Vehicle-mounted hot rail dislocation detection system and method thereof

Abstract

Therefore, the invention provides a vehicle-mounted hot rail dislocation detection system and method. The system may detect signs and symptoms of (edge) track thermal misalignment using on-board locomotive sensors mounted on train end equipment. Once detected, an alarm is issued to prevent potential derailment. The system may also include forward and backward imaging sensors (e.g., cameras, LiDAR sensors, etc.). The system may communicate with a traction locomotive via wireless communication, such as radio, to ensure appropriate air pressure and location. The system may be powered by an on-board battery and / or a pneumatic device. Advantageously, the system can calculate whether any track deviation is significant (e.g., through one or more thresholds). The system may also utilize image processing functions performed by one or more processors to determine centerlines and track spacing.

Description

Technical Field

[0001] The present disclosure relates generally to rail alignment detection, and more particularly to an on-board hot rail misalignment detection system. Background Art

[0002] About 1,200-1,400 trains run every day. Thermal misalignment (also known as track buckling or seat buckling) is a failure of rail infrastructure (such as the roadbed of a railroad track) that often has catastrophic consequences. Such events often cause severe damage and even loss of life. Since 2019, derailments due to track buckling that are reportable under the FRA (Federal Railroad Act) have caused approximately $26 million in losses. Track buckling is caused by the softening and loss of rigidity of the roadbed as the track expands thermally. Typically, the first train in the area causes the softened roadbed to collapse, but the additional weight usually stabilizes the track enough to allow the first train to pass. After the first train passes, the roadbed will deform immediately after the train leaves, leaving behind the deformed track. Therefore, most track buckling occurs after the first train passes. Railroad infrastructure is very consistent and generally always conforms to known safe train operation scenarios. Track buckling does not meet this consistency. Summary of the invention

[0003] The present disclosure achieves technical advantages as an on-board hot rail misalignment detection system method. The system can use on-board locomotive sensors that are operably coupled to the train end device to detect signs and symptoms of (edge) track thermal misalignment. Once detected, an alarm is sounded to prevent potential derailment. In one embodiment, a train end device (ETD) can be installed on each train. The ETD is a self-powered system suitable for all trains (such as the rear locomotive) operating in the event of a power outage. The ETD can be a compact device similar to the ETD LiDAR sensor. The LiDAR sensor can generate highly accurate 3-D ground data using the principles of light ranging and reflection. The price of LiDAR sensors is rapidly decreasing, and solid-state packaging is used to improve reliability. The system can also include forward and backward imaging sensors (e.g., cameras, LiDAR sensors, etc.). The system can communicate with the traction locomotive via wireless communication (e.g., radio) to ensure proper air pressure and position. The system can be powered by an on-board battery and or air pressure.

[0004] Advantageously, the system can calculate whether the deviation between the tracks has reached a significant deviation (e.g., reached one or more thresholds). The system can also utilize image processing functions performed by one or more processors to determine the centerline and track spacing. Such calculations can be performed as the train passes through a section of track. Such image processing and calculations can be performed by an edge processor (a processor close to the imaging sensor). Edge processing can determine "good" and "non-compliant" standards (anomaly detection), which reduces detection time and infrastructure costs compared to wayside calculations. The system can utilize a rear-facing sensor system (imaging sensor and edge computing device) in conjunction with an existing ETD as an optional power source to perform track buckling detection.

[0005] The present invention is directed to a hot rail misalignment detection system. These and other objects are provided by at least the following embodiments.

[0006] In one embodiment, a hot rail misalignment detection system may include: an onboard locomotive sensor configured to generate a track image; an onboard processor operably coupled to a camera and configured to perform the following tasks: analyzing the image to determine a centerline between the rails; calculating the rail spacing; and determining whether the deviation between the rails has reached a significant deviation based on one or more thresholds. Also included is generating a notification to indicate that a significant deviation exists. Also included is detecting signs and symptoms of hot rail misalignment at the edge. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The present disclosure will be easily understood by the following detailed description in combination with the accompanying drawings, which illustrate the principles of the present disclosure by way of example. The accompanying drawings show the design and practicality of one or more exemplary embodiments of the present disclosure, wherein the same elements are represented by the same reference numerals or symbols. The objects and elements in the accompanying drawings are not necessarily drawn in proportion, ratio or precise positional relationship. Instead, the focus is on illustrating the principles of the present disclosure.

