub.k{tilde over (y)}.sub.k
P.sub.k|k=(I-K.sub.kH.sub.k)P.sub.k|k-1
[0053] where:
[0054] S.sub.k is the innovation
[0055] K.sub.k is the kalman gain
[0056] x.sub.k is the state estimate
[0057] P.sub.k is the covariance estimate
[0058] In the classical Kalman filter settings the residual is
given by
{tilde over (y)}.sub.k=z.sub.k-H.sub.k{circumflex over
(x)}.sub.k|k-1
[0059] This equation allows the determination of the prediction of
the time of failure Tof.sub.k by computing the difference between
the residual {tilde over (y)}.sub.k and the threshold .sigma.
obtained during the stress test.
Tof.sub.k={tilde over (y)}.sub.k-.sigma.
[0060] The same approach can be applied by using an extension of
the Kalman filter, such as Extended Kalman filter, unscented Kalman
Filter, particle filter and so on, or by using spectral analysis
methods or data-based methods.
[0061] Referring now to FIG. 5, this figure shows a hardware
configuration of computing system 500 according to an embodiment of
the present invention. As seen, this hardware configuration has at
least one processor or central processing unit (CPU) 511. The CPUs
511 are interconnected via a system bus 512 to a random access
memory (RAM) 514, read-only memory (ROM) 516, input/output (I/O)
adapter 518 (for connecting peripheral devices such as disk units
521 and tape drives 540 to the bus 512), user interface adapter 522
(for connecting a keyboard 524, mouse 526, speaker 528, microphone
532, and/or other user interface device to the bus 512), a
communications adapter 534 for connecting the system 500 to a data
processing network, the Internet, an Intranet, a local area network
(LAN), etc., and a display adapter 536 for connecting the bus 512
to a display device 538 and/or printer 539 (e.g., a digital printer
or the like).
[0062] In another embodiment, a method implemented in a device for
predicting a time of failure of the device is provided, the method
comprising: receiving by the device at least one laboratory test
result; generating by the device at least one physical measurement
associated with the device; determining by the device a difference
between information associated with the at least one received
laboratory test result and information associated with the at least
one generated physical measurement; generating by the device a
predicted time of failure of the device, wherein the generation of
the predicted time of failure of the device is based at least in
part upon the determined difference between the information
associated with the at least one received laboratory test result
and the information associated with the at least one generated
physical measurement; and outputting by the device the predicted
time of failure of the device.
[0063] In one example, the at least one laboratory test result is a
test result of a laboratory stress test associated with the
device.
[0064] In another example, the at least one physical measurement is
selected from the group comprising: a temperature measurement; a
current measurement; a voltage measurement; an acceleration
measurement; a tension measurement; a compression measurement; a
force measurement; a magnetic field measurement; and an
electro-magnetic field measurement.
[0065] In another example, the at least one physical measurement is
made in real-time.
[0066] In another example, the output is made to a display screen
associated with the device.
[0067] In another example, the method further comprises: receiving
by the device a plurality of laboratory test results; generating by
the device a plurality of physical measurements associated with the
device; and determining by the device a difference between
information associated with the plurality of received laboratory
test results and information associated with the plurality of
generated physical measurements; wherein the generation of the
predicted time of failure of the device is based at least in part
upon the determined difference between information associated with
the plurality of received laboratory test results and information
associated with the plurality of generated physical
measurements.
[0068] In another example: each of the plurality of laboratory test
results is received at different times; and each of the plurality
of physical measurements is generated at different times.
[0069] In another example, the device is selected from the group
comprising: a smart phone; a tablet; a laptop computer; a desktop
computer; a refrigerator; a freezer; a stove; a range; a hot water
heater; a boiler; a furnace; and a vehicle mounted device.
[0070] In another example, the information associated with the at
least one generated physical measurement comprises a difference
between the at least one generated physical measurement and at
least one value from a model.
[0071] In another example, the information associated with the at
least one generated physical measurement is the at least one
generated physical measurement itself.
