U.S. patent application number 15/427891 was filed with the patent office on 2018-08-09 for location based railway anomaly detection.
The applicant listed for this patent is Intel Corporation. Invention is credited to Thomas A. Birch, David I. Poisner.
Application Number | 20180222504 15/427891 |
Document ID | / |
Family ID | 63039086 |
Filed Date | 2018-08-09 |
United States Patent
Application |
20180222504 |
Kind Code |
A1 |
Birch; Thomas A. ; et
al. |
August 9, 2018 |
LOCATION BASED RAILWAY ANOMALY DETECTION
Abstract
Systems and methods for detecting train rail and railcar
anomalies are disclosed herein. In an example, detecting anomalies
includes: receiving measurements from a sensor array coupled to a
railcar in a train; obtaining baseline measurements from the sensor
array; obtaining, in near real time, measurements from the sensor
array while the railcar is operating; and detecting a railcar
anomaly based a comparison between the baseline and operating
measurements. In an example, the comparison of baseline and
operating measurements includes evaluating, over a sequence of time
data points, inertia sensor measurements (such as caused by side to
side railcar movement) to detect abnormal railcar oscillation. In
further examples, the data indexed to a GPS location is stored in a
database, and respective alerts are transmitted or outputted to an
output device (such as a display device) when an anomaly is
detected based on the collected measurements from the railcars.
Inventors: |
Birch; Thomas A.; (Portland,
OR) ; Poisner; David I.; (Carmichael, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Intel Corporation |
Santa Clara |
CA |
US |
|
|
Family ID: |
63039086 |
Appl. No.: |
15/427891 |
Filed: |
February 8, 2017 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
B61L 23/048 20130101;
B61L 2205/04 20130101; B61K 9/12 20130101; B61L 3/008 20130101;
B61L 25/025 20130101; B61L 27/0005 20130101; B61L 23/044 20130101;
B61L 23/045 20130101; B61L 23/041 20130101; B61L 25/021
20130101 |
International
Class: |
B61L 23/04 20060101
B61L023/04; B61K 9/12 20060101 B61K009/12; B61L 25/02 20060101
B61L025/02; B61L 27/00 20060101 B61L027/00 |
Claims
1. A system to detect railway anomalies, the system comprising: a
processor subsystem; and a memory including instructions that, when
executed by the processor subsystem, cause the processor subsystem
to: receive measurements from a sensor array coupled to a railcar
in a train; obtain baseline measurements from the sensor array;
obtain, in near real time, measurements from the sensor array while
the railcar is operating; detect a railcar anomaly based a
comparison between the baseline and operating measurements, wherein
the comparison of baseline and operating measurements includes an
evaluation, over a sequence of time data points, inertia sensor
measurements to detect abnormal railcar oscillation; store the data
indexed to a GPS location in a database; and transmit an alert to
an output device when an anomaly is detected based on the collected
measurements from the railcars.
2. The system of claim 1, wherein the inertia sensor measurements
and time data points are correlated with geographic location data
to locate a rail anomaly.
3. The system of claim 1, wherein to detect the railcar anomaly,
the processor subsystem is to detect an oscillation of the railcar
that exceeds a predetermined threshold, and wherein the processor
subsystem is to initiate an automatic braking subsystem to reduce
the railcar speed.
4. The system of claim 1, wherein the memory further includes
instructions to detect an anomaly as a tie or ballast issue,
wherein the evaluation is based on measurements including the
amplitude of a dip from the rails with an inertia sensor, the speed
of the train, weight of the railcar and train, and GPS
location.
5. The system of claim 1, wherein the memory further includes
instructions to detect an anomaly as rail warp, wherein the
evaluation is based on measurements including repeated amplitude
change, UPS location, train speed, and railcar and train
weight.
6. The system of claim 1, wherein the memory further includes
instructions to detect an anomaly as rail wear, wherein the
determination is based on captured data from an inertia sensor and
a camera is correlated with GPS location, railcar and train weight,
and train speed.
7. The system of claim 1, wherein the data indexed by UPS location
and stored in a database is subsequently analyzed, to identify a
rail anomaly at a UPS location of the railway.
8. The system of claim 1, wherein the inertia sensor measurements
indicate a side to side movement of the railcar, and wherein the
sensor array is coupled via an attachment of the sensor array to
respective trucks of the railcar.
9. At least one machine readable medium including instructions to
detect railway anomalies that, when executed by a machine, cause
the machine to: receive measurements from a sensor array coupled to
a railcar in a train; obtain baseline measurements from the sensor
array; obtain, in near real time, measurements from the sensor
array while the railcar is operating; detect a railcar anomaly
based a comparison between the baseline and operating measurements,
wherein the comparison of baseline and operating measurements
includes an evaluation, over a sequence of time data points, of
inertia sensor measurements to detect abnormal railcar oscillation;
store the data indexed to a GPS location in a database; and
transmit an alert to an output device when an anomaly is detected
based on the collected measurements from the railcars.
10. The at least one machine readable medium of claim 9, wherein
the inertia sensor measurements and time data points are integrated
with geographic location data to locate a rail anomaly.
11. The at least one machine readable medium of claim 9, wherein to
detect the railcar anomaly, the machine is further to detect an
oscillation of the railcar that exceeds a predetermined threshold,
and wherein the machine is further to initiate an automatic braking
subsystem to reduce the railcar speed.
12. The at least one machine readable medium of claim 9, wherein
the at least one machine readable medium further includes
instructions to detect an anomaly as a tie or ballast issue,
wherein the evaluation is based on measurements including the
amplitude of a dip from the rails with an inertia sensor, the speed
of the train, weight of the railcar and train, and GPS
location.
13. The at least one machine readable medium of claim 9, wherein
the at least one machine readable medium further includes
instructions to detect an anomaly as rail warp, wherein the
evaluation is based on measurements including repeated amplitude
change, GPS location, train speed, and railcar and train
weight.
14. The at least one machine readable medium of claim 9, wherein
the at least one machine readable medium further includes
instructions to detect an anomaly as rail wear, wherein the
evaluation is based on captured data from an inertia sensor and a
camera is correlated with GPS location, railcar and train weight,
and train speed.
15. The at least one machine readable medium of claim 9, wherein
the data indexed by UPS location and stored in a database is
subsequently analyzed, to identify a rail anomaly at a GPS location
of the railway.
16. The at least one machine readable medium of claim 9, wherein
the inertia sensor measurements indicate a side to side movement of
the railcar, and wherein the sensor array is coupled via an
attachment of the sensor array to respective trucks of the
railcar.
17. A method for detecting railway anomalies, the method
comprising: receiving, by a processor subsystem, measurements from
a sensor array coupled to a railcar in a train; obtaining baseline
measurements from the sensor array; obtaining, in near real time,
measurements from the sensor array while the railcar is operating;
detecting a railcar anomaly based a comparison between the baseline
and operating measurements, wherein the comparison of baseline and
operating measurements includes an evaluation, over a sequence of
time data points, of inertia sensor measurements to detect abnormal
railcar oscillation; storing the data indexed to a GPS location in
a database; and transmitting an alert to an output device when an
anomaly is detected based on the collected measurements from the
railcars.
18. The method of claim 17, wherein the inertia sensor measurements
and time data points are integrated with geographic location data
to locate a rail anomaly.
19. The method of claim 17, wherein to detect the railcar anomaly,
the processor subsystem is to detect an oscillation of the railcar
that exceeds a predetermined threshold, and wherein the processor
subsystem is to initiate an automatic braking subsystem to reduce
the railcar speed.
20. The method of claim 17, wherein the method further includes
detecting an anomaly as a tie or ballast issue, wherein the
evaluation is based on measurements including the amplitude of a
dip from the rails with an inertia sensor, the speed of the train,
weight of the railcar and train, and GPS location.
21. The method of claim 17, wherein the method further includes
detecting an anomaly as rail warp, wherein the evaluation is based
on measurements including repeated amplitude change, GPS location,
train speed, and railcar and train weight.
22. The method of claim 17, wherein the method further includes
detecting an anomaly as rail wear, wherein the evaluation is based
on captured data from an inertia sensor and a camera is correlated
with GPS location, railcar and train weight, and train speed.
23. The method of claim 17, wherein the data indexed by GPS
location and stored in a database is subsequently analyzed, to
identify a rail anomaly at a GPS location of the railway.
24. The method of claim 17, wherein the inertia sensor measurements
indicate a side to side movement of the railcar, and wherein the
sensor array is coupled via an attachment of the sensor array to
respective trucks of the railcar.
Description
TECHNICAL FIELD
[0001] Embodiments described herein generally relate to railway
monitoring and more particularly, to detecting anomalies in a
railway system occurring either with the railcars or on the rails
of the track.
BACKGROUND
[0002] The rail industry has 140,000 of miles of track in the US.
The trains that run on these tracks may be made of 100 railcars
each, with each railcar possibly weighing over 120 tons. Carrying
large amounts of weight over so many miles of track may lead to
many problems with both the railcars and the rails of the track.
Monitoring for anomalies on the rails and the railcars is a
necessary task for continuous operation and prevention of train
derailments.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] In the drawings, which are not necessarily drawn to scale,
like numerals may describe similar components in different views.
Like numerals having different letter suffixes may represent
different instances of similar components. The drawings illustrate
generally, by way of example, but not by way of limitation, various
embodiments discussed in the present document.
[0004] FIG. 1 is an example of an environment and system for
location based railway anomaly detection, according to an
embodiment.
[0005] FIG. 2 is a block of an example of an anomaly detection
system for location based railway anomaly detection, according to
an embodiment.
[0006] FIG. 3A is an illustration of an example of a level rail
track.
[0007] FIG. 3B is an illustration of an example of an out of level
rail track.
[0008] FIG. 4 is an illustration of an example of rail warp.
[0009] FIG. 5A is an illustration of an example of a normal rail
connected to a tie.
[0010] FIG. 5B is an illustration of a example of a rail that has
become separated from the tie.
[0011] FIG. 6 is an illustration of an example of forces that may
occur when a truck has a stuck center pin.
[0012] FIG. 7 is an illustration of an example of a railcar wheel
with a flat spot.
[0013] FIG. 8 is an illustration of an example of a cross section
of a rail and railcar wheel with wheel flange wear and rail head
wear.
[0014] FIG. 9 is an illustration of a flowchart of an example of a
method for detecting railroad anomalies, according to various
embodiments.
[0015] FIG. 10 is a block diagram illustrating an example of a
machine upon which one or more embodiments may be implemented.
DETAILED DESCRIPTION
[0016] The use of sensors and wireless communications has grown
exponentially in recent years providing a means for monitoring and
tracking many aspects of our industrialized world. Trains and the
railway system is one of the oldest modes of transportation.
Because of the thousands of miles of track and the weight carried
by each railcar, problems may easily develop, but are not
necessarily detectable until it is too late. Utilizing sensors
provides a way of monitoring both the rails and railcars for
anomalies. By implementing the use of sensors, a communication
system, and a means for aggregating the data, it is possible for
trains to detect anomalies while in use and operating under normal
speeds and conditions. Real time anomaly detection may occur,
alerting the train operator and preventing accidents, damage to the
rails or railcars, and in the worst case scenario, derailment.
[0017] Some of these anomalies impact train performance, may damage
track, or even lead to train derailments. By detecting the signs of
early failure or "pre-failure" events, an operator may be notified
prior to a massive failure, saving downtime and possibly avoiding
harm to people. This invention leverages a computer device with
sensors attached to collect and analyze data and performance of
various components of the railcar. These components include, for
example, attributes of the wheel, axle, bearing, truck, center pin,
coupling, suspension and brake. When a system begins to fail--it
often provides "pre-failure" symptoms that may be captured and
characterized. An alert may be provided to the operator pertaining
to the discovered symptom for remedial action. Sometimes these will
be emergency issues, other times they may indicate maintenance is
needed, but any danger is not immediate. This system allows the
operator to better plan and manage maintenance activities for a
railcar, with data from its previous loaded journeys.