[0008] Figure 1 is a cross-sectional view of a conventional track infrastructure including a train drawn according to one or more exemplary embodiments of the present disclosure;

[0009] Figure 2A and 2B is a perspective view of a track thermal misalignment drawn according to one or more exemplary embodiments of the present disclosure;

[0010] Figure 3 is a schematic diagram of a vehicle-mounted hot rail misalignment system drawn according to one or more exemplary embodiments of the present disclosure;

[0011] Figure 4is a perspective view of an on-board hot rail misalignment system disposed at the rear of a train drawn according to one or more exemplary embodiments of the present disclosure;

[0012] Figure 5 is a side view schematic diagram of a vehicle-mounted hot rail misalignment system drawn according to one or more exemplary embodiments of the present disclosure; and

[0013] Figure 6 is a perspective view of an on-board hot rail misalignment system disposed at the front of a train according to one or more exemplary embodiments of the present disclosure. DETAILED DESCRIPTION

[0014] The present disclosure and the various features and advantages details therein set forth in the description below will be more fully explained by reference to the non-limiting examples contained in the accompanying drawings and the detailed description that follows. Descriptions of well-known components are omitted herein so as not to unnecessarily obscure the main features described herein. The following examples are intended to facilitate understanding of the implementation and practice of the present disclosure. Those of ordinary skill in the art will understand that the present disclosure means that any suitable combination of the following functions or exemplary embodiments can be combined to achieve the claimed subject matter. The present disclosure includes the number of representative species within the genus, or structural features common to members of the genus, so that those of ordinary skill in the art can see or identify members of the genus. Therefore, these examples should not be construed as limiting the scope of the claims.

[0015] Figure 11 is a cross-sectional view of a conventional railway infrastructure including a train, generally referred to as 100, drawn according to one or more exemplary embodiments of the present disclosure. Conventional railway infrastructure may include ballast 102, anchors 104, sleepers 106, and tracks 108. A train 110 may travel along the track 108. The structure of the railway track system generally holds all elements in place for the train 110 to travel. However, when the railway track system is heated, the steel of the track 108 expands, but the ballast 102, sleepers 106, and anchors 104 keep the track 108 in place. Even a temperature change of one or two degrees can cause the expansion stress to push back and then bend outward, and actual temperature fluctuations are usually 20 to 40 degrees higher than the ambient temperature. Therefore, once the railway infrastructure fails, the track 108 is prone to expansion. Usually, the failure occurs when a train is traveling on the track. When the first train passes over the track, it disturbs the ballast 102. The ballast 102 is usually jagged rock or gravel. The sawtooth shape allows the ballast 102 to interlock. However, over time, the ballast 102 gradually rounds and the track loses its structural rigidity. When a train passes over the track 108 above the ballast 102, the ballast 102 may round due to low-level vibrations. As the structural rigidity decreases, the track infrastructure can no longer balance the expansion forces and the anchors 104 begin to move, causing the track 108 to bend, deform, and generally become misaligned.

[0016] Figure 2A and 2B is a perspective view of track thermal misalignment according to one or more exemplary embodiments of the present disclosure. When railway infrastructure is no longer a rigid structure, it can bend more. Figure 2A Indicates track buckling events that could be severe enough to cause a train derailment. Figure 2B Indicates track buckling events severe enough to cause a train derailment.

[0017] Figure 3 3 is a schematic diagram of a vehicle-mounted hot rail misalignment system 300 drawn according to one or more exemplary embodiments of the present disclosure. The vehicle-mounted hot rail misalignment system 300 may include one or more components, such as a first sensor module 302 including a first imaging sensor 304 and a second imaging sensor 306, a second sensor module 308 including a first imaging sensor 310 and a second imaging sensor 312, a GPS device 314, a processor 316, an inertial sensor 318, a battery 320, a solar panel 322, a universal mounting system 324, and a network device 326, among others.