[0072] In another embodiment, a computer readable storage medium,
tangibly embodying a program of instructions executable by a device
predicting a time of failure of the device is provided, the program
of instructions, when executing, performing the following steps:
receiving by the device at least one laboratory test result;
generating by the device at least one physical measurement
associated with the device; determining by the device a difference
between information associated with the at least one received
laboratory test result and information associated with the at least
one generated physical measurement; generating by the device a
predicted time of failure of the device, wherein the generation of
the predicted time of failure of the device is based at least in
part upon the determined difference between the information
associated with the at least one received laboratory test result
and the information associated with the at least one generated
physical measurement; and outputting by the device the predicted
time of failure of the device.
[0073] In one example, the at least one laboratory test result is a
test result of a laboratory stress test associated with the
device.
[0074] In another example, the at least one physical measurement is
selected from the group comprising: a temperature measurement; a
current measurement; a voltage measurement; an acceleration
measurement; a tension measurement; a compression measurement; a
force measurement; a magnetic field measurement; and an
electro-magnetic field measurement.
[0075] In another example, the at least one physical measurement is
made in real-time.
[0076] In another example, the output is made to a display screen
associated with the device.
[0077] In another example, the program of instructions, when
executing, further performs the steps of: receiving by the device a
plurality of laboratory test results; generating by the device a
plurality of physical measurements associated with the device; and
determining by the device a difference between information
associated with the plurality of received laboratory test results
and information associated with the plurality of generated physical
measurements; wherein the generation of the predicted time of
failure of the device is based at least in part upon the determined
difference between information associated with the plurality of
received laboratory test results and information associated with
the plurality of generated physical measurements.
[0078] In another example: each of the plurality of laboratory test
results is received at different times; and each of the plurality
of physical measurements is generated at different times.
[0079] In another example, the device is selected from the group
comprising: a smart phone; a tablet; a laptop computer; a desktop
computer; a refrigerator; a freezer; a stove; a range; a hot water
heater; a boiler; a furnace; and a vehicle mounted device.
[0080] In another example, the information associated with the at
least one generated physical measurement comprises a difference
between the at least one generated physical measurement and at
least one value from a model.
[0081] In another example, the information associated with the at
least one generated physical measurement is the at least one
generated physical measurement itself.
[0082] In another embodiment, a system for predicting a time of
failure of a device, the system being integrated into the device is
provided, the system comprising: a receiving element configured to
receive at least one laboratory test result; a first generating
element configured to generate at least one physical measurement
associated with the device; a determining element in operative
communication with the receiving element and the first generating
element configured to determine a difference between information
associated with the at least one received laboratory test result
and information associated with the at least one generated physical
measurement; a second generating element in operative communication
with the determining element configured to generate a predicted
time of failure of the device, wherein the generation of the
predicted time of failure of the device is based at least in part
upon the determined difference between the information associated
with the at least one received laboratory test result and the
information associated with the at least one generated physical
measurement; and an outputting element in operative communication
with the second generating element configured to output the
predicted time of failure of the device.
[0083] In one example, the at least one laboratory test result is a
test result of a laboratory stress test associated with the
device.
[0084] In another example, the at least one physical measurement is
selected from the group comprising: a temperature measurement; a
current measurement; a voltage measurement; an acceleration
measurement; a tension measurement; a compression measurement; a
force measurement; a magnetic field measurement; and an
electro-magnetic field measurement.
[0085] In another example, the at least one physical measurement is
made in real-time.
[0086] In another example, the outputting element comprises a
display screen associated with the device.
[0087] In another example: the receiving element is configured to
receive a plurality of laboratory test results; the first
generating element is configured to generate a plurality of
physical measurements associated with the device; and the
determining element is further configured to determine a difference
between information associated with the plurality of received
laboratory test results and information associated with the
plurality of generated physical measurements; wherein the
generation of the predicted time of failure of the device is based
at least in part upon the determined difference between information
associated with the plurality of received laboratory test results
and information associated with the plurality of generated physical
measurements.
[0088] In another example, the information associated with the at
least one generated physical measurement is the at least one
generated physical measurement itself.
[0089] In another example: each of the plurality of laboratory test
results is received at different times; and each of the plurality
of physical measurements is generated at different times.
[0090] In another example, the device is selected from the group
comprising: a smart phone; a tablet; a laptop computer; a desktop
computer; a refrigerator; a freezer; a stove; a range; a hot water
heater; a boiler; a furnace; and a vehicle mounted device.