[0018] Current anomaly detection solutions may not be deployed
while under normal load conditions. The systems and techniques
disclosed herein may provide accurate anomaly identification by
capturing and characterizing data from the railcar while it is
under load, utilizing sensors, and at track speed. An example of
being under load would be when the railcar is carrying cargo and is
part of a train containing multiple railcars each carrying over 100
tons. Examples of sensors that could be utilized on the railcar
include, but are not limited to, an accelerometer, gyroscope,
compass, camera, microphone, strain gauge, GPS, and physical
contact sensor. In a further example, a position of the sensor
within the railcar or within the train within may be captured or
tracked by the sensor or within a data system. For instance,
knowledge of whether the sensor is located on railcar #23 in the
train may be used to identify a position of a data event captured
on the railcar relative to a geographic location of the overall
train.
[0019] The present subject matter may avoid interruption of service
and manual intervention. By continuously assessing the health of
the track while under normal operating conditions, the track may
continuously be monitored and maintenance crews may be more
efficiently deployed. Track maintenance may turn from reactive to
proactive by catching early warning signs of wear and tear, well
before failure. Performing this data acquisition while at "track
speed" allows for a true representation of the state of the rail,
while loads continue to run on the track and interact with the
rail.
[0020] The measurement data with GPS location data is collected,
through radio frequency communications technique, by manually
gathering the information from the unit, etc. in an example, the
GPS location data may be collected from a centralized location
(e.g., in the front of the train), and the position of the other
sensors is calculated based on information that indicates which
railcar the sensor is located at, and the length of each railcar or
the distance from the centralized location. The data may be
uploaded to a cloud application. The data in the cloud application
may be integrated with other data from the same geolocations,
providing an historic view of the indicators for each railcar while
under different loads and at different speeds.
[0021] FIG. 1 illustrates an example environment 100 and an anomaly
detection system 200 for location based railway anomaly detection,
according to an embodiment. A train may comprise one or more engine
cars that provide the power for movement to the train. Behind
and/or in front of the engine cars are the railcars which carry the
cargo for the train. Each of the railcars are connected to each
other and are pulled along the rail track by the engines. In the
example environment 100 a railcar 105a positioned on a railway
track 131 and connected to another railcar 105b. A train typically
has many railcars connected together. In the example environment
100, railcar 105a comprises the cargo container 106 and two trucks
110a and 110b. Railcar 105a is an example of any one of the
railcars in a train. A cutout of the cargo container 106 shows a
truck 110a that the cargo container 106 is seated on. The truck
110a is connected to the cargo container 106 by center pin 121. The
center pin 121 pivots so that the truck 110a can rotate. The
rotation of truck 110a allows the train and its railcars, such as
railcar 105a, to move along curves on the railway track 131. In the
example embodiment 100, the truck 110a comprises the center pin 121
for connecting to cargo container 106 and two axles 120, which each
have two wheels 125. The wheels 125 roll along the rails 130 of
railway track 131. A wheel 125 will have a flange, which, in
combination with the other wheels on the truck 110a, secures the
truck 110a to the rails 130. The railcars 105a, 105b have no
propulsion means of their own, but because of the immense weight of
a railcar's cargo combined with the force and momentum for moving
the railcar, smooth railcar wheel and rail interaction is
essential.
[0022] In this example embodiment, the truck 110a has an anomaly
detection system 200 comprising at least one sensor and a processor
subsystem. Because of the layout of a train, a railcar may be
experiencing an issue, but the train operator is unaware. For a
train to operate smoothly and continuously move thousands of tons
of cargo, the wheels of each railcar need to interface with the
rails without issue. The example environment 100 also depicts a
break 135 in the rail 130. The break 135 is a type of anomaly which
could lead to, as an example, a difference in the height of the two
rails 130 that will cause railcar issues. The break 135 is an
example of an anomaly located on the rail which could cause an
issue for each railcar that passes over it. Some anomalies located
on the rails can cause immediate dangerous situations while others
are less severe, but can still damage the railcar wheels. The
anomaly detection system 200 may be utilized on each railcar 105a,
105b to monitor the condition and operation of its own trucks,
wheels, and axles. Additionally, the anomaly detection system 200
may collect data from multiple railcars to find anomalies located
on the rails.
[0023] FIG. 2 is a block diagram of an example of an anomaly
detection system 200 for location based railway anomaly detection
according to an embodiment. The anomaly detection system 200
comprises one or more sensors 201 connected to a processor
subsystem and memory in the sensor data processing unit 202. The
sensor data processing unit 202 is further connected to an anomaly
alert unit 203, which may include an output device (e.g., to
provide audible output or visible output) for providing an anomaly
alert. In an example, the communication of information between the
sensor data processing unit 202 arid the anomaly alert unit 203 may
be via a wired connection; in another example, the communication
may occur via a wireless transmitter 204.
[0024] Features of the anomaly alert unit 203 or the sensor data
processing unit 202 may also be connected to a remote database 207
via a remote network, such as via the cloud 205. The features of
anomaly detection or anomaly output performed by the sensor data
processing unit 202 or the anomaly alert unit 203 may be fed from
data in the remote database 207, because an anomaly may be specific
to the conditions of the track at a particular location.
[0025] In an example, the sensor data processing unit 202 and the
anomaly alert unit 203 are connected to a local database 209 that
is used to temporarily host, persist, or store data. For example,
the local database 209 may include data related to the past 10
miles and the next 10 miles of track. In a further example, the
data is periodically synchronized from the local database 209 to
the back-end database 209. This is used, for example, when the
train goes through tunnels or other periods when there are no
communications to the back-end services and the remote database
207.
[0026] The sensors 201 may be an accelerometer, gyroscope, GPS,
tachometer, thermistor, camera, or microphone. The sensors 201 may
be configured to provide constant measurements or measurements at
set intervals of time to the sensor data processing unit 202. In an
example, the sensors are mounted to obtain sensor measurements
regardless of the direction of travel of the railcar, as the
railcar may be pushed or pulled from either end. In further
examples, two separate sets of sensors, such as two separate
cameras, are used to obtain sensor measurements from the different
directions.
[0027] As an example, the sensor data processing unit 202 receives
the measurement data from the one or more sensors 201. The sensor
data processing unit 202 evaluates the sensor measurement data to
determine if an anomaly is present. To detect anomalies, in an
embodiment, the sensor data processing unit 202 may collect
measurement data to create a set of baseline data. The baseline
data set may be created by the train operator indicating that a set
of measurement data collected from a particular journey by the
train be used as the baseline data set. The baseline data set may
also be created by having the sensor data processing unit 202
analyze a gathered set of measurement data. If the gathered set of
data is all within a predetermined range of standard deviation,
then the set may be considered anomaly free and used as the
baseline data. In both scenarios, the sensor data processing unit
202 may take all the readings for each sensor and average them to
find the baseline data value for that sensor. The sensor data
processing unit 202 may also find the maximum and minimum values
recorded for each sensor 201 from the gathered set and use those
values as the threshold or guardrail values in the baseline data
set.
[0028] Once a set of baseline data has been established for the
sensors 201 present on a given railcar 105a, the sensor data
processing unit 202 may begin collecting operating measurement data
from the sensors 201 on a railcar 105a. The sensor data processing
unit 202 may evaluate the operating data against the baseline data
to determine if the operating data has surpassed a threshold for
what is normative operation by a railcar 105a and its components.
When this occurs, an anomaly may have been detected.
[0029] As an example scenario, a railcar 105a should not oscillate
or move from side to side excessively. The momentum created by
railcar oscillation may travel to additional railcars and
eventually have enough force to cause a railcar to topple and cause
a train derailment. If detected early enough, the train operator
may slow the train to decrease the oscillation and prevent a
derailment. An inertia sensor, such as an accelerometer or
gyroscope, may provide a measurement for the degree from center a
railcar is leaning to one side. As a railcar travels, especially as
it goes around curves, a normative degree of lean is expected. If
the degree of lean measurement data reported by the sensor is
determined by the sensor data processing unit to exceed a threshold
or guardrail degree of lean, then the sensor data processing unit
may begin to look for measurement data indicating lean in the other
direction. If the sensor data processing unit receives measurement
data from an inertia sensor that it determines is a degree of lean
in the opposite direction which exceeds the threshold limit, then
the railcar may be experiencing oscillation. The sensor data
processing unit may continue to receive measurement data from the
inertia sensor and denote the time for each measurement in excess
of the threshold. Several factors may be indicative of dangerous
oscillation the degree of lean in both directions continues to be
in excess of the threshold without diminishing, the frequency
increases for the lean in both directions in excess of the
threshold, or the degree of lean in both directions increases. In
this example, when the sensor data processing unit determines
dangerous oscillation is occurring, then a warning or alert is
generated.
[0030] When the sensor data processing unit determines an anomaly
is present, a message is sent to the anomaly alert unit 203 to warn
a train operator. Examples of embodiments for the anomaly alert
unit 203 may be a display located in the lead engine car or a
mobile device used by the train operator.
[0031] In an example, measurement data is sent from the sensor data
processing unit 202 to a wireless transmitter 204 attached to the
railcar or train. The wireless transmitter may communicate using
various wireless standards and networks, such as Wi-Fi, a cellular
data network, satellite communications, or long-range communication
networks. The measurement data is transmitted from wireless
transmitter 204 to the cloud 205.
[0032] At an offsite location, the measurement data for multiple
railcars and trains in the cloud 205 is collected at the data
collection unit 206. The collected measurement data, indexed by UPS
location, is stored in the database 207. The collected measurement
data may include sensor measurement data from railcars in a railway
network. In addition to being indexed by the UPS location, the
stored measurement data may include train related metadata such as
the size, length, or weight of the railcars on which the sensors
are located, the number of railcars in the train, the position
within the train of the railcar on which the sensor is located, or
the weight of railcars immediately in front of and behind the
railcar with the sensors.
[0033] The location processing unit 208 analyzes the measurement
data stored in the database 207. As an example, the location
processing unit 208 groups the measurement data by UPS location and
analyzes the measurement data at each location. In this embodiment,
when the location processing unit 208 finds multiple instances of
measurement data that exceeds a predetermined threshold for
normative operation all occurring at the same GPS location, then a
rail anomaly may be detected. Upon detecting a rail anomaly, the
location processing unit 208 may transmit a message through the
cloud 205 to the anomaly alert unit 203.
[0034] To detect anomalies on either a rail or a railcar, many
devices and technologies may be utilized. The following are some of
those devices and technologies and how they are used for rail and
railcar anomaly detection.
[0035] Measurements and sensor recordings are stored in a database
to track normal and baseline data and detect anomalies that are
persistent on a rail location. The database may also include a
railcar manufacturer's recommended service information, railcar
owner's maintenance and repair records, and previously detected
problem areas. The database may store train data, including the
train's configuration and semi static conditions, such as: the
size, length, and weight of each railcar on which sensors are
located, the number of railcars in the train, the direction of
travel for each railcar (forwards or backwards), and the position
within the train each railcar. Additional environmental factors
that may be detected by respective sensors may include factors such
as temperature, humidity, barometric pressure, wind velocity,
recent rainfall, soil moisture, or seismic data, relating to the
track environment, may be collected and stored.
[0036] Location and movement sensors are needed to detect and
locate most anomalies occurring on the rails or railcars. A GPS and
speedometer may be used to derive real-time data on the train's
current operation, including the location and velocity. An
accelerometer may be used to measure vibrations and absolute
orientation of the sensor with respect to the Earth's gravity, such
as an inclinometer. A gyroscope may be used to measure minute
angular changes. A compass or magnetometer may be used to measure
orientation with respect to the Earth's magnetic field.
