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.

What is claimed is: 
     
         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.