[0091] In another example, the information associated with the at
least one generated physical measurement comprises a difference
between the at least one generated physical measurement and at
least one value from a model.
[0092] In another example, the information associated with the at
least one generated physical measurement is the at least one
generated physical measurement itself.
[0093] In various examples, a monitoring approach is developed for
each subsystem, where the ultimate objective is to diagnose first
the potential failures of subsystems and then to predict the date
of failure of the entire device. The diagnosis may be performed
through the detection, the localization and the characterization of
failure(s). The detection may be conducted by computing residuals
which are failure indicators. The residuals may be constructed by
comparing the measurements obtained from laboratory stress tests to
those recorded by embedded sensors. In one specific example, the
predicted time of failure obtained from the stress tests will
remain intact provided that the residuals are equal to zero in
terms of mean. The deviation of residuals from zero may provide an
amplitude which will be a function of the time of failure of a
device. In one specific example, a fault diagnosis strategy based
on residual calculation obtained from models may be a fault
indicator (e.g., only in the case where the fault is characterized
by the mean-deviation of measurements). However, there are some
failures which cannot be diagnosed by the residual computation
process using physical models. In such scenarios, a frequency
components analysis may be used. This may comprise computing
frequency components of some signals during the stress tests and
repeating the same process with the recorded data (by
accelerometers, for example). Then, the objective is to isolate the
same band of frequencies in both cases and establish a comparison,
which gives a residual. When the obtained residual deviates from
zero in this example, this will be a fault indicator. The
combination of the various results obtained from the various models
and methods will indicate the time of failure of a device.
[0094] The monitoring performed for each subsystem may be combined
with module(s) transmitting the outcomes of the monitoring process
to a centralized system (e.g., a centralized system disposed on a
given device). The centralized system combines all of the received
information for processing. Then, the time of failure of the entire
device is predicted and transferred to a display system. The
display system may express the time of failure in terms of, for
example, days, months or years or according to a given format
available to a user (the user may be provided a mechanism to select
and/or change the format). For systems which are not ordinarily
embedded with a display system, a display system can be added for
this purpose. The displayed prediction, besides indicating the time
of failure of the entire device may indicate to the user the
reason(s) for the failure(s) and/or the component(s) that will lead
to the failure(s). Further, when component(s) are replaced by new
ones (and/or repaired) the user may be provided an indication of
what will become the updated predicted time of failure of the
entire device.
[0095] As described herein, mechanisms are provided for indicating
to a user the status of the user's devices and a decrease of the
devices' performance, quantified in terms of life expectancy.
[0096] As described herein, mechanisms are provided to help avoid
sudden and unexpected breakdown of devices that may have
detrimental consequences (e.g., loss of critical information such
as on a laptop computer or desktop computer).
[0097] As described herein, mechanisms are provided to allow
planning ahead and predictive maintenance that will increase the
life duration of devices, for example, by replacing the components
that will lead to failures.
[0098] When a device is purchased the life duration of the device
is at its highest level and, over time, the life duration will
decrease due to the different activities that the device may be
subjected to. As described herein, mechanisms are provided to
permit a user (e.g., a purchaser of a device) to monitor the status
of the device.
[0099] In one example, one or more algorithms may be implemented in
such a manner as to operate in real-time. Therefore, in this
example, there is no need to store a large amount of data, which
avoids high memory requirements.
[0100] In various examples, mechanisms may be provided to monitor
key indicators of failures (via measurements). In these examples,
the amplitude of such key indicators is function of a device's life
expectancy (as new data arrives, these amplitudes may be regularly
updated (e.g., periodically every hour, every day, every month) to
generate updated predictions of time of failure).
[0101] As described herein, prediction of the time of failure of a
device may be made by using a set of measurements (such as
temperature(s), current(s), tension(s), environmental variable(s)
and/or acceleration(s)).
[0102] As described herein, a device that ordinarily does not have
a display system may have such a display system incorporated
therein for the purpose of providing to a user a device status
(e.g., when the devices will fail or breakdown).
[0103] As described herein, various embodiments may combine two
sets of information--those obtained during baseline laboratory
stress tests (e.g., current, tension, temperature measurements)
with their counterparts recorded while the device is operating (and
this for each subsystem of the device). After determining the
predicted time of failure of each subsystem, a centralized system
may estimate the predicted time of failure of the device. The
predicted time of failure may be displayed by a display system
according to a format requested by the user. Also, updating the
time of failure after a failing component of the device is replaced
and/or repaired may be provided.