[0037] Cameras, both visible and infrared, are used to capture
images. The one or more cameras may be mounted in one of several
positions, such as a single camera mounted with a wide-angle lens
to allow for viewing the track and the wheels, including the wheel
flange or multiple cameras, with one for each rail. One or more
microphones may be used to detect the squeal of the wheels on the
track.
[0038] Most railcars leverage an air brake system where the brakes
are applied by default through the use of internal springs. The
brakes are held open, or not applied, by compressed air. The
compressed air line extends the length of the train. If the
compressed air line is broken, the brakes will go into default mode
of applied. If the compressed air is restored, all brakes should be
released, but brakes may remain stuck in the applied position. A
moving train with a stuck brake results in dragging a non-moving
axle and stuck wheels. A railcar may also get stuck wheels outside
of a stuck brake scenario from ice or other debris preventing axle
rotation. This causes wheel flats and excessive heat build-up which
may lead to train derailments. Operators may need to look back at
the railcars to observe any signs of stuck brakes or wheels. A
stuck brake or wheel may be detected by monitoring axle and wheel
rotation while the train is moving. One example to detect a stuck
brake or wheel is the use of an optical tachometer with GPS or
inertia sensor data which reports data indicating movement of the
railcar but no corresponding report of rotation of the axle from
the tachometer.
[0039] A track will have cross level issues when the levelness of
the track is uneven. The cross level refers to the precise
measurement of the track's evenness and levelness when railcars are
traveling on the tracks. Current methods for cross level assessment
involve a rail engineer or road aster walking the track with a
handheld device or running a large "geometry car" deployed on the
track.
[0040] FIG. 3A is an illustration of an example of a level rail
track. As seen in FIG. 3A, track cross section 305 is a level
track, with rail 325 and rail 326 at approximately the same height.
Railroad tie 320 is also level. FIG. 3B is an illustration of an
example of an out of level rail track. In FIG. 3B, track cross
section 310 is an out of level track, with rail 340 at a lower
height than rail 341. Railroad tie 335 sits angled downward on one
side. Causes of the anomaly may be frost heave, erosion, ballast
issues, tie issues or temperature extremes. An example of detecting
cross level issues includes using an inertia sensor to determine
three of the dip in track as a railcar moves from level track 305
to out of level track 310. The force measurement, in conjunction
with a GPS reading indicates an out of level track location. When a
truck utilizes inertia sensors on each side, then if a dip is
detected on one side, but not the other, then a cross level issue
is present.
[0041] When rail track is laid, the method for connecting rails
together to form a continuous track leverages two main methods:
mechanical connectors joining one rail to another rail, and fusing
rails with a welded joint. Both of these joint types are offset
from each other to allow for a stronger section of track versus
placing them directly next to one another. FIG. 4 is an
illustration of an example of rail warp. As seen in FIG. 4, this
offset may sometimes cause a slight height variation from one rail
405 to the next connected rail 416. Because the joint 410 and joint
411 of the parallel rails are offset, as one side of the railcar is
dipping at a joint 411, the other side of the railcar is raised by
a rail 415. As the railcar moves along the track, the railcar will
then be rocked in the other direction as the side that was raised
dips at a joint 410 and the side that was previously dipped, is
raised by a rail 416. As the railcar moves along the track with the
alternating up and down for each side, the railcar will begin to
oscillate from side to side. The oscillation may cause a "rock `n`
roll" derailment. The remedy for an oscillation situation is to
slow the train to a speed that does not propagate the frequency of
joint crossing to the railcars. When a derailment occurs because of
oscillation, the documented speeds of the train are between 12 and
24 MPH. Detection of this activity depends on the type of railcar,
weight of the railcar, speed of the railcar, and observation of the
engineers during train travel. Rail companies have enacted speed
policies, adjusted track geometry, and adjusted joint positioning
to reduce this issue. An example of detecting the symptoms of one
or more oscillating railcars uses an inertia sensor to detect a
repeated pattern of significant amplitude in the one or more
railcars. The force is recorded in conjunction with the GPS
location, train speed, railcar and train weight. The data is then
aggregated into a larger dataset in the cloud for that section of
track.
[0042] FIG. 5A is an illustration of an example of a normal rail
connected to a tie. As seen in FIG. 5A, a normal cross section 505
shows rail 520 resting on top of tie 515. FIG. 5B is an
illustration of an example of a rail that has become separated from
the tie. Abnormal cross section 510 shows rail 540 not resting on
tie 535. When a railroad tie 535 becomes worn or ballast 545 is
lost, a "pot hole" or a general "dip" in the support of the rail
540 will develop. In normal cross section 505, the rail 520 is
placed upon the top plate 516, which is placed on the tie 515. The
top plate 516 is tightly secured with lag bolts to the bottom
section of rail 520. In abnormal cross section 510, when the tie
535 begins to shrink or rot, or the ballast 545 erodes, a gap 541
occurs between the top plate 536 and the tie 535. This gap 541 may
decrease in size when a train rolls along the rail 540 as the rail
540 is pressed onto the tie 535. Because the surrounding ties to
the rotted tie 535 are not deteriorating, it is not detectable
without manual cross level testing. An example of detecting a tie
or ballast issue along the track with railcar sensors uses an
inertia sensor to detect the amplitude of a change in conjunction
with the speed of train, weight of the railcar and train, and the
GPS location. A significant amplitude change occurring for multiple
railcars in the same geographic location is an indication of a
chuck hole or pot hole.
[0043] FIG. 6 is an illustration of an example of forces that may
occur when a truck has a stuck center pin. As illustrated in FIG. 6
from a top down view, a railcar's truck 620 includes four wheels
615 that engage with rail 635 and 636. In the center of the truck
is the center pin 621 and truck bed 622 which the railcar sits
upon. This allows the truck 620 to pivot around curves. If a truck
center pivot pin 621 becomes stuck, track gauge may be widened or
worse a derailment may occur.
[0044] When the truck 620 does not properly pivot to the
requirements of the track 630, a lateral force 610 is applied to
inside of rail 635 with force 605 and the inside of rail 636 with
force 625 by the flange 616 of the wheels 615. One example of
detection for a stuck center pin 621 is using a sensor to monitor
the truck centerline. If the truck centerline position does not
move when train or railcar curve movement is detected by a sensor,
such as an accelerometer or gyroscope, then the truck center pin
621 is stuck. Another example is the use of a spring to calculate
force of a normal truck turn. If the similar forces from the spring
are not recorded at the times as an inertia sensor or GPS is
indicating that the train is on a curve, then the truck center pin
621 is stuck. A further example would be the use of an LED attached
to the railcar which is pointing at non-uniform reflective tape
attached to the truck 620. The non-uniform reflective tape is most
reflective in center and less reflective at the edges. A sensor
measures light amplitude reflection. If the light amplitude
reflection does not change when the inertia sensors or GPS indicate
the train is on a curve, then the truck center pin 621 is stuck.
Another example uses the physical measure of fixed point on railcar
in contact with variable electronic sensor on the truck 620 or a
fixed point on the truck 620 with a variable electronic sensor on
the railcar. If the sensor does not indicate a change in position
when an inertia sensor indicates the train is on a curve, then the
truck center pin 621 is stuck.
[0045] The bearings of the axle may overheat and cause axle failure
leading to downtime and a possible train derailment. An example of
detecting overheating bearings uses a thermistor or infrared camera
that continually measures the temperature values of the bearing
assembly. If the bearing temperature exceeds a predetermined
temperature, then the bearings are overheating. Another example is
the use of a vibration sensor to detect a failing bearing. A wheel
and axle will generate a normative amount of vibration operating
under normal conditions. When a hearing fails, the axle will spin
abnormally and create abnormal vibration with may be detected when
compared to the normative vibration. A further example is the use
of a microphone to capture the sound of the bearings and analyze
the captured sound for the sound of a failing bearing.
[0046] Each truck 621 of a railcar usually includes two axles with
two wheels connected to each axle. The wheels are heat and pressure
fitted onto the axle. The axle rotates with the wheels as one unit.
When the train, and each truck of a railcar goes around a turn
there is great pressure exerted on each axle to keep the wheels
turning at a consistent rate. This stress may cause an axle to
fail, and thus allowing the wheels to rotate independently.
Independently rotating wheels may lead to railcar downtime or the
possibility of a derailment. One example for detecting a broken or
failing axle involves placing tachometer sensors next to each
wheel. When the tachometer measurements for two wheels on the same
axle are not the same, then the axle has broken. Another example of
broken axle detection uses imaging sensors that monitors the
alignment of the axle.
[0047] Railcar wheels 615 are commonly made from thick steel, but
may still lose their balance or become warped. This results in
wheels that wobble either vertically or horizontally which then
causes uneven flange wear on the rails. One example for detecting
unbalanced or warped wheels is by using an inertia sensor that
measures the repeating pattern of a wheel. An uneven repeating
pattern differing from a baseline wheel repeating patter is
indicative of a wobbling wheel, in either a vertical or horizontal
direction. Another example uses an imaging analytics system to
watch the wheel and rail interface to detect out of balance or
warped wheels.
[0048] FIG. 7 is an illustration of an example of a railcar wheel
with a flat spot. As illustrated in FIG. 7, a wheel 710 may develop
a flat spot 711 by moving a railcar with a non-rotating wheel 710.
A stuck brake or a frozen wheel 710 or axle 715 are several
examples of causes for non-rotating wheels 710. A wheel 710 with a
flat spot 711 will make a "ticking" sound when it rotates. When a
wheel 710 with a flat spot 711 rolls along a track rail 720, it may
seriously damage the rail 720 as the rotation impact, while under
heavy load, may mark or dent the rail head 725. Remediation for a
wheel 710 with a flat spot 711 is to remove the railcar from
service and resurface the wheel 710 or replace the entire axle 715
and wheel 710. An example for detecting a flat spot 711 on a wheel
710 uses an inertia sensor to measure a repeating vibration pattern
caused by the wheel flat 711. A wheel 710 with a flat spot 711 will
not have a smooth rotation and detected vibrating pattern is
indicative of a wheel flat. Another example uses an acoustic sensor
to record the repeating signature of the "ticking" noise. A further
example uses a camera to capture images of the wheel 710 and then
analyze the images of the wheel 710 for its round or out-of-round
characteristics. An additional example uses a physical sensor such
as an Electronic Drop Indicator, attached to the truck 705 and
identify the flat spot 711 through continuous measurements.
[0049] FIG. 8 is an illustration of an example of a cross section
of a rail and railcar wheel with wheel flange wear and rail head
wear. As seen in FIG. 8, a cross section of a wheel 805 is resting
on a rail 820. When a wheel 805 wears past industry standards, the
railcar may experience unpredictable behaviors including vibrations
and potential derailment. Wheel 805 examination is performed
manually on a cadence per railroad and railcar leasing company. A
wheel 805 may develop a problem between maintenance cycles. Over
time, a wheel 805 may develop flange wear 810 or a rail head 820
may develop rail wear 815. Each of these conditions may allow for
more side to side movement of the wheel 805 along the rail 820 from
the gap that exists as a result of the wheel wear 810 or rail wear
815. An example for detecting wheel wear 810 includes using a
camera for collecting images of the wheel 805. The wheel 805 may be
continuously or periodically monitored over the course of its life.
Due to the variable nature of wheel wear 810 and rail wear 815, it
is difficult to attribute one vibration signal specifically to
either wheel wear 810 or rail wear 815.
[0050] Truck hunting is when the truck of the railcar is pivoting
back and forth while the train is on a straight segment of track.
Truck hunting typically occurs when the contact point of the wheels
on the rails do not find a balance point between the four wheels of
the truck. Truck hunting may damage rails and wheel flanges.
