System and method for detecting and compensating wheel impact load

Abstract

A wheel impact load detection system is presented that detects defects in the wheels of a rail vehicle. The system can receive data from sensors and / or weather stations to determine a maximum force applied to the rail, and can subsequently calibrate the determined maximum force to account for environmental conditions. In addition, the present disclosure can assign a severity level and generate an alert having an assigned severity level, and such severity level can facilitate proper prioritization of the alerts. It is an object of the present invention to provide a system that considers variable environmental conditions and / or variable rail tensions in the assignment of severity levels to mitigate unnecessary stoppages of rail traffic.

Description

Technical Field

[0001] The present disclosure generally relates to detecting wheel impact loads in railway infrastructure.

Background Art

[0002] In current railway infrastructure, generally, a wheel impact load detection system is installed to notify railway personnel about potential defects in the wheels of vehicles moving on railway tracks. Generally, a wheel impact load detection system includes a strain gauge coupled to a rail that is operable to measure strain or stress applied to the rail. As a train or other vehicle moves over the portion of the rail where the strain gauge is mounted, the strain gauge can measure an increase in the strain generated by the vehicle. This increase in strain can correlate to a measured value of the force exerted on the rail by one or more wheels of the vehicle. The strain gauges are often distributed over a sufficient length of the rail to allow for the collection of force measurement values corresponding to not only a particular section but also the entire area around a given wheel.

[0003] Railway vehicles are carefully loaded to ensure that the combined weight of the vehicle and cargo does not exceed the weight limits of the track and vehicle infrastructure, and when there are no defects in the vehicle wheels or the rails on which they move, the measured values of the strain gauges will generally reflect that the trains moving on the rails are exerting acceptable forces on the rails. However, if a wheel has a defect (e.g., a flat generated by sliding or mechanical shelling, etc.), as a result, increased forces may be applied to the track by the vehicle via the defective wheel. These forces can be measured by the strain gauges and ultimately detected by an overall wheel impact load detection system, thereby warning railway personnel about potential defects.

[0004] Due to the need to maintain the movement of trains to meet schedules and supply timelines, railway systems often prioritize alerts for many potential issues, including detected wheel impact loads that notify of wheel defects. Some alerts may indicate a serious problem and require the vehicle to be immediately stopped and inspected, while some alerts may notify that the inspection can wait until the train's next maintenance. In the context of wheel impact load detection, such prioritization generally depends on the amount of force detected by strain gauges; the greater the force, the more serious the alert and the higher the priority assigned to it. As an example of this prioritization, the Association of American Railroads (AAR) has published different levels of concern corresponding to various detected force values in wheel impact load detection, and these force values are often measured in "kips," i.e., thousands of pounds. The AAR considers a wheel to have a "problem" (i.e., potentially a problem for the railroad) if the kip value of the wheel exceeds 90. In addition to the guidelines from the AAR, railroads often maintain their own internal priority system for wheels with problems to assist themselves in managing the vast amount of alerts that can be generated on any given date. However, the time constraints inherent in railroad operations mean that incorrect prioritization can be devastating to the efficiency and productivity of railroad operations. Therefore, ensuring the proper prioritization of alerts is key to the successful and reliable implementation of a wheel impact load detection system. Summary of the Invention Problems to be Solved by the Invention

[0005] The present disclosure realizes technical advantages as a system and method for wheel impact load detection (WILD) that can prioritize alerts by considering environmental conditions. The system can consider the variability of rail tension generated by environmental conditions (e.g., temperature, pressure, humidity, etc.) when assigning severity levels to alerts. The system can receive data that can notify the forces exerted on the track by the vehicle, calibrate the received data to account for environmental conditions, utilize the calibrated data when assigning severity levels that control prioritization in the railway system, and then generate alerts with the assigned severity levels.

[0006] The present disclosure solves the technical problem of providing a wheel impact load detection system configured to consider environmental conditions when prioritizing generated alerts. The present disclosure can calculate values from the received data, thereby calibrating relevant values using the received data, determining when to use the original or calibrated data, and comparing the original or calibrated values to a predefined threshold to facilitate appropriate prioritization (as appropriate). Such specialized processing can provide the benefit of increased operational efficiency of the railway system by reducing mis-prioritized alerts that may be generated, for example, by an increase in rail tension due to linear shrinkage of the rail during a cold wave.