[0104] As described herein, mechanisms are provided that utilize
physical subsystems, a centralized system and information obtained
from stress field tests as well as those obtained from the actual
device (via embedded sensors) to determine a predicted time of
failure of the device.
[0105] As described herein, mechanisms are provided that use a
combination of data coming from laboratory/field tests along with
those acquired from the device to determine one or more residuals
(which may comprise one or more failure indicators). In various
examples, relying on the characteristics of the residuals and the
maximum life expectancy of the device, the predicted time of
failure may be inferred.
[0106] As described herein, in one example, a failure prediction is
made by analyzing the state of a subsystem. Such analysis may
comprise computing one or more residuals. In the case where the
residual(s) are equal to zero a given subsystem is perfectly
operating and the predicted time of failure for that subsystem is
equal to the one obtained from the stress tests results. On the
other hand (that is, when the residua(s) are different from zero),
the failure prediction is made by calculating a difference between
each residual and its counterpart obtained from the results of the
stress tests. The higher a residual, the closer the time of the
failure.
[0107] In various embodiments, the environments (conditions) in
which the stress tests have been performed change when the
corresponding measurements are provided by the device sensors (the
change may be due, for example, to device mobility and/or the
dynamic nature of the device use). In various examples, the
prediction algorithm(s) are not dependent on the environments (that
is, the same algorithm can be applied when the devices are
operating in different environments). In other examples, the
prediction algorithm(s) may depend on the nature of a given
subsystem (for example, if the characteristics of the subsystem
prevent elaboration of a mathematical model of the subsystem, a
data base model could be used (or vice versa)).
[0108] In other examples, prediction algorithm(s) may comprise:
Kalman filtering or its variants (e.g., when elaborating a
mathematical model is possible); fast fourier analysis or/and
wavelet analysis (with measurements acquired from the actual
subsystem to be used, in the case where the elaboration of
mathematical models are difficult or impossible).
[0109] In other examples, the mechanisms disclosed herein may be
applied in the context of one or more of the following: Computer,
Desktop; Computer, Embedded Systems; Computer, Laptop; Consumer
Device or Appliance, Applications; Consumer Device or Appliance,
Audio; Consumer Device or Appliance, General Consumer Device or
Appliance, HDTV; Consumer Device or Appliance, Image; Consumer
Device or Appliance, Media; Consumer Device or Appliance,
Television; Consumer Device or Appliance, User Interface; Consumer
Device or Appliance, Video; and/or Converged Communications, Mobile
applications.
[0110] In other examples, the mechanisms disclosed herein may be
implemented in the form of one or more software applications.
[0111] In other examples, the mechanisms disclosed herein may be
applied to any type of device(s).
[0112] Further, in various examples, any desired number of
device(s) may be configured for time of failure prediction and/or
time of failure diagnosis as described herein.
[0113] In other examples, any steps described herein may be carried
out in any appropriate desired order.
[0114] As will be appreciated by one skilled in the art, aspects of
the present invention may be embodied as a system, method or
computer program product. Accordingly, aspects of the present
invention may take the form of an entirely hardware embodiment, an
entirely software embodiment (including firmware, resident
software, micro-code, etc.) or an embodiment combining software and
hardware aspects that may all generally be referred to herein as a
"circuit," "module" or "system". Furthermore, aspects of the
present invention may take the form of a computer program product
embodied in one or more computer readable medium(s) having computer
readable program code embodied thereon.
[0115] Any combination of one or more computer readable medium(s)
may be utilized. The computer readable medium may be a computer
readable signal medium or a computer readable storage medium. A
computer readable storage medium may be, for example, but not
limited to, an electronic, magnetic, optical, electromagnetic,
infrared, or semiconductor system, apparatus, or device, or any
suitable combination of the foregoing. More specific examples (a
non-exhaustive list) of the computer readable storage medium would
include the following: an electrical connection having one or more
wires, a portable computer diskette, a hard disk, a random access
memory (RAM), a read-only memory (ROM), an erasable programmable
read-only memory (EPROM or Flash memory), an optical fiber, a
portable compact disc read-only memory (CD-ROM), an optical storage
device, a magnetic storage device, or any suitable combination of
the foregoing. In the context of this document, a computer readable
storage medium may be any tangible medium that can contain, or
store a program for use by or in connection with an instruction
execution system, apparatus, or device.