Excessive truck hunting pushes the gauge of the wheel out of
specification. An example of detecting truck hunting use an inertia
sensor to detect the oscillating nature of truck which occurs
during truck hunting. If measurements show the truck pivoting while
the train or railcar is on a straight section of track, then truck
hunting is occurring. This may be combined with an acoustic sensor
to detect the wheel flange hit between the front wheel on one side
of the truck and the back wheel on the other side of the truck.
[0051] Railcars that begin to oscillate as a train goes over track
with recurring track geometry defects may cause a complete
derailment. This typically happens when trains are traveling
between 12 MPH and 24 MPH. Causes of this oscillation, or "Rock 'n
Roll" train effect are from either recurring joints that have begun
to depress in the ties or joints that are offset the same distance
for a long run. This causes the oscillation to occur and propagate
along the train. An example for detecting railcar oscillation uses
an inertia sensor, to measure the oscillation. Abnormal oscillation
for a railcar is when side to side sway of the car occurs at such a
degree that the railcars become unbalanced and have enough force
cause a complete derailment. Oscillation is determined by using a
sequence of timed data points. To detect the oscillation, an
initial inertia sensor measurement is recorded at one data point
for the degree of sway from center for the railcar. When a
measurement is recorded at a subsequent time data point for similar
sway in the opposite direction, then possible oscillation may be
occurring. If the pattern continues, with an increase in sequence
repetition or increase in degree of sway, then abnormal oscillation
may be occurring. Oscillation guard band limits may be assigned. If
the guard band limits are reached, then a notice to the engineering
may be sent, as well as braking of the train and counter suspension
activities to prevent a railcar from toppling over.
[0052] Track rails in North America use a gauge of 561/2inches.
Gauge is measured from the inside (gauge side) of the rail head to
inside of the other rail head. The outside of the rail head is
called the field side. Train derailments may occur if the gauge
exceeds 58'' or is under 56''. In either case, a wheel may fall off
the rail and ride on the wheel flange along the ballast and ties. A
derailment such as this will destroy tie plates, lag bolts, and
ties. The damage may cause the rail to dislodge from the ties and
all subsequent cars will derail. An example of detecting track
gauge deflection uses sensors such as an electronic measuring stick
and measuring the gauge near the load points or wheels. By using
sensors attached to the railcar, the gauge may be recorded at track
speed under heavy loads. This data is incorporated with UPS
information, the railcar and train weight. Additional sensors could
be used such as cameras or laser distance sensors to assess
gauge.
[0053] Rail head is the section of rail where the wheels touch the
rail. The wear occurs on the top but also occurs on the gauge side
of the rail head due to wheel flange contact, especially on track
curves. Rail head wear may also create a "corrugated" rail, which
usually occurs on a track with an incline. Detection of wear or a
corrugated rail is performed by a manual inspection or with a
geometry car inspection. An example of detecting rail head wear
uses an inertia sensor arid a camera. With the information from
these sensors, rail wear anomalies may be identified from
observation and actual loaded system actions. Rail head wear, such
as on the side of the rail head, has symptoms similar to and is
detected in the same manner as issues like wheel wear or truck
hunting. A corrugated rail would have similar symptoms and
detection as a failing bearing. However, if the detection is not
continuous for the railcar and its respective trucks and wheels,
but the detection is repeated for multiple railcars in the same
geographic location, then the issue lies with the rail instead. The
sensor data is captured and integrated with GPS data, railcar and
train weight, and train speed. The data is uploaded to the cloud
system for integration with other data from previous recordings in
the same location.
[0054] Tracks are welded together using a method called Continuous
Weld Track. Each rail section is 144 feet long, and is welded with
a thermite welding kit to join two rails together. Sometimes these
welds may become corrupt and will fail over time. An example of
detecting rail weld anomalies uses magnetic inductance to determine
rail continuity by running a current through the rail and detecting
changes in the magnetic inductance. When the inductance breaks or
becomes weak, the track weld is either failing or close to failing
and maintenance is necessary. This is performed while a train is on
the track, and the train is under load and at track speed. Data is
also collected for the GPS position, the ambient temperature, the
induction measurement and other train data. The collected data is
uploaded to the cloud system for further analysis and correlation
with other data.
[0055] FIG. 9 illustrates a flowchart of an example of a method 900
for railway anomaly detection, according to various embodiments.
The method 900 may provide similar functionality as described in
FIG. 2.
[0056] A railcar may have one or more sensors installed on it. Some
of the sensors may report information for the railcar itself, while
others may be situated to monitor specific parts of the railcar
such as the trucks or individual wheels. While the train and
railcars are underload and moving at track speed, the sensors may
continually or at set intervals record and transmit measurements.
At operation 905, a processor subsystem at the sensor data
processing unit on the railcar receives the measurement data from a
sensor array. A sensor array may include one or more sensors of one
or more types of sensors including an accelerometer, gyroscope,
GPS, or thermistor. In another example, the processor subsystem
receives images or video from a camera attached to the railcar. In
a further example, the processor subsystem receives audio recording
from a microphone attached to the railcar.
[0057] At operation 910, the processor subsystem obtains
measurement data to use as baseline measurements.
[0058] At operation 915, as the train and railcars continue to
operate under normative conditions, the sensors transmit
measurements to the processor subsystem. For example, the near real
time sensor measurements occur while the railcar is travelling at
track speed. As another example, the near real time sensor
measurements occur while the railcar is a full carry weight.
[0059] At operation 920, the baseline and operating measurement
data is compared to determine if a railway anomaly has been
detected. For example, a stuck brake or stuck wheel anomaly may be
detected when it is determined a wheel or axle is not moving using
measurement data from an optical tachometer in combination with
either a GPS sensor or an inertia sensor. In another example, a
stuck truck center pin may be detected when it is determined the
truck is not pivoting. A non-pivoting truck may be determined in
any of the following ways in combination with GPS positioning that
shows the railcar is turning: using accelerometer or gyroscope
measurement data of the truck, force measurement from a spring
connected to the truck, measuring light amplitude reflection from a
light pointed at the truck with non-uniform reflective tape
attached, a physical measure of a fixed point on the railcar in
contact with a variable electronic sensor on the truck, or a
physical measure of a fixed point on the truck in contact with a
variable electronic sensor on the railcar. In another example, an
anomaly of overheating bearings may be detected using measurement
data from a thermistor sensor, an infrared camera, a vibration
sensor, or audio analytics. As another example, a broken axle is
detected when the two wheels of the axle are rotating independently
based on measurements from tachometers placed next to each wheel on
the same axle. A further example, a warped wheel may be detected
when a wobble is found in either a vertical or horizontal direction
using an inertia sensor. In another example, a flat spot on a wheel
may be detected using measurements from one or more sensors
including an inertia sensor, an acoustic sensor, an imaging sensor,
or a physical sensor. For example, excessive wheel wear may be
detected based on images taken from the attached camera. In a
further example, excessive truck pivoting while the train is on a
straight segment of track may be detected by determining railcar
oscillation from an inertia sensor or measuring wheel flange hits
with an acoustic sensor.
[0060] At operation 925, measurement data from one or more inertia
sensors, taken at a sequence of time data points, is evaluated to
determine if the railcar is excessively swaying from side to side.
Sway may also be referred to as tilt, and may represent the angle
or amount of lean from vertical. To determine side to side sway or
oscillation, a measurement is taken from the inertia sensors for a
lean of the railcar to one side starting at one time data point.
Another inertia sensor measurement is then taken at the next data
point. The data points are examined to determine the amplitude of
the side to side sway or oscillation, and also the instantaneous
sway to either side. If the amplitude of the oscillation exceeds a
predetermined threshold, or the instantaneous tilt to the left or
right exceeds a threshold, then the train may automatically brake
or provide a warning provided to the train operator. Note that each
type of railcar may have separate thresholds for the maximum
permitted oscillation amplitude and maximum instantaneous tilt, and
those values may also vary depending on the specific contents of
the railcar.
[0061] Thresholds may be maintained for both a maximum permitted
oscillation amplitude as well as a maximum instantaneous tilt in
either direction. As an example, a particular railcar may have a
maximum permitted oscillation amplitude of 15 degrees. However,
this may not detect scenarios where there is excessive lean in one
direction but not the other, such as a 10 degree lean to the left
from center, but only a 4 degree lean to the right from center.
Thus, the particular railcar may also have a maximum instantaneous
tilt threshold, such as 9 degrees, to detect excessive sway even
though the total oscillation amplitude has not exceeded the maximum
permitted oscillation amplitude.
[0062] At operation 930, the measurement data is stored in a
database and indexed by the GPS location. As an example, the
database may include train's configuration and operating
conditions, including at least one of: the size, length, or weight
of the railcars on which the sensors are located, the number of
railcars in the train, the position within the train of the railcar
on which the sensor is located, or the weight of railcars
immediately in front of the railcar with the sensors. For example,
the measurement data is transmitted to a cloud-based service to
collect the data from multiple trains and update the track
condition database.
[0063] The data indexed UPS location may be further analyzed based
on the location to determine anomalies located on the rails. For
example, the sensor measurement data and time data points may be
integrated with the geographic location information to determine
location based anomalies, or anomalies occurring on the rail. A
further example, a tie or ballast issue is determined when a pot
hole is detected from measurement data including the amplitude of
drop from an inertia sensor, the speed of the train, the weight of
the railcar and train, and the UPS location. Another example may
determine the presence of rail warp from repeated amplitude change
occurring at the same UPS location, along with the train speed, and
the weight of the railcar and train. Another example may find the
presence of rail wear when an inertia sensor or camera captures
side to side movement of the truck and wheels along the rails
occurring in the same UPS location, along with train speed and the
weight of the train and railcar data. In another example, an
anomaly may be detected as a cross level issue based on unevenness
of the rails, wherein the measurements to determine unevenness
include the use of an inertia sensor to detect the force of a dip
in the rails combined with the GPS for train location arid speed,
and the weight of the railcar and train. Another example may use
images taken from a camera attached to the railcar and integrated
with GPS location information to detect a track gauge deflection
from either a wide or narrow rail gauge. In a further example, rail
weld failure is detected by running a current through the rail
while the train is under load and at track speed, and collecting
the GPS location, ambient temperature, and induction
measurement.
[0064] At operation 935, if an anomaly is detected, then an alert
is transmitted to a display device to notify an operator. As an
example, the display device may he located in the lead engine car.
The display device may also be a mobile device carried by a train
operator.
[0065] FIG. 10 illustrates a block diagram of an example machine
1000 upon which any one or more of the techniques (e.g.,
methodologies) discussed herein may perform. In alternative
embodiments, the machine 1000 may operate as a standalone device or
may be connected (e.g., networked) to other machines. In a
networked deployment, the machine 1000 may operate in the capacity
of a server machine, a client machine, or both in server-client
network environments. In an example, the machine 1000 may act as a
peer machine in peer-to-peer (P2P) (or other distributed) network
environment. The machine 1000 may be a personal computer (PC), a
tablet PC, a set-top box (STB), a personal digital assistant (PDA),
a mobile telephone, a web appliance, a network router, switch or
bridge, or any machine capable of executing instructions
(sequential or otherwise) that specify actions to be taken by that
machine. Further, while only a single machine is illustrated, the
term "machine" shall also be taken to include any collection of
machines that individually or jointly execute a set (or multiple
sets) of instructions to perform any one or more of the
methodologies discussed herein, such as cloud computing, software
as a service (SaaS), other computer cluster configurations.
[0066] Examples, as described herein, may include, or may operate
by, logic or a number of components, or mechanisms. Circuit sets
are a collection of circuits implemented in tangible entities that
include hardware (e.g., simple circuits, gates, logic, etc.).
