On-board thermal track misalignment detection system and method therefor
An on-board thermal track misalignment detection system method therefor is presented. The system can use on-board locomotive sensors attached to an end-of-train device to detect (on the edge), signs and symptoms of thermal misalignments of the track. Once detected an alert can be transmitted to prevent potential derailments. The system can also include a forward-facing and rearward-facing imaging sensors (e.g., camera, LiDAR sensor, etc). The system can wirelessly communicate (e.g., via radio) with a leading locomotive to ensure proper air pressure and location. The system can be powered by an on-board battery and/or air pressure device. Advantageously, the system can calculate whether any rail deviation is significant (e.g., via one or more threshold values). The system can also leverage image processing functionality, executed by one or more processors) to find the centerline and the distance between the tracks.
1. A method of detecting railroad track misalignment, comprising:
receiving, via a first imaging sensor coupled to a rail vehicle, a first rail view of a first section of a first rail;
generating first imaging data related to the first rail via the first imaging sensor;
receiving, via a second imaging sensor coupled to the rail vehicle, a second rail view of a second section of the first rail;
generating second imaging data related to the first rail via the second imaging sensor; and
comparing, via an edge processor, the first and second imaging data to determine whether any differences between the first and the second data exceed an adaptive anomaly threshold to detect misalignments of a railroad track.
2. The method of claim 1 , wherein the adaptive anomaly threshold varies based upon the measured deviation of the rail.
3. The method of claim 2 , wherein the measured deviation is calculated by the edge processor using a distance between the first rail and a second rail.
4. The method of claim 2 , wherein the measured deviation is calculated by the edge processor using a distance between the first rail and a centerline between rails.
5. The method of claim 1 , further comprising a GPS device operably coupled to the edge processor and configured to receive one or more satellite signals to determine the position of the rail vehicle.
6. The method of claim 5 , wherein the edge processor can determine a speed index for a particular section of track using the rail vehicle position.
7. The method of claim 6 , wherein the adaptive anomaly threshold varies based upon the speed index of the rail vehicle location.
8. The method of claim 1 , wherein the first imaging sensor and the second imaging sensor can be positioned within a first sensor module to selectively determine the first rail view and the second rail view.
9. The method of claim 8 , wherein the first imaging sensor is positioned parallel to the first rail.
10. The method of claim 8 , wherein the second imaging sensor is positioned canted downward from parallel to the first rail.
11. The method of claim 8 , further comprising:
receiving, via a third imaging sensor coupled to the rail vehicle, a third rail view of a first section of a second rail;
generating third imaging data related to the second rail via the third imaging sensor;
receiving, via a fourth imaging sensor coupled to the rail vehicle, a fourth rail view of a second section of the second rail; and
generating fourth imaging data related to the second rail via the fourth imaging sensor.
12. The method of claim 11 , wherein the edge processor is disposed proximate the first, second, third, and fourth imaging sensors and configured to receive and compare the third and fourth imaging data to determine whether any differences between the third and fourth imaging data exceed the adaptive anomaly threshold.
13. The method of claim 11 , wherein the third imaging sensor and the fourth imaging sensor can be positioned within a second sensor module to selectively determine the third rail view and the fourth rail view.
14. The method of claim 11 , wherein the first imaging sensor is positioned parallel to the first rail.
15. The method of claim 11 , wherein the second imaging sensor is positioned canted downward from parallel to the first rail.
16. The method of claim 1 , wherein the edge processor generates a notification indicating that the adaptive anomaly threshold is exceeded.
17. A method of detecting railroad track misalignments via a railroad track misalignment system, comprising:
receiving, via a first imaging sensor coupled to a rail vehicle, a first rail view of a first section of a first rail;
generating first imaging data related to the first rail via the first imaging sensor;
receiving, via a second imaging sensor coupled to the rail vehicle, a second rail view of a second section of the first rail;
generating second imaging data related to the first rail via the second imaging sensor; and
generating inertial sensor data, via an inertial sensor, to provide an estimation of a railcar's velocity, heading, and orientation; and
comparing, via an edge processor, the first and second or a third and fourth imaging data to determine whether any differences between the first and the second imaging data or the third and the fourth imaging data exceed an adaptive anomaly threshold, to detect railroad track misalignments.
18. The method of claim 17 , further comprising a solar panel or battery operably coupled to the system and configured to provide power to one or more components.
19. The method of claim 17 , wherein the edge processor removes any variations due to movement of the rail vehicle from the first, second, third, and fourth imaging data using the inertial sensor data.
20. The method of claim 17 , further comprising a universal mounting system configured to couple the system to the rail vehicle.