[0018] In one embodiment, the first sensor module 302 may include a housing configured to position multiple imaging sensors to observe the first track from different angles. For example, the first imaging sensor 304 may be positioned at a first angle below the parallel line of the first track to observe the first track, and the second imaging sensor 306 may be positioned at a second angle below the parallel line of the first track to observe the first track. In another embodiment, the first imaging sensor 304 and the second imaging sensor 306 of the first sensor module 302 may be positioned vertically to observe the first track with a larger field of view. In another embodiment, the imaging sensors 304 and 306 may be cameras, LiDAR sensors, RADAR sensors, ultrasonic sensors, acoustic sensors, or other suitable sensor types or combinations thereof. In another embodiment, the second sensor module 308 may include a housing configured to position multiple imaging sensors to observe the second track from different angles. For example, the first imaging sensor 310 may be positioned at a first angle below the parallel line of the second track to observe the second track, and the second imaging sensor 312 may be positioned at a second angle below the parallel line of the second track to observe the second track. In another embodiment, the first imaging sensor 310 and the second imaging sensor 312 of the second sensor module 308 can be positioned vertically to observe the second track with a larger field of view. In another embodiment, the imaging sensors 310 and 312 can be cameras, LiDAR sensors, RADAR sensors, ultrasonic sensors, acoustic sensors, or other suitable sensor types or combinations thereof. In another embodiment, more than two imaging sensors can be positioned vertically at different viewing angles. In another embodiment, the imaging sensors 304, 306, 310, and 312 are solid-state sensors. In another example, the image sensor can generate imaging data. For example, the imaging data can include one or more images associated with the track generated by a camera, one or more point clouds associated with the track generated by a LiDAR sensor, and the like. The point cloud can represent one or more railway infrastructure elements. In another embodiment, the housing can include an inertial measurement unit (IMU) and a processing unit.

[0019] In one embodiment, the system 314 may include a GPS device 314. In another embodiment, the GPS device 314 may be disposed in the first sensor module 302, the second sensor module 308, or both. For example, the GPS device 314 may receive one or more satellite signals to determine the location of the GPS device. In another embodiment, the GPS device 314 may be a high-precision GPS device, such as a GPS receiver with real-time kinematic positioning (RTK) correction capabilities.

[0020] In one embodiment, the system 300 may include one or more processors 316 operably coupled to a memory. The memory may include electronic memory, which may include non-transitory storage media that electronically stores information. The electronic storage media of the electronic memory may include one or both of a system memory that may be provided integrally with the processor 316 (e.g., substantially non-removable) and / or a removable memory that may be removably connected to the processor 316 via, for example, a port (e.g., a USB port, a FireWire port, etc.) or a drive (e.g., a disk drive, etc.). The electronic memory may include one or more optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard disk, floppy disk drive, etc.), charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drives, etc.), and / or other electronically readable storage media. The electronic storage may include one or more virtual storage resources (e.g., cloud storage, virtual private networks, and / or other virtual storage resources). The electronic storage may include a database, or a public or private distributed ledger (e.g., blockchain). Electronic storage may store machine-readable instructions, software algorithms, control logic, data generated by a processor, data received from a server, data received from a computing platform, and / or other data that may enable a server to operate as described herein. Electronic storage may also include third-party databases accessible over a network.

[0021] Processor 316 may be configured to provide data processing capabilities for server 102. Thus, processor 316 may include one or more digital processors, analog processors, digital circuits designed to process information, analog circuits designed to process information, state machines, and / or other mechanisms for electronically processing information, such as FPGAs (field programmable gate arrays), ASICs (application specific integrated circuits), Raspberry Pis, controllers, or other suitable devices. Processor 316 may be a single entity, or include multiple processing units. These processing units may be physically located within the same device, or processor 316 may represent processing functions of multiple devices, or software functions that operate independently or in conjunction.

[0022] The processor 316 may be configured to execute machine-readable instructions or machine learning modules through software, hardware, firmware, some combination of software, hardware and / or firmware, and / or other mechanisms for configuring processing capabilities on the processor 316. The term "machine-readable instructions" as used herein may refer to any component or set of components that perform the functionality attributed to the machine-readable instruction component. This may include one or more physical processors 316, processor-readable instructions, circuits, hardware, storage media, or any other component during the execution of processor-readable instructions.