Circuit set membership may be flexible over time and underlying
hardware variability. Circuit sets include members that may, alone
or in combination, perform specified operations when operating. In
an example, hardware of the circuit set may be immutably designed
to carry out a specific operation (e.g., hardwired). In an example,
the hardware of the circuit set may include variably connected
physical components (e.g., execution units, transistors, simple
circuits, etc.) including a computer readable medium physically
modified (e.g., magnetically, electrically, moveable placement of
invariant massed particles, etc.) to encode instructions of the
specific operation. In connecting the physical components, the
underlying electrical properties of a hardware constituent are
changed, for example, from an insulator to a conductor or vice
versa. The instructions enable embedded hardware (e.g., the
execution units or a loading mechanism) to create members of the
circuit set in hardware via the variable connections to carry out
portions of the specific operation when in operation. Accordingly,
the computer readable medium is communicatively coupled to the
other components of the circuit set member when the device is
operating. In an example, any of the physical components may be
used in more than one member of more than one circuit set. For
example, under operation, execution units may be used in a first
circuit of a first circuit set at one point in time and reused by a
second circuit in the first circuit set, or by a third circuit in a
second circuit set at a different time.
[0067] Machine (e.g., computer system) 1000 may include a hardware
processor 1002 (e.g., a central processing unit (CPU), a graphics
processing unit (GPU), a hardware processor core, or any
combination thereof), a main memory 1004 and a static memory 1006,
some or all of which may communicate with each other via an
interlink (e.g., bus) 1008. The machine 1000 may further include a
display unit 1010, an alphanumeric input device 1012 (e.g., a
keyboard), and a user interface (UI) navigation device 1014 (e.g.,
a mouse). In an example, the display unit 1010, input device 1012
and UI navigation device 1014 may be a touch screen display. The
machine 1000 may additionally include a storage device (e.g., drive
unit) 1016, a signal generation device 1018 (e.g., a speaker), a
network interface device 1020, and one or more sensors 1021, such
as a global positioning system ((GPS) sensor, compass,
accelerometer, or other sensor. The machine 1000 may include an
output controller 1028, such as a serial (e.g., Universal Serial
Bus (USB), parallel, or other wired or wireless (e.g., infrared
(IR), near field communication (NFC), etc.) connection to
communicate or control one or more peripheral devices (e.g., a
printer, card reader, etc.).
[0068] The storage device 1016 may include a machine readable
medium 1022 on which is stored one or more sets of data structures
or instructions 1024 (e.g., software) embodying or utilized by any
one or more of the techniques or functions described herein. The
instructions 1024 may also reside, completely or at least
partially, within the main memory 1004, within static memory 1006,
or within the hardware processor 1002 during execution thereof by
the machine 1000. In an example, one or any combination of the
hardware processor 1002, the main memory 1004, the static memory
1006, or the storage device 1016 may constitute machine readable
media.
[0069] While the machine readable medium 1022 is illustrated as a
single medium, the term "machine readable medium" may include a
single medium or multiple media (e.g., a centralized or distributed
database, and/or associated caches and servers) configured to store
the one or more instructions 1024.
[0070] The term "machine readable medium" may include any medium
that is capable of storing, encoding, or carrying instructions for
execution by the machine 1000 and that cause the machine 1000 to
perform any one or more of the techniques of the present
disclosure, or that is capable of storing, encoding or carrying
data structures used by or associated with such instructions.
Non-limiting machine readable medium examples may include
solid-state memories, and optical and magnetic media. In an
example, a massed machine readable medium comprises a machine
readable medium with a plurality of particles having invariant
(e.g., rest) mass. Accordingly, massed machine-readable media are
not transitory propagating signals. Specific examples of massed
machine readable media may include: non-volatile memory, such as
semiconductor memory devices (e.g., Electrically Programmable
Read-Only Memory (EPROM), Electrically Erasable Programmable
Read-Only Memory (EEPROM)) and flash memory devices; magnetic
disks, such as internal hard disks and removable disks;
magneto-optical disks; and CD-ROM and DVD-ROM disks.
[0071] A processor subsystem may be used to execute the instruction
on the machine-readable medium. The processor subsystem may include
one or more processors, each with one or more cores. Additionally,
the processor subsystem may be disposed on one or more physical
devices. The processor subsystem may include one or more
specialized processors, such as a graphics processing unit (GPU), a
digital signal processor (DSP), a field programmable gate array
(FPGA), or a fixed function processor.
[0072] The instructions 1024 may further be transmitted or received
over a communications network 1026 using a transmission medium via
the network interface device 1020 utilizing any one of a number of
transfer protocols (e.g., frame relay, Internet protocol (IP),
transmission control protocol (TCP), user datagram protocol (UDP),
hypertext transfer protocol (HTTP), etc.). Example communication
networks may include a local area network (LAN), a wide area
network (WAN), a packet data network (e.g., the Internet), mobile
telephone networks (e.g., cellular networks), Plain Old Telephone
(POTS) networks, and wireless data networks (e.g., Institute of
Electrical and Electronics Engineers (IEEE) 802.11 family of
standards known as Wi-Fi.RTM., IEEE 802.16 family of standards
known as WiMax.RTM.), IEEE 802.15.4 family of standards,
peer-to-peer (P2P) networks, among others. In an example, the
network interface device 1020 may include one or more physical
jacks (e.g., Ethernet, coaxial, or phone jacks) or one or more
antennas to connect to the communications network 1026. In an
example, the network interface device 1020 may include a plurality
of antennas to wirelessly communicate using at least one of
single-input multiple-output (SIMO), multiple-input multiple-output
(MLMO), or multiple-input single-output (MISO) techniques. The term
"transmission medium" shall be taken to include any intangible
medium that is capable of storing, encoding or carrying
instructions for execution by the machine 1000, and includes
digital or analog communications signals or other intangible medium
to facilitate communication of such software.
ADDITIONAL NOTES AND EXAMPLES
[0073] Example 1 is a system to detect railway anomalies, the
system comprising: a processor subsystem; and a memory including
instructions that, when executed by the processor subsystem, cause
the processor subsystem to: receive measurements from a sensor
array coupled to a railcar in a train; obtain baseline measurements
from the sensor array; obtain, in near real time, measurements from
the sensor array while the railcar is operating; detect a railcar
anomaly based a comparison between the baseline and operating
measurements, wherein the comparison of baseline and operating
measurements includes an evaluation, over a sequence of time data
points, inertia sensor measurements to detect abnormal railcar
oscillation; store the data indexed to a GPS location in a
database; and transmit an alert to an output device when an anomaly
is detected based on the collected measurements from the
railcars.
[0074] In Example 2, the subject matter of Example 1 optionally
includes wherein the inertia sensor measurements and time data
points are correlated with geographic location data to locate a
rail anomaly.
[0075] In Example 3, the subject matter of any one or more of
Examples 1-2 optionally include wherein the inertia sensor
measurements and time data points are integrated with environmental
data to locate a rail anomaly, the environmental data indicating at
least one of: temperature, humidity, barometric pressure, wind
velocity, recent rainfall, soil moisture, or seismic data.
[0076] In Example 4, the subject matter of any one or more of
Examples 1-3 optionally include wherein to detect the railcar
anomaly, the processor subsystem is to detect an oscillation of the
railcar that exceeds a predetermined threshold, and wherein the
processor subsystem is to initiate an automatic braking subsystem
to reduce the railcar speed.
[0077] In Example 5, the subject matter of any one or more of
Examples 1-4 optionally include wherein the memory further includes
instructions to detect an anomaly as a tie or ballast issue,
wherein the evaluation is based on measurements including the
amplitude of a dip from the rails with an inertia sensor, the speed
of the train, weight of the railcar and train, and GPS
location.
[0078] In Example 6, the subject matter of any one or more of
Examples 1-5 optionally include wherein the memory further includes
instructions to detect an anomaly as rail warp, wherein the
evaluation is based on measurements including repeated amplitude
change, GPS location, train speed, and railcar and train
weight.
[0079] In Example 7, the subject matter of any one or more of
Examples 1-6 optionally include wherein the memory further includes
instructions to detect an anomaly as rail wear, wherein the
evaluation is based on captured data from an inertia sensor and a
camera is correlated with GPS location, railcar and train weight,
and train speed.
[0080] In Example 8, the subject matter of any one or more of
Examples 1-7 optionally include wherein the data indexed by GPS
location and stored in a database is subsequently analyzed, to
identify a rail anomaly at a GPS location of the railway.
[0081] In Example 9, the subject matter of any one or more of
Examples 1-8 optionally include wherein the inertia sensor
measurements indicate a side to side movement of the railcar, and
wherein the sensor array is coupled via an attachment of the sensor
array to respective trucks of the railcar.
[0082] In Example 10, the subject matter of any one or more of
Examples 1-9 optionally include wherein the output device is a
located in a lead engine car.
[0083] In Example 11, the subject matter of any one or more of
Examples 1-10 optionally include wherein the sensor array
comprises: a GPS, accelerometer, or gyroscope.
[0084] In Example 12, the subject matter of any one or more of
Examples 1-11 optionally include wherein the operating sensor
measurements occur while the railcar is at track speed.
[0085] In Example 13, the subject matter of any one or more of
Examples 1-12 optionally include wherein the operating sensor
measurements occur when the railcar is at full carry weight.
[0086] In Example 14, the subject matter of any one or more of
Examples 1-13 optionally include wherein the database includes the
train's configuration and operating conditions, including: the
size, length, or weight of the railcars on which the sensor array
is located, the number of railcars in the train, the position
within the train of the railcar on which the sensor is located, or
the weight of railcars immediately in front of the railcar with the
sensors.
[0087] In Example 15, the subject matter of any one or more of
Examples 1-14 optionally include wherein the sensor array includes
a camera to produce image data, and wherein the near real time
measurements are derived from the image data.
[0088] in Example 16, the subject matter of any one or more of
Examples 1-15 optionally include wherein the sensor array includes
a microphone to produce audio data, and wherein the near real time
measurements are derived from the audio data.
[0089] In Example 17, the subject matter of any one or more of
Examples 1-16 optionally include communications circuitry
communicatively coupled to the processor subsystem, and wherein the
processor subsystem is to transmit the real time measurement data
via the communications circuitry to a remote server.
[0090] In Example 18, the subject matter of Example 17 optionally
includes wherein the remote server is configured to collect data
from multiple trains and maintain a track condition database based
on the data from the multiple trains.
[0091] In Example 19, the subject matter of any one or more of
Examples 1-18 optionally include wherein the memory further
includes instructions to detect an anomaly as a cross level issue
based on an unevenness of the rails determination, wherein the
measurements include the use of an inertia sensor to detect the
force of a dip in the rails combined with the GPS for train
location and speed, and the weight of the railcar and train.
[0092] In Example 20, the subject matter of any one or more of
Examples 1-19 optionally include wherein the memory further
includes instructions to detect an anomaly as track gauge
deflection based on a wide rail gauge determination, wherein the
images taken from a camera are used to measure the rail gauge.
[0093] In Example 21, the subject matter of any one or more of
Examples 1-20 optionally include wherein the memory further
includes instructions to detect an anomaly as track gauge
deflection based on a narrow rail gauge determination, wherein the
images taken from a camera are used to measure the rail gauge.
[0094] In Example 22, the subject matter of any one or more of
Examples 1-21 optionally include wherein the memory further
includes instructions to detect an anomaly as track gauge
deflection based on a wide rail gauge determination, wherein the
track gauge is measured with an electronic measurement device.
[0095] In Example 23, the subject matter of any one or more of
Examples 1-22 optionally include wherein the memory further
includes instructions to detect an anomaly as track gauge
deflection based on a narrow rail gauge determination, wherein the
track gauge is measured with an electronic measurement device.
[0096] In Example 24, the subject matter of any one or more of
Examples 1-23 optionally include wherein the memory further
includes instructions to detect an anomaly as a rail weld failure,
wherein the evaluation is based on measurements including running a
current through the rail while the train is under load and at track
speed, and collecting the GPS location, ambient temperature, and
induction measurement.