[0116] A computer readable signal medium may include a propagated
data signal with computer readable program code embodied therein,
for example, in baseband or as part of a carrier wave. Such a
propagated signal may take any of a variety of forms, including,
but not limited to, electro-magnetic, optical, or any suitable
combination thereof. A computer readable signal medium may be any
computer readable medium that is not a computer readable storage
medium and that can communicate, propagate, or transport a program
for use by or in connection with an instruction execution system,
apparatus, or device.
[0117] Program code embodied on a computer readable medium may be
transmitted using any appropriate medium, including but not limited
to wireless, wireline, optical fiber cable, RF, etc., or any
suitable combination of the foregoing.
[0118] Computer program code for carrying out operations for
aspects of the present invention may be written in any programming
language or any combination of one or more programming languages,
including an object oriented programming language such as Java,
Smalltalk, C++ or the like or a procedural programming language,
such as the "C" programming language or similar programming
languages. The program code may execute entirely on the user's
computer, partly on the user's computer, as a stand-alone software
package, partly on the user's computer and partly on a remote
computer or entirely on the remote computer or server. In the
latter scenario, the remote computer may be connected to the user's
computer through any type of network, including a local area
network (LAN) or a wide area network (WAN), or the connection may
be made to an external computer (for example, through the Internet
using an Internet Service Provider).
[0119] Aspects of the present invention may be described herein
with reference to flowchart illustrations and/or block diagrams of
methods, systems and/or computer program products according to
embodiments of the invention. It will be understood that each block
of the flowchart illustrations and/or block diagrams, and
combinations of blocks in the flowchart illustrations and/or block
diagrams, can be implemented by computer program instructions.
These computer program instructions may be provided to a processor
of a general purpose computer, special purpose computer, or other
programmable data processing apparatus to produce a machine, such
that the instructions, which execute via the processor of the
computer or other programmable data processing apparatus, create
means for implementing the functions/acts specified in the
flowchart and/or block diagram block or blocks.
[0120] These computer program instructions may also be stored in a
computer readable medium that can direct a computer, other
programmable data processing apparatus, or other devices to
function in a particular manner, such that the instructions stored
in the computer readable medium produce an article of manufacture
including instructions which implement the function/act specified
in the flowchart and/or block diagram block or blocks.
[0121] The computer program instructions may also be loaded onto a
computer, other programmable data processing apparatus, or other
devices to cause a series of operational steps to be performed on
the computer, other programmable apparatus or other devices to
produce a computer implemented process such that the instructions
which execute on the computer or other programmable apparatus or
other devices provide processes for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks
[0122] The flowcharts and block diagrams in the figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowcharts or block diagrams may
represent a module, segment, or portion of code, which comprises
one or more executable instructions for implementing the specified
logical function(s). It should also be noted that, in some
implementations, the functions noted in the block may occur out of
the order noted in the figures. For example, two blocks shown in
succession may, in fact, be executed substantially concurrently, or
the blocks may sometimes be executed in the reverse order,
depending upon the functionality involved. It will also be noted
that each block of the block diagrams and/or flowchart
illustrations, and combinations of blocks in the block diagrams
and/or flowchart illustrations, can be implemented by special
purpose hardware-based systems that perform the specified functions
or acts, or combinations of special purpose hardware and computer
instructions.
[0123] It is noted that the foregoing has outlined some of the
objects and embodiments of the present invention. This invention
may be used for many applications. Thus, although the description
is made for particular arrangements and methods, the intent and
concept of the invention is suitable and applicable to other
arrangements and applications. It will be clear to those skilled in
the art that modifications to the disclosed embodiments can be
effected without departing from the spirit and scope of the
invention. The described embodiments ought to be construed to be
merely illustrative of some of the features and applications of the
invention. Other beneficial results can be realized by applying the
disclosed invention in a different manner or modifying the
invention in ways known to those familiar with the art. In
addition, all of the examples disclosed herein are intended to be
illustrative, and not restrictive.
* * * * *