[0023] Processor 316 can be configured as machine-readable instructions with one or more functional modules. Machine-readable instructions can be implemented on one or more processors 316 that can access memory. Machine-readable instructions can be a single networked node or a machine cluster, which can include a distributed architecture of multiple networked nodes. Machine-readable instructions may include control logic for implementing various functions, as described in more detail below. Machine-readable instructions may include certain functions related to system 300. In addition, machine-readable instructions may include smart contracts or multi-signature contracts that can process, read and write data to a database, distributed ledger or blockchain.

[0024] Advantageously, the processor 316 can be an edge processor disposed near the first and second imaging sensors to provide real-time (sub-second) processing of the generated data. The processor 316 can be a Raspberry Pi or Arduino type processor. For example, the track geometry is easy to understand, and when the collected data (e.g., LiDAR point cloud, image, etc.) no longer meets the expected geometry, the edge device can quickly process the generated LiDAR data to complete such a determination. Although the processor 316 can be any type of processor, the edge processor can provide processing at the location where detection is required, thereby reducing the requirement to transmit the generated data from the generation point to a centralized location (e.g., cloud, data center, etc.), and the throughput can be increased without considering bandwidth. By locating the processing power at the edge (close to the sensor data generation), a smaller analytical determination payload can be transmitted instead of a relatively large generated data payload, so that binary instructions (yes / no type answers, alarms, etc.) can be transmitted to other locations.

[0025] In one embodiment, system 300 may include network device 326 to connect to a network. The network device may include a wireless communication transceiver, and one or more wired communication ports. The above-mentioned system components may be coupled to each other in a communication manner through a network to achieve data transmission. The network may be a system bus, the Internet, an intranet, a cellular network, or other suitable networks. Data transmission may be encrypted, unencrypted, through a VPN tunnel, or other suitable communication methods. The network may be a WAN, a LAN, a PAN, or other suitable network types. Network communication between any system components may be encrypted using PGP, Blowfish, Twofish, AES, 3DES, HTTPS, or other suitable encryption methods. System 300 may be configured to provide communication via an application programming interface (API), PCI, PCI-Express, ANSI-X12, Ethernet, cellular network, Wi-Fi, Bluetooth, or other suitable communication protocols or media through various systems, components, and modules disclosed in the present disclosure. The system 300 may provide bidirectional data communication between networked components through one or more libraries, programs, protocols (e.g., FTP, sFTP, etc.), or APIs (e.g., HTTP Live Streaming (HLS), Web Real-Time Communication (WebRTC), HTTP Dynamic Streaming (HDS), MPEG-DASH, etc.). In addition, third-party systems and databases may be operably coupled to system components via a network.

[0026] In one embodiment, the system 300 may include inertial sensors 318 to provide inertial monitoring. For example, when a train travels across a track, the system 300 and its components will vibrate and move (e.g., bumps, braking, etc.), and such vibrations and movements should be monitored to compensate for the transition motion in the processed data so that the processed data can function properly. In another embodiment, the inertial sensor 318 may be an inertial navigation system (INS), which may integrate accelerometers, gyroscopes, and other sensors with a global positioning system (GPS / GNSS) and other orientation values ​​to estimate the position, velocity, heading, and orientation of an object in space.

[0027] In one embodiment, the system 300 may include a battery 320. For example, the battery 320 may be an alkaline battery, a nickel metal hydride battery (NIMH), or a lithium ion battery, etc. The battery 320 may also be rechargeable or recharged by a train power source. The battery 320 may be disposed in a housing and operably coupled to components of the system 300 to power one or more components of the system 300 and to obtain power from one or more system components (e.g., solar panels 322). In another embodiment, the system 300 may be powered by an existing train power source (e.g., a device that can generate power based on air pressure or an existing train power outlet, etc.).