[0097] In Example 25, the subject matter of any one or more of
Examples 1-24 optionally include wherein the memory further
includes instructions to detect an anomaly as a stuck brake based
on a non-moving axle or wheel from an unreleased brake
determination, wherein measurements are collected from an optical
tachometer in combination with either a GPS sensor or an inertia
sensor.
[0098] In Example 26, the subject matter of any one or more of
Examples 1-25 optionally include wherein the memory further
includes instructions to detect an anomaly as a stuck wheel based
on a non-moving axle or wheel determination, wherein measurements
are collected from an optical tachometer in combination with either
a GPS sensor or an inertia sensor.
[0099] In Example 27, the subject matter of any one or more of
Examples 1-26 optionally include wherein the memory further
includes instructions to detect an anomaly as a stuck truck center
pin based on a non-pivoting truck determination, wherein the
measurements include accelerometer or gyroscope sensor data with
GPS position.
[0100] In Example 28, the subject matter of any one or more of
Examples 1-27 optionally include wherein the memory further
includes instructions to detect an anomaly as a stuck truck center
pin based on a non-pivoting truck determination, wherein the
measurements include calculated force from a spring connected to
the truck combined with inertia sensor and GPS sensor data.
[0101] In Example 29, the subject matter of any one or more of
Examples 1-28 optionally include wherein the memory further
includes instructions to detect an anomaly as a stuck truck center
pin based on a non-pivoting truck determination, wherein the
measurements include light amplitude reflection from a light
pointed at the truck with non-uniform reflective tape attached.
[0102] In Example 30, the subject matter of any one or more of
Examples 1-29 optionally include wherein the memory further
includes instructions to detect an anomaly as a stuck truck center
pin based on a non-pivoting truck determination, wherein the
measurements include a physical measure of a fixed point on the
railcar in contact with a variable electronic sensor on the
truck.
[0103] In Example 31, the subject matter of any one or more of
Examples 1-30 optionally include wherein the memory further
includes instructions to detect an anomaly as a stuck truck center
pin based on a non-pivoting truck determination, wherein the
measurements include a physical measure of a fixed point on the
truck in contact with a variable electronic sensor on the
railcar.
[0104] In Example 32, the subject matter of any one or more of
Examples 1-31 optionally include wherein the memory further
includes instructions to detect an anomaly as overheating bearings,
wherein the evaluation is based on measurements that include data
from thermistor sensor, an infrared camera, a vibration sensor, or
audio analytics.
[0105] In Example 33, the subject matter of any one or more of
Examples 1-32 optionally include wherein the memory further
includes instructions to detect an anomaly as a broken axle based
on the two wheels of one axle rotating independently determination,
wherein the measurement includes tachometers placed next to each
wheel on the same axle.
[0106] In Example 34, the subject matter of any one or more of
Examples 1-33 optionally include wherein the memory further
includes instructions to detect an anomaly as the wheels being out
of balance based on a warped wheel determination, wherein the
measurements include a wobble in either horizontal or vertical
directions using an inertia sensor.
[0107] In Example 35, the subject matter of any one or more of
Examples 1-34 optionally include wherein the memory further
includes instructions to detect an anomaly as a wheel with one or
more flat spots, wherein the evaluation is based on measurements
from the sensor array including an inertia sensor, an acoustic
sensor, an imaging sensor, or a physical sensor.
[0108] In Example 36, the subject matter of any one or more of
Examples 1-35 optionally include wherein the memory further
includes instructions to detect an anomaly as excessive wheel wear,
wherein the evaluation is based on measurements that include
collected images taken from an attached camera.
[0109] In Example 37, the subject matter of any one or more of
Examples 1-36 optionally include wherein the memory further
includes instructions to detect an anomaly as truck hunting based
on the truck pivoting while the train is on a straight segment of
track, wherein the measurements include detected railcar
oscillation by an inertia sensor.
[0110] In Example 38, the subject matter of any one or more of
Examples 1-37 optionally include wherein the memory further
includes instructions to detect an anomaly as truck hunting based
on the truck pivoting while the train is on a straight segment of
track, wherein the measurements also comprise measuring wheel
flange hits with an acoustic sensor.
[0111] Example 39 is at least one machine readable medium including
instructions to detect railway anomalies that, when executed by a
machine, cause the machine to: receive measurements from a sensor
array coupled to a railcar in a train; obtain baseline measurements
from the sensor array; obtain, in near real time, measurements from
the sensor array while the railcar is operating; detect a railcar
anomaly based a comparison between the baseline and operating
measurements, wherein the comparison of baseline and operating
measurements includes an evaluation, over a sequence of time data
points, of inertia sensor measurements to detect abnormal railcar
oscillation; store the data indexed to a GPS location in a
database; and transmit an alert to an output device when an anomaly
is detected based on the collected measurements from the
railcars.
[0112] In Example 40, the subject matter of Example 39 optionally
includes wherein the inertia sensor measurements and time data
points are integrated with geographic location data to locate a
rail anomaly.
[0113] In Example 41, the subject matter of any one or more of
Examples 39-40 optionally include wherein the inertia sensor
measurements and time data points are integrated with environmental
data to locate a rail anomaly, the environmental data indicating at
least one of: temperature, humidity, barometric pressure, wind
velocity, recent rainfall, soil moisture, or seismic data.
[0114] In Example 42, the subject matter of any one or more of
Examples 39-41 optionally include wherein to detect the railcar
anomaly, the machine is to detect an oscillation of the railcar
that exceeds a predetermined threshold, and wherein the machine is
to initiate an automatic braking subsystem to reduce the railcar
speed.
[0115] In Example 43, the subject matter of any one or more of
Examples 39-42 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as a tie or ballast issue, wherein the evaluation is based on
measurements including the amplitude of a dip from the rails with
an inertia sensor, the speed of the train, weight of the railcar
and train, and GPS location.
[0116] In Example 44, the subject matter of any one or more of
Examples 39-43 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as rail warp, wherein the evaluation is based on measurements
including repeated amplitude change, GPS location, train speed, and
railcar and train weight.
[0117] In Example 45, the subject matter of any one or more of
Examples 39-44 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as rail wear, wherein the evaluation is based on captured data from
an inertia sensor and a camera is correlated with GPS location,
railcar and train weight, and train speed.
[0118] In Example 46, the subject matter of any one or more of
Examples 39-45 optionally include wherein the data indexed by GPS
location and stored in a database is subsequently analyzed, to
identify a rail anomaly at a GPS location of the railway.
[0119] In Example 47, the subject matter of any one or more of
Examples 39-46 optionally include wherein the inertia sensor
measurements indicate a side to side movement of the railcar, and
wherein the sensor array is coupled via an attachment of the sensor
array to respective trucks of the railcar.
[0120] In Example 48, the subject matter of any one or more of
Examples 39-47 optionally include wherein the output device is a
located in the lead engine car.
[0121] In Example 49, the subject matter of any one or more of
Examples 39-48 optionally include wherein the sensor array
comprises at least one of a GPS, accelerometer, gyroscope, or other
inertia sensor.
[0122] In Example 50, the subject matter of any one or more of
Examples 39-49 optionally include wherein the operating sensor
measurements occur while the railcar is at track speed.
[0123] In Example 51, the subject matter of any one or more of
Examples 39-50 optionally include wherein the operating sensor
measurements occur when the railcar is at full carry weight.
[0124] In Example 52, the subject matter of any one or more of
Examples 39-51 optionally include wherein the database includes the
train's configuration and operating conditions, including at least
one of: the size, length, or weight of the railcars on which the
sensor array is located, the number of railcars in the train, the
position within the train of the railcar on which the sensor is
located, or the weight of railcars immediately in front of the
railcar with the sensors.
[0125] In Example 53, the subject matter of any one or more of
Examples 39-52 optionally include wherein the sensor array includes
a camera to produce image data, and wherein the near real time
measurements are derived from the image data.
[0126] In Example 54, the subject matter of any one or more of
Examples 39-53 optionally include wherein the sensor array includes
a microphone to produce audio data, and wherein the near real time
measurements are derived from the audio data.
[0127] In Example 55, the subject matter of any one or more of
Examples 39-54 optionally include wherein the machine transmits the
real time measurement data via communications circuitry to a remote
server.
[0128] In Example 56, the subject matter of Example 55 optionally
includes wherein the remote server is configured to collect data
from multiple trains and maintain a track condition database based
on the data from the multiple trains.
[0129] In Example 57, the subject matter of any one or more of
Examples 39-56 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as a cross level issue based on an unevenness of the rails
determination, wherein the measurements include the use of an
inertia sensor to detect the force of a dip in the rails combined
with the GPS for train location and speed, and the weight of the
railcar and train.
[0130] In Example 58, the subject matter of any one or more of
Examples 39-57 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as track gauge deflection based on a wide rail gauge determination,
wherein the images taken from a camera are used to measure the rail
gauge.
[0131] In Example 59, the subject matter of any one or more of
Examples 39-58 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as track gauge deflection based on a narrow rail gauge
determination, wherein the images taken from a camera are used to
measure the rail gauge.
[0132] In Example 60, the subject matter of any one or more of
Examples 39-59 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as track gauge deflection based on a wide rail gauge determination,
wherein the track gauge is measured with an electronic measurement
device.
[0133] in Example 61, the subject matter of any one or more of
Examples 39-60 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as track gauge deflection based on a narrow rail gauge
determination, wherein the track gauge is measured with an
electronic measurement device.
[0134] In Example 62, the subject matter of any one or more of
Examples 39-61 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as a rail weld failure, wherein the evaluation is based on
measurements including running a current through the rail while the
train is under load and at track speed, and collecting the OPS
location, ambient temperature, and induction measurement.
[0135] In Example 63, the subject matter of any one or more of
Examples 39-62 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as a stuck brake based on a non-moving axle or wheel from an
unreleased brake determination, wherein measurements are collected
from an optical tachometer in combination with either a GPS sensor
or an inertia sensor.
[0136] in Example 64, the subject matter of any one or more of
Examples 39-63 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as a stuck wheel based on a non-moving axle or wheel determination,
wherein measurements are collected from an optical tachometer in
combination with either a GPS sensor or an inertia sensor.
[0137] in Example 65, the subject matter of any one or more of
Examples 39-64 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as a stuck truck center pin based on a non-pivoting truck
determination, wherein the measurements include accelerometer or
gyroscope sensor data with GPS position.
[0138] In Example 66, the subject matter of any one or more of
Examples 39-65 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as a stuck truck center pin based on a non-pivoting truck
determination, wherein the measurements include calculated force
from a spring connected to the truck combined with inertia sensor
and GPS sensor data.
[0139] In Example 67, the subject matter of any one or more of
Examples 39-66 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as a stuck truck center pin based on a non-pivoting truck
determination, wherein the measurements include light amplitude
reflection from a light pointed at the truck with non-uniform
reflective tape attached.
[0140] In Example 68, the subject matter of any one or more of
Examples 39-67 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as a stuck truck center pin based on a non-pivoting truck
determination, wherein the measurements include a physical measure
of a fixed point on the railcar in contact with a variable
electronic sensor on the truck.
[0141] In Example 69, the subject matter of any one or more of
Examples 39-68 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as a stuck truck center pin based on a non-pivoting truck
determination, wherein the measurements include a physical measure
of a fixed point on the truck in contact with a variable electronic
sensor on the railcar.
[0142] In Example 70, the subject matter of any one or more of
Examples 39-69 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as overheating bearings, wherein the evaluation is based on
measurements that include data from thermistor sensor, an infrared
camera, a vibration sensor, or audio analytics.
[0143] In Example 71, the subject matter of any one or more of
Examples 39-70 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as a broken axle based on the two wheels of one axle rotating
independently determination, wherein the measurement includes
tachometers placed next to each wheel on the same axle.
[0144] In Example 72, the subject matter of any one or more of
Examples 39-71 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as the wheels being out of balance based on a warped wheel
determination, wherein the measurements include a wobble in either
horizontal or vertical directions using an inertia sensor.
[0145] In Example 73, the subject matter of any one or more of
Examples 39-72 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as a wheel with one or more flat spots, wherein the evaluation is
based on measurements from the sensor array including an inertia
sensor, an acoustic sensor, an imaging sensor, or a physical
sensor.
[0146] In Example 74, the subject matter of any one or more of
Examples 39-73 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as excessive wheel wear, wherein the evaluation is based on
measurements that include collected images taken from an attached
camera.
[0147] In Example 75, the subject matter of any one or more of
Examples 39-74 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as truck hunting based on the truck pivoting while the train is on
a straight segment of track, wherein the measurements include
detected railcar oscillation by an inertia sensor.
[0148] In Example 76, the subject matter of any one or more of
Examples 39-75 optionally include wherein the at least one machine
readable medium further includes instructions to detect an anomaly
as truck hunting based on the truck pivoting while the train is on
a straight segment of track, wherein the measurements also comprise
measuring wheel flange hits with an acoustic sensor.
[0149] Example 77 is a method for detecting railway anomalies, the
method comprising: receiving, by a processor subsystem,
measurements from a sensor array coupled to a railcar in a train;
obtaining baseline measurements from the sensor array; obtaining,
in near real time, measurements from the sensor array while the
railcar is operating; detecting a railcar anomaly based a
comparison between the baseline and operating measurements, wherein
the comparison of baseline and operating measurements includes an
evaluation, over a sequence of time data points, of inertia sensor
measurements to detect abnormal railcar oscillation; storing the
data indexed to a GPS location in a database; and transmitting an
alert to an output device when an anomaly is detected based on the
collected measurements from the railcars.
[0150] In Example 78, the subject matter of Example 77 optionally
includes wherein the inertia sensor measurements and time data
points are integrated with geographic location data to locate a
rail anomaly.
[0151] In Example 79, the subject matter of any one or more of
Examples 77-78 optionally include wherein the inertia sensor
measurements and time data points are integrated with environmental
data to locate a rail anomaly, the environmental data indicating at
least one of: temperature, humidity, barometric pressure, wind
velocity, recent rainfall, soil moisture, or seismic data.
[0152] In Example 80, the subject matter of any one or more of
Examples 77-79 optionally include wherein to detect the railcar
anomaly, the processor subsystem is to detect an oscillation of the
railcar that exceeds a predetermined threshold, and wherein the
processor subsystem is to initiate an automatic braking subsystem
to reduce the railcar speed.
[0153] In Example 81, the subject matter of any one or more of
Examples 77-80 optionally include wherein the method further
includes detecting an anomaly as a tie or ballast issue, wherein
the evaluation is based on measurements including the amplitude of
a dip from the rails with an inertia sensor, the speed of the
train, weight of the railcar and train, and GPS location.
[0154] In Example 82, the subject matter of any one or more of
Examples 77-81 optionally include wherein the method further
includes detecting an anomaly as rail warp, wherein the evaluation
is based on measurements including repeated amplitude change, GPS
location, train speed, and railcar and train weight.
[0155] In Example 83, the subject matter of any one or more of
Examples 77-82 optionally include wherein the method further
includes detecting an anomaly as rail wear, wherein the evaluation
is based on captured data from an inertia sensor and a camera is
correlated with GPS location, railcar and train weight, and train
speed.
[0156] In Example 84, the subject matter of any one or more of
Examples 77-83 optionally include wherein the data indexed by GPS
location and stored in a database is subsequently analyzed, to
identify a rail anomaly at a GPS location of the railway.
[0157] In Example 85, the subject matter of any one or more of
Examples 77-84 optionally include wherein the inertia sensor
measurements indicate a side to side movement of the railcar, and
wherein the sensor array is coupled via an attachment of the sensor
array to respective trucks of the railcar.
[0158] In Example 86, the subject matter of any one or more of
Examples 77-85 optionally include wherein the output device is a
located in the lead engine car.
[0159] In Example 87, the subject matter of any one or more of
Examples 77-86 optionally include wherein the sensor array
comprises at least one of a GPS, accelerometer, gyroscope, or other
inertia sensor.
[0160] In Example 88, the subject matter of any one or more of
Examples 77-87 optionally include wherein the operating sensor
measurements occur while the railcar is at track speed.
[0161] In Example 89, the subject matter of any one or more of
Examples 77-88 optionally include wherein the operating sensor
measurements occur when the railcar is at full carry weight.
[0162] In Example 90, the subject matter of any one or more of
Examples 77-89 optionally include wherein the database includes the
train's configuration and operating conditions, including at least
one of: the size, length, or weight of the railcars on which the
sensor array is located, the number of railcars in the train, the
position within the train of the railcar on which the sensor is
located, or the weight of railcars immediately in front of the
railcar with the sensors.
[0163] In Example 91, the subject matter of any one or more of
Examples 77-90 optionally include wherein the sensor array includes
a camera to produce image data, and wherein the near real time
measurements are derived from the image data.
[0164] In Example 92, the subject matter of any one or more of
Examples 77-91 optionally include wherein the sensor array includes
a microphone to produce audio data, and wherein the near real time
measurements are derived from the audio data.
[0165] In Example 93, the subject matter of any one or more of
Examples 77-92 optionally include communications circuitry
communicatively coupled to the processor subsystem, and wherein
the, processor subsystem is to transmit the real time measurement
data via the communications circuitry to a remote server.
[0166] In Example 94, the subject matter of Example 93 optionally
includes wherein the remote server is configured to collect data
from multiple trains and maintain a track condition database based
on the data from the multiple trains.
[0167] In Example 95, the subject matter of any one or more of
Examples 77-94 optionally include wherein the method further
includes detecting an anomaly as a cross level issue based on an
unevenness of the rails determination, wherein the measurements
include the use of an inertia sensor to detect the force of a dip
in the rails combined with the UPS for train location and speed,
and the weight of the railcar and train.
[0168] In Example 96, the subject matter of any one or more of
Examples 77-95 optionally include wherein the method further
includes detecting an anomaly as track gauge deflection based on a
wide rail gauge determination, wherein the images taken from a
camera are used to measure the rail gauge.
[0169] In Example 97, the subject matter of any one or more of
Examples 77-96 optionally include wherein the method further
includes detecting an anomaly as track gauge deflection based on a
narrow rail gauge determination, wherein the images taken from a
camera are used to measure the rail gauge.
[0170] In Example 98, the subject matter of any one or more of
Examples 77-97 optionally include wherein the method further
includes detecting an anomaly as track gauge deflection based on a
wide rail gauge determination, wherein the track gauge is measured
with an electronic measurement device.
[0171] In Example 99, the subject matter of any one or more of
Examples 77-98 optionally include wherein the method further
includes detecting an anomaly as track gauge deflection based on a
narrow rail gauge determination, wherein the track gauge is
measured with an electronic measurement device.
[0172] In Example 100, the subject matter of any one or more of
Examples 77-99 optionally include wherein the method further
includes detecting an anomaly as a rail weld failure, wherein the
evaluation is based on the measurements including running a current
through the rail while the train is under load and at track speed,
and collecting the GPS location, ambient temperature, and induction
measurement.
[0173] In Example 101, the subject matter of any one or more of
Examples 77-100 optionally include wherein the method further
includes detecting an anomaly as a stuck brake based on a
non-moving axle or wheel from an unreleased brake determination,
wherein measurements are collected from an optical tachometer in
combination with either a GPS sensor or an inertia sensor.
[0174] In Example 102, the subject matter of any one or more of
Examples 77-101 optionally include wherein the method further
includes detecting an anomaly as a stuck wheel based on a
non-moving axle or wheel determination, wherein measurements are
collected from an optical tachometer in combination with either a
UPS sensor or an inertia sensor.
[0175] In Example 103, the subject matter of any one or more of
Examples 77-102 optionally include wherein the method further
includes detecting an anomaly as a stuck truck center pin based on
a non-pivoting truck determination, wherein the measurements
include accelerometer or gyroscope sensor data with GPS
position.
[0176] In Example 104, the subject matter of any one or more of
Examples 77-103 optionally include wherein the method further
includes detecting an anomaly as a stuck truck center pin based on
a non-pivoting truck determination, wherein the measurements
include calculated force from a spring connected to the truck
combined with inertia sensor and GPS sensor data.
[0177] In Example 105, the subject matter of any one or more of
Examples 77-104 optionally include wherein the method further
includes detecting an anomaly as a stuck truck center pin based on
a non-pivoting truck determination, wherein the measurements
include light amplitude reflection from a light pointed at the
truck with non-uniform reflective tape attached.
[0178] In Example 106, the subject matter of any one or more of
Examples 77-105 optionally include wherein the method further
includes detecting an anomaly as a stuck truck center pin based on
a non-pivoting truck determination, wherein the measurements
include a physical measure of a fixed point on the railcar in
contact with a variable electronic sensor on the truck.
[0179] In Example 107, the subject matter of any one or more of
Examples 77-106 optionally include wherein the method further
includes detecting an anomaly as a stuck truck center pin based on
a non-pivoting truck determination, wherein the measurements
include a physical measure of a fixed point on the truck in contact
with a variable electronic sensor on the railcar.
[0180] In Example 108, the subject matter of any one or more of
Examples 77-107 optionally include wherein the method further
includes detecting an anomaly as overheating bearings, wherein the
evaluation is based on measurements that include data from
thermistor sensor, an infrared camera, a vibration sensor, or audio
analytics.
[0181] In Example 109, the subject matter of any one or more of
Examples 77-108 optionally include wherein the method further
includes detecting an anomaly as a broken axle based on the two
wheels of one axle rotating independently determination, wherein
the measurement includes tachometers placed next to each wheel on
the same axle.
[0182] In Example 110, the subject matter of any one or more of
Examples 77-109 optionally include wherein the method further
includes detecting an anomaly as the wheels being out of balance
based on a warped wheel determination, wherein the measurements
include a wobble in either horizontal or vertical directions using
an inertia sensor.
[0183] In Example 111, the subject matter of any one or more of
Examples 77-110 optionally include wherein the method further
includes detecting an anomaly as a wheel with one or more flat
spots, wherein the evaluation is based on measurements from the
sensor array including an inertia sensor, an acoustic sensor, an
imaging sensor, or a physical sensor.
[0184] In Example 112, the subject matter of any one or more of
Examples 77-111 optionally include wherein the method further
includes detecting an anomaly as excessive wheel wear, wherein the
evaluation is based on measurements that include collected images
taken from an attached camera.
[0185] In Example 113, the subject matter of any one or more of
Examples 77-112 optionally include wherein the method further
includes detecting an anomaly as truck hunting based on the truck
pivoting while the train is on a straight segment of track, wherein
the measurements include detected railcar oscillation by an inertia
sensor.
[0186] In Example 114, the subject matter of any one or more of
Examples 77-113 optionally include wherein the method further
includes detecting an anomaly as truck hunting based on the truck
pivoting while the train is on a straight segment of track, wherein
the measurements also comprise measuring wheel flange hits with an
acoustic sensor.
[0187] Example 115 is at least one machine readable medium
including instructions, which when executed by an electronic
device, cause the computing system to perform any of the methods of
Examples 77-114.
[0188] Example 116 is an apparatus comprising means for performing
any of the methods of Examples 77-114.
[0189] Example 117 is a system for detecting railway anomalies, the
system comprising: means for receiving measurements from a sensor
array coupled a railcar in a train; means for obtaining baseline
measurements from the sensor array; means for obtaining, in near
real time, measurements from the sensor array while the railcar is
operating; means for detecting a railcar anomaly based a comparison
between the baseline and operating measurements, wherein the
comparison of baseline and operating measurements includes an
evaluation, over a sequence of time data points, of inertia sensor
measurements to detect abnormal railcar oscillation; means for
storing the data indexed to a GPS location in a database; and means
for transmitting an alert to an output device when an anomaly is
detected based on the collected measurements from the railcars.