[0028] In one embodiment, the system 300 may include a solar panel 322. For example, the solar panel 322 may be a monocrystalline, polycrystalline, or thin-film (amorphous) solar panel, etc. The solar panel may be detachable, foldable, positionable, or any combination thereof. The solar panel 322 may be operably coupled to the housing. For example, one or more articulated arms may positionally couple the solar panel 322 to the housing. The solar panel 322 may also be disposed on one or more surfaces of the housing. The solar panel 322 may be operably coupled to one or more components of the system 300 to power one or more components of the system 300 (e.g., component operation or battery 320 charging).

[0029] In one embodiment, the system 300 may include a universal mounting system 324 to couple the system 300 to a rail vehicle. For example, the universal mounting system may include a bracket, a vise, a magnet, a brace, or other suitable mechanism to secure the system 300 to the rail vehicle. The universal mounting system 324 is removable. For example, a single person may connect and reconnect the on-board hot rail misalignment detection system 300 to the end of a rail vehicle 402. In fact, any rail vehicle 402 could theoretically be the last car of a train. Regardless of the type of rail vehicle, the system 300 can be mounted to the rail vehicle via the universal mounting system 324 that is removably coupled to the rear of any rail vehicle 402.

[0030] Figure 4 is a perspective view of an on-board hot rail misalignment system disposed near the rear of a train, generally referred to as 400, according to one or more exemplary embodiments of the present disclosure. When a rail vehicle 402 (e.g., a rail car, etc.) traverses a railroad track having a first rail 404 and a second rail 406, the system 400 may include one or more first and second imaging sensors directed toward one or more rails (or portions thereof) of the railroad track.

[0031] In one embodiment, the first sensor module 302 can be operably coupled to the rail vehicle 402. The first sensor module 302 can include a first imaging sensor 304 and a second imaging sensor 306. The first sensor module 302 can be disposed near a first wheel of the rail vehicle 402 to obtain a first track view of the first track 404. For example, the first view can be centered so that the first track 404 bisects the first track view. The first sensor module 302 can also be disposed at any location near the rear of the rail vehicle 402 and positioned to have a first track view showing at least a portion of the first track 404. The first track view can be bisected by the first track 404 or in any other suitable bisecting ratio.

[0032] In one embodiment, the second sensor module 308 can be operably coupled to the rail vehicle 402. The second sensor module 308 can include the first imaging sensor 310 and the second imaging sensor 312. The second sensor module 308 can be disposed near a second wheel of the rail vehicle 402 to obtain a second track view of the second track 406. For example, the second view can be centered so that the second track 406 bisects the second track view. The second sensor module 308 can also be disposed at any location near the rear of the rail vehicle 402 and positioned to have a second track view showing at least a portion of the first track 404. The second track view can be bisected by the second track 406 or in any other suitable bisecting ratio.

[0033] Thus, when the rail vehicle traverses the railroad track, the first sensor module 302 may acquire one or more images or videos of each section of the first track 404 in the first track view, and the second sensor module 308 may acquire one or more images or videos of each section of the second track 404 in the first track view.

[0034] Figure 5 is a schematic side view of an on-board hot rail misalignment system, generally referred to as 500, drawn in accordance with one or more exemplary embodiments of the present disclosure. A first imaging sensor 310 may be positioned to have a first second rail view 502 of a first portion 506 of a second rail 406. A second imaging sensor 312 may be positioned to have a second second rail view 504 of a second portion 508 of a second rail 406. The first imaging sensor 310 and the second imaging sensor 312 may be positioned within a first sensor module (not shown for clarity) to selectively determine the first second rail view 502 and the second second rail view 504. In another embodiment, the first imaging sensors 304, 310 may be positioned parallel to the rails 404, 406, and the second imaging sensors 306, 312 may be tilted downward by 30-40 degrees from parallel. One or more sensors may be tilted toward the rails. The first second rail view 502 and the second second rail view 504 may at least partially overlap, such that at least a portion of the first portion 506 and the second portion 508 of the second rail 406 may be visible to the first imaging sensor 310 and the second imaging sensor 312. Due to Figure 5 As a side view, the present description refers to the second sensor module and its components, however, the principles disclosed herein are equally applicable to the first sensor module 302 and its components and any additional sensor modules that may be added to the system 300. In one embodiment, the first imaging sensor 304 and the second imaging sensor 306 may be positioned to collect data associated with the first track 404 in order to have two test points on the track.