[0190] In Example 118, the subject matter of Example 117 optionally
includes wherein the inertia sensor measurements and time data
points are integrated with geographic location data to locate a
rail anomaly.
[0191] In Example 119, the subject matter of any one or more of
Examples 117-118 optionally include wherein the inertia sensor
measurements and time data points are integrated with environmental
data to locate a rail anomaly, the environmental data indicating at
least one of: temperature, humidity, barometric pressure, wind
velocity, recent rainfall, soil moisture, or seismic data.
[0192] In Example 120, the subject matter of any one or more of
Examples 117-119 optionally include wherein to detect the railcar
anomaly, the system is to detect an oscillation of the railcar that
exceeds a predetermined threshold, and wherein the system is to
initiate an automatic braking subsystem to reduce the railcar
speed.
[0193] In Example 121, the subject matter of any one or more of
Examples 117-120 optionally include wherein the system further
includes a means for detecting an anomaly as a tie or ballast
issue, wherein the evaluation is based on measurements including
the amplitude of a dip from the rails with an inertia sensor, the
speed of the train, weight of the railcar and train, and GPS
location.
[0194] In Example 122, the subject matter of any one or more of
Examples 117-121 optionally include wherein the system further
includes a means for detecting an anomaly as rail warp, wherein the
evaluation is based on measurements including repeated amplitude
change, GPS location, train speed, and railcar and train
weight.
[0195] In Example 123, the subject matter of any one or more of
Examples 117-122 optionally include wherein the system further
includes a means for detecting an anomaly as rail wear, wherein the
evaluation is based on captured data from an inertia sensor and a
camera is correlated with GPS location, railcar and train weight,
and train speed.
[0196] In Example 124, the subject matter of any one or more of
Examples 117-123 optionally include wherein the data indexed by UPS
location and stored in a database is subsequently analyzed, to
identify a rail anomaly at a UPS location of the railway.
[0197] In Example 125, the subject matter of any one or more of
Examples 117-124 optionally include wherein the output device is a
located in the lead engine car.
[0198] In Example 126, the subject matter of any one or more of
Examples 117-125 optionally include wherein the sensor array
comprises at least one of a UPS, accelerometer, gyroscope, or other
inertia sensor.
[0199] In Example 127, the subject matter of any one or more of
Examples 117-126 optionally include wherein the operating sensor
measurements occur while the railcar is at track speed.
[0200] In Example 128, the subject matter of any one or more of
Examples 117-127 optionally include wherein the operating sensor
measurements occur when the railcar is at full carry weight.
[0201] In Example 129, the subject matter of any one or more of
Examples 117-128 optionally include wherein the database includes
the train's configuration and operating conditions, including at
least one of: the size, length, or weight of the railcars on which
the sensor array is located, the number of railcars in the train,
the position within the train of the railcar on which the sensor is
located, or the weight of railcars immediately in front of the
railcar with the sensors.
[0202] In Example 130, the subject matter of any one or more of
Examples 117-129 optionally include wherein the sensor array
includes a camera to produce image data, and wherein the near real
time measurements are derived from the image data.
[0203] In Example 131, the subject matter of any one or more of
Examples 117-130 optionally include wherein the sensor array
includes a microphone to produce audio data, and wherein the near
real time measurements are derived from the audio data.
[0204] In Example 132, the subject matter of any one or more of
Examples 117-131 optionally include means for transmitting the real
time measurement data via the communications circuitry to a remote
server.
[0205] In Example 133, the subject matter of Example 132 optionally
includes wherein the remote server is configured to collect data
from multiple trains and maintain a track condition database based
on the data from the multiple trains.
[0206] In Example 134, the subject matter of any one or more of
Examples 117-133 optionally include wherein the system further
includes a means for detecting an anomaly as a cross level issue
based on an unevenness of the rails determination, wherein the
measurements include the use of an inertia sensor to detect the
force of a dip in the rails combined with the GPS for train
location and speed, and the weight of the railcar and train.
[0207] In Example 135, the subject matter of any one or more of
Examples 117-134 optionally include wherein the system further
includes a means for detecting an anomaly as track gauge deflection
based on a wide rail gauge determination, wherein the images taken
from a camera are used to measure the rail gauge.
[0208] In Example 136, the subject matter of any one or more of
Examples 117-135 optionally include wherein the system further
includes a means for detecting an anomaly as track gauge deflection
based on a narrow rail gauge determination, wherein the images
taken from a camera are used to measure the rail gauge.
[0209] In Example 137, the subject matter of any one or more of
Examples 117-136 optionally include wherein the system further
includes a means for detecting an anomaly as track gauge deflection
based on a wide rail gauge determination, wherein the track gauge
is measured with an electronic measurement device.
[0210] In Example 138, the subject matter of any one or more of
Examples 117-137 optionally include wherein the system further
includes a means for detecting an anomaly as track gauge deflection
based on a narrow rail gauge determination, wherein the track gauge
is measured with an electronic measurement device.
[0211] In Example 1.39, the subject matter of any one or more of
Examples 117-138 optionally include wherein the system further
includes a means for detecting an anomaly as a rail weld failure,
wherein the evaluation is based on measurements including running a
current through the rail while the train is under load and at track
speed, and collecting the UPS location, ambient temperature, and
induction measurement.
[0212] In Example 140, the subject matter of any one or more of
Examples 117-139 optionally include wherein the system further
includes a means for detecting an anomaly as a stuck brake based on
a non-moving axle or wheel from an unreleased brake determination,
wherein measurements are collected from an optical tachometer in
combination with either a UPS sensor or an inertia sensor.
[0213] In Example 141, the subject matter of any one or more of
Examples 117-140 optionally include wherein the system further
includes a means for detecting an anomaly as a stuck wheel based on
a non-moving axle or wheel determination, wherein measurements are
collected from an optical tachometer in combination with either a
UPS sensor or an inertia sensor.
[0214] In Example 142, the subject matter of any one or more of
Examples 117-141 optionally include wherein the system further
includes a means for detecting an anomaly as a stuck truck center
pin based on a non-pivoting truck determination, wherein the
measurements include accelerometer or gyroscope sensor data with
UPS position.
[0215] In Example 143, the subject matter of any one or more of
Examples 117-142 optionally include wherein the system further
includes a means for detecting an anomaly as a stuck truck center
pin based on a non-pivoting truck determination, wherein the
measurements include calculated force from a spring connected to
the truck combined with inertia sensor and UPS sensor data.
[0216] In Example 144, the subject matter of any one or more of
Examples 117-143 optionally include wherein the system further
includes a means for detecting an anomaly as a stuck truck center
pin based on a non-pivoting truck determination, wherein the
measurements include light amplitude reflection from a light
pointed at the truck with non-uniform reflective tape attached.
[0217] In Example 145, the subject matter of any one or more of
Examples 117-144 optionally include wherein the system further
includes a means for detecting an anomaly as a stuck truck center
pin based on a non-pivoting truck determination, wherein the
measurements include a physical measure of a fixed point on the
railcar in contact with a variable electronic sensor on the
truck.
[0218] In Example 146, the subject matter of any one or more of
Examples 117-145 optionally include wherein the system further
includes a means for detecting an anomaly as a stuck truck center
pin based on a non-pivoting truck determination, wherein the
measurements include a physical measure of a fixed point on the
truck in contact with a variable electronic sensor on the
railcar.
[0219] In Example 147, the subject matter of any one or more of
Examples 117-146 optionally include wherein the system further
includes a means for detecting an anomaly as overheating hearings,
wherein the evaluation is based on measurements that include data
from thermistor sensor, an infrared camera, a vibration sensor, or
audio analytics.
[0220] In Example 148, the subject matter of any one or more of
Examples 117-147 optionally include wherein the system further
includes a means for detecting an anomaly as a broken axle based on
the two wheels of one axle rotating independently determination,
wherein the measurement includes tachometers placed next to each
wheel on the same axle.
[0221] In Example 149, the subject matter of any one or more of
Examples 117-148 optionally include wherein the system further
includes a means for detecting an anomaly as the wheels being out
of balance based on a warped wheel determination, wherein the
measurements include a wobble in either horizontal or vertical
directions using an inertia sensor.
[0222] In Example 150, the subject matter of any one or more of
Examples 117-149 optionally include wherein the system further
includes a means for detecting an anomaly as a wheel with one or
more flat spots, wherein the evaluation is based on measurements
from the sensor array including an inertia sensor, an acoustic
sensor, an imaging sensor, or a physical sensor.
[0223] In Example 151, the subject matter of any one or more of
Examples 117-150 optionally include wherein the system further
includes a means for detecting an anomaly as excessive wheel wear,
wherein the evaluation is based on measurements that include
collected images taken from an attached camera.
[0224] In Example 152, the subject matter of any one or more of
Examples 117-151 optionally include wherein the system further
includes a means for detecting an anomaly as truck hunting based on
the truck pivoting while the train is on a straight segment of
track, wherein the measurements include detected railcar
oscillation by an inertia sensor.
[0225] In Example 153, the subject matter of any one or more of
Examples 117-152 optionally include wherein the system further
includes a means for detecting an anomaly as truck hunting based on
the truck pivoting while the train is on a straight segment of
track, wherein the measurements also comprise measuring wheel
flange hits with an acoustic sensor.
[0226] The above detailed description includes references to the
accompanying drawings, which form a part of the detailed
description. The drawings show, by way of illustration, specific
embodiments that may be practiced. These embodiments are also
referred to herein as "examples." Such examples may include
elements in addition to those shown or described. However, the
present inventors also contemplate examples in which only those
elements shown or described are provided. Moreover, the present
inventors also contemplate examples using any combination or
permutation of those elements shown or described (or one or more
aspects thereof), either with respect to a particular example (or
one or more aspects thereof), or with respect to other examples (or
one or more aspects thereof) shown or described herein.
[0227] All publications, patents, and patent documents referred to
in this document are incorporated by reference herein in their
entirety, as though individually incorporated by reference. In the
event of inconsistent usages between this document and those
documents so incorporated by reference, the usage in the
incorporated reference(s) should be considered supplementary to
that of this document; for irreconcilable inconsistencies, the
usage in this document controls.
[0228] In this document, the terms "a" or "an" are used, as is
common in patent documents, to include one or more than one,
independent of any other instances or usages of "at least one" or
"one or more." in this document, the term "or" is used to refer to
a nonexclusive or, such that "A or B" includes "A but not B," "B
but not A," and "A and B," unless otherwise indicated. In the
appended claims, the terms "including" and "in which" are used as
the plain-English equivalents of the respective terms "comprising"
and "wherein." Also, in the following claims, the terms "including"
and "comprising" are open-ended, that is, a system, device,
article, or process that includes elements in addition to those
listed after such a term in a claim are still deemed to fall within
the scope of that claim. Moreover, in the following claims, the
terms "first," "second," and "third," etc. are used merely as
labels, and are not intended to impose numerical requirements on
their objects.
[0229] The above description is intended to he illustrative, and
not restrictive. For example, the above-described examples (or one
or more aspects thereof) may be used in combination with each
other. Other embodiments may be used, such as by one of ordinary
skill in the art upon reviewing the above description. The Abstract
is to allow the reader to quickly ascertain the nature of the
technical disclosure and is submitted with the understanding that
it will not be used to interpret or limit the scope or meaning of
the claims. Also, in the above Detailed Description, various
features may be grouped together to streamline the disclosure. This
should not be interpreted as intending that an unclaimed disclosed
feature is essential to any claim. Rather, inventive subject matter
may lie in less than all features of a particular disclosed
embodiment. Thus, the following claims are hereby incorporated into
the Detailed Description, with each claim standing on its own as a
separate embodiment. The scope of the embodiments should be
determined with reference to the appended claims, along with the
full scope of equivalents to which such claims are entitled.
* * * * *