[0035] In operation, when the rail vehicle crosses the railway track, the on-board hot rail misalignment detection system acquires images, point clouds or videos, and the on-board edge processor can analyze the acquired images for track alignment. Most track buckling derailment accidents occur on tangential flow tracks. Therefore, the data collected by the first and second sensor modules 302, 308 of the first and second tracks 404, 406 can determine whether the track deviates from consistency. In one embodiment, the system 300 can use imaging data to determine the centerline between the first track 404 and the second track 406. For example, the centerline between the two tracks can be calculated by determining the distance between the two tracks and dividing the distance by two. In another embodiment, the distance between the tracks 404, 406 and the previously determined distance between the tracks 404, 406 can be compared to determine the deviation. In another embodiment, the distance from the track 404, 406 to the centerline can be compared to the distance from the track 404, 406 to the centerline previously determined to determine the deviation. For tangent (straight) tracks, any deviation from the straight line should be below the abnormal threshold to ensure track safety. The track behind the rail vehicle 402 should be straight to avoid any curvature that is unacceptable due to the speed of the train. For example, if the train is traveling at 79 miles per hour, there should be no 6-degree or 4-degree curves behind the vehicle. At 79 miles per hour, the system 300 may detect a 1-1 / 2 degree curve behind the rail vehicle 402, but such a curve may be below the abnormal threshold and therefore safe to travel. If a higher degree of curvature begins to appear, it is abnormal. When the system 300 generates these measurements based on the index of the rated speed of the track through edge detection, the system can take into account the speed of the train. In one case, the rated travel speed of a section of track may be 79 miles per hour, but due to excessive traffic on the line, the train travel speed on the section of track may be 40 miles per hour. Each section of track can have an associated speed index, that is, an acceptable speed curvature. The system 300 can access a timetable database containing track ratings that can be accessed by the processor 316. Therefore, the abnormal threshold can be an adaptive threshold that takes into account the speed index of a section of track and the measured deviation from the center of the track.

[0036] The system 300 can calculate whether the deviation between the tracks reaches a significant deviation. The system 300 can also determine the centerline of the track and the distance between the tracks. Such calculations can be performed when the rail vehicle passes through a section of track, and notifications can be generated and sent (if necessary). The system 300 can combine data from the inertial sensor 318 and normalize the generated data before comparison to eliminate any changes caused by the data generation itself (e.g., bumps, braking, etc.). In one embodiment, the system 300 can compare two or more data sets generated by the first and second imaging sensors 304, 306, 310, 312 or other suitable devices in real time. For example, the data sets can be images, LiDAR point clouds, video clips, or other suitable data. In another embodiment, the generated LiDAR point clouds can be superimposed to determine any differences in a section of track and the size of any differences. If the difference exceeds an adaptive anomaly threshold (based at least on the deviation of the track from the center and the speed index), the system 300 can generate an alert (using GPS device 314 data) marking the section of track and transmit the alert to one or more recipients.

[0037] Figure 6 6 is a perspective view of an onboard hot rail misalignment system disposed near the front of a train according to one or more exemplary embodiments of the present disclosure, generally referred to as 600. When a rail vehicle 402 (e.g., a locomotive, etc.) traverses a railroad track including a first rail 404 and a second rail 406, the system 600 may include one or more first and second imaging sensors directed toward one or more rails (or portions thereof) of the railroad track.

[0038] In one embodiment, the first sensor module 302 can be operably coupled to a rail vehicle 402 disposed near the front of a train. The first sensor module 302 can include a first imaging sensor 304 and a second imaging sensor 306. The first sensor module 302 can be disposed at a first height of the rail vehicle 402 to obtain a first rail view of the first track 404. For example, the first view can be centered so that the first track 404 and the second track 406 are at least partially located in the first rail view. The first sensor module 302 can also be disposed at any position near the rear of the rail vehicle 402 and positioned to have a first rail view showing at least a portion of the first track 404. The first rail view can be bisected by the first track 404 or in any other suitable bisecting ratio.

[0039] In one embodiment, the second sensor module 308 may be operably coupled to the rail vehicle 402 disposed near the front of the train. The second sensor module 308 may include a first imaging sensor 310 and a second imaging sensor 312. The second sensor module 308 may be disposed at a second height of the rail vehicle 402 to obtain a second track view of at least a portion of the second track 406. For example, the second view may be centered so that the second track 406 and the first track 404 are at least partially located in the second track view. The second sensor module 308 may also be disposed at any location near the rear of the rail vehicle shovel 402 and positioned to have a second track view showing at least a portion of the first track 404. The second track view may be bisected by the second track 406 or in any other suitable bisecting ratio. Thus, as the rail vehicle traverses the railroad track, the first sensor module 302 may acquire one or more images or videos of at least each segment of the first track 404 in the first track view, and the second sensor module 308 may acquire one or more images or videos of at least each segment of the second track 404 in the first track view. Different heights may provide different views of the first and second tracks.

[0040] In another embodiment, the third sensor module 602 can be operably coupled to the rail vehicle 402 disposed near the front of the train. The third sensor module 602 can include the first imaging sensors 304, 310 and the second imaging sensors 306, 312. The third sensor module 602 can be disposed at a third height of the rail vehicle 402 to obtain first and second rail views of at least a portion of the first rail 404 and the second rail 406. For example, the first view can be centered so that the first rail 404 and the second rail 406 are at least partially located in the first and second rail views. The first view can be centered so that the first rail 404 is at least partially located in the first rail view. The second view can be centered so that the second rail 406 is at least partially located in the second rail view. The third sensor module 308 can also be disposed at any position near the front of the rail vehicle 402. The first imaging sensor 304 and the second imaging sensor 306 can be oriented toward the first rail 404. The first imaging sensor 310 and the second imaging sensor 312 can be oriented toward the second rail 406. The first rail view can be bisected by the first rail 404 or in any other suitable bisecting ratio. The second track view may be bisected by the second track 406 or by any other suitable bisection ratio. Thus, as the rail vehicle 402 traverses the railroad track, the third sensor module 602 may acquire one or more images or videos of at least the first track 404 within the first track view and each segment of the second track 404 within the first track view. In another embodiment, the imaging sensors 304, 306, 310, 312 may be tilted laterally to obtain views of different tracks. For example, the first imaging sensors 304, 310 may be tilted toward the first track 404, and the second imaging sensors 306, 312 may be tilted toward the second track 406. In another embodiment, the imaging sensors 304, 306, 310, 312 may be tilted vertically to obtain different views of a particular track.

[0041] Those skilled in the art will readily appreciate that without the specific combination of computer hardware and other structural components and mechanisms assembled in the system of the present invention and described herein, the above advantages and purposes will not be possible to achieve. In addition, the algorithms, methods and processes disclosed herein improve and convert any general-purpose computer or processor disclosed in this specification and the accompanying drawings into a special-purpose computer, which is programmed to execute the disclosed algorithms, methods and processes to achieve the above functions, advantages and goals. It should also be understood that for those who are proficient in this field, there are a variety of programming tools that can be used to generate and implement the functions and operations described above. In addition, the selection of a specific programming tool may be determined by the specific goals and constraints of implementing these concepts, which are selected to implement the concepts proposed in this article and the appended claims.

[0042] The description in this patent document should not be construed as implying that any particular element, step, or function is an essential or critical element that must be included within the scope of the claims. In addition, unless the exact words "means for" or "step for" are expressly used in a particular claim and followed by a participatory phrase identifying the function, no claim is intended to invoke 35 U.S.C. §112(f) for any additional claim or claim element. Terms used in the claims (such as (but not limited to) "mechanism", "module", "device", "unit", "component", "element", "member", "device", "machine", "system", "processor", "processing device", or "controller") should be understood and intended to refer to structures known to those skilled in the relevant art and are further modified or enhanced by the features of the claims themselves, and are not intended to invoke 35 U.S.C. §112(f). Even under the broadest reasonable interpretation, in accordance with this paragraph of the specification, the claims are not intended to invoke 35 U.S.C. §112(f) in the absence of the above specific language.

[0043] The present disclosure may be embodied in other specific forms without departing from its spirit or essential features. For example, each new structure described herein may be modified to accommodate specific local changes or requirements while retaining their basic configuration or structural relationship to each other, or while performing the same or similar functions described herein. Therefore, the present embodiments should be considered illustrative rather than restrictive in all respects. Therefore, the scope of the invention should be determined by the appended claims rather than by the foregoing description. Therefore, all changes within the meaning and equivalent scope of the claims should be included in the claims. In addition, the various elements in the claims are not well-known, conventional or traditional. Instead, the claims point to the non-traditional innovative concepts described in the specification.

Claims

1. A hot rail misalignment detection system, comprising: A first sensor module, operably coupled to a rail vehicle, comprises: a first imaging sensor positioned to have a first track view of a first segment of a first track and configured to generate first imaging data associated with the first track, and a second imaging sensor positioned to have a second track view of a second segment of the first track and configured to generate second imaging data associated with the first track, and The edge processor, disposed proximate the first sensor module, is configured to receive and compare the first and second imaging data to determine whether a difference between the first and second data exceeds an adaptive anomaly threshold.

2. The system of claim 1, wherein the adaptive anomaly threshold varies depending on the measured track deviation.

3. The system of claim 2, wherein the measured deviation is calculated by the edge processor using a distance between the first track and the second track.

4. The system of claim 2, wherein the measured deviation is calculated by the edge processor using a distance between the first track and a centerline between the tracks.

5. The system of claim 1, further comprising a GPS device configured to receive one or more satellite signals to determine the location of the rail vehicle.

6. The system of claim 5, wherein the edge processor can use the rail vehicle position to determine a speed index for a particular track section.

7. The system of claim 6, wherein the adaptive anomaly threshold value differs according to a speed index of the rail vehicle location.

8. The system of claim 1, wherein the first imaging sensor and the second imaging sensor are positionable within the first sensor module to selectively determine the first track view and the second track view.

9. The system of claim 8, wherein the first imaging sensor is positioned parallel to the first track.

10. The system of claim 8, wherein the second imaging sensor is positioned tilted downward from a position parallel to the first track.

11. The system of claim 1 , further comprising: The second sensor module comprises: a third imaging sensor positioned to have a third track view of the first segment of the second track and configured to generate third imaging data associated with the second track, and A fourth imaging sensor is positioned to have a fourth track view of the second segment of the second track and is configured to generate fourth imaging data associated with the second track.

12. The system of claim 11, wherein the edge processor is disposed proximate the first and second sensor modules and is configured to receive and compare the third and fourth imaging data to determine whether any difference between the third and fourth imaging data exceeds an adaptive anomaly threshold.

13. The system of claim 11, wherein a third imaging sensor and a fourth imaging sensor are positionable within the second sensor module to selectively determine a third orbital view and a fourth orbital view.

14. The system of claim 11, wherein the first imaging sensor is positioned parallel to the first track.

15. The system of claim 11, wherein the second imaging sensor is positioned tilted downward from a position parallel to the first track.

16. The system of claim 1, wherein the edge processor generates a notification to indicate that a significant deviation exists.

17. A hot rail misalignment detection system, comprising: A first sensor module, operably coupled to a rail vehicle, comprises: a first imaging sensor positioned to have a first track view of a first segment of a first track and configured to generate first imaging data associated with the first track, and a second imaging sensor positioned to have a second track view of a second portion of the first track and configured to generate second image data associated with the first track; The second sensor module comprises: a third imaging sensor positioned to have a third track view of the first segment of the second track and configured to generate third imaging data associated with the second track, and a fourth imaging sensor positioned to have a fourth track view of a second segment of the second track and configured to generate fourth imaging data associated with the second track; inertial sensors configured to provide estimates of system velocity, heading, and orientation; and The edge processor, disposed proximate the first sensor module, is configured to receive and compare the first and second imaging data to determine whether a difference between the first and second data exceeds an adaptive anomaly threshold.

18. The system of claim 17, further comprising a battery.

19. The system of claim 17, further comprising a solar panel.

20. The system of claim 17, further comprising a universal mounting system configured to couple the system to a rail vehicle.

Images