[0007] The present disclosure improves the performance and functionality of the system itself by implementing specialized algorithms adapted to data related to environmental conditions in the vicinity of sensors (e.g., strain gauges, load cells, accelerometers, etc.). The system can assign severity levels related to the received WILD sensor data in view of the environmental conditions related to the received environmental data. In contrast, conventional systems often simply rely on incomplete data and assume uniform conditions without adjusting values based on feedback values, for example, to account for variable rail tensions and / or linear contractions / expansions, resulting in inappropriate prioritized alerts that can reduce operating efficiency and often lead to tragic stops of mostly sound railway vehicles. In certain embodiments, the disclosed WILD system not only determines when data needs to be calibrated but can also determine whether the severity level of an alert should be changed by the calibration.

[0008] The disclosed WILD system can include a database, a client, and a server in operable communication with a weather station. The WILD system can further be in operable connection with a plurality of sensors or gauges designed to measure one or more forces exerted on the track by the vehicle. The WILD system can generate a record containing relevant data including the nature of the vehicle, the time and date of detection, the direction in which the vehicle was moving when detected, the determined weight of the vehicle, the determined maximum force applied by the vehicle to the track, the percentage of the maximum force that can be attributed to a vehicle defect, and the environmental conditions at the time of detection.

[0009] One object of the present disclosure is to provide a system for wheel impact load detection and alert generation. A further object of the present disclosure is to provide a method for generating prioritized alerts related to wheel impact load detection. These and other objects are provided by at least the following embodiments.

Means for Solving the Problems

[0010] In one embodiment, a system for generating a railway alert related to wheel impact load detection sensor data can include a memory having a first database with a plurality of sensor data, thresholds, and specifications related to at least a portion of a vehicle and a track, and a network-connected computer processor operably coupled to the memory and having the ability to execute machine-readable instructions to perform program steps. The program steps can include detecting a vehicle on the track, receiving environmental data, receiving sensor data corresponding to one or more forces exerted on the track by the vehicle, determining an original maximum peak force from the sensor data, comparing the original maximum peak force with a first force threshold, determining whether the environmental data meets an environmental threshold when the original maximum peak force exceeds the first force threshold, generating a calibrated maximum peak force via the processor when the environmental data meets the environmental threshold, using the original maximum peak force or the calibrated maximum peak force when assigning a severity level, and generating an alert including the severity level. And when the original maximum peak force is less than the first force threshold, no alert is generated. In this case, the calibrated maximum peak force is generated by normalizing the original maximum peak force using an operating variable. In this case, the program steps can further include generating a plot of the sensor data, comparing a plurality of points on the plot corresponding to the sensor data, recognizing a static peak force trend within the plot, determining a weight value using the static peak force trend, and calculating a dynamic force value using the original maximum peak force and the weight value. In this case, the program steps can further include determining a confidence level of the sensor data accuracy. In this case, the program steps can further include using the confidence level when assigning a severity level. In this case, the severity level can vary based on the magnitude of the original maximum peak force or the calibrated maximum peak force and the confidence level.In this case, when the original maximum peak force exceeds the first force threshold and the calibrated maximum peak force is less than the first force threshold, the severity level notifies that the calibrated maximum peak force was used when assigning the severity level. In this case, the vehicle is a train. In this case, when the environmental data does not meet the environmental threshold, the original maximum peak force is used when assigning the severity level, and when the environmental data meets the environmental threshold, the calibrated maximum peak force is used when assigning the severity level. In this case, the environmental data includes weather data.

[0011] In another embodiment, a method for compensating for environmental conditions in wheel impact load detection includes detecting a train on a track, generating at least one record including a date, a time, a direction of the vehicle, and a number of axles of the vehicle via one or more processors, receiving environmental data, receiving sensor data from at least one strain gauge coupled to the track, determining an original maximum force value from the sensor data, comparing the original maximum force value with a first force threshold, generating a calibrated maximum force value by calibrating the original maximum force value with an operating variable via one or more processors when the environmental data meets an environmental threshold, using the original maximum force value to assign a first severity level when the original maximum force value exceeds the first force threshold and the calibrated maximum force value is not generated, using the calibrated maximum force value to assign a second severity level when the original maximum force value exceeds the first force threshold and the calibrated maximum force value is generated, generating an alert including the first or second severity level, and updating at least one record. In this case, when the original maximum force value is less than the first force threshold, at least one record is updated without generating an alert. In this case, the environmental data includes temperature, and the environmental threshold is a temperature threshold. The method further includes determining a confidence level in the accuracy of the sensor data. In this case, the confidence level is used with the original maximum force value to assign the first severity level. In this case, the confidence level is used with the calibrated maximum force value to assign the second severity level.

[0012] In another embodiment, a method for compensating for variable rail tension generated by environmental conditions in wheel impact load detection includes detecting a vehicle on a track, receiving environmental data, receiving sensor data from at least one strain gauge coupled to the track, determining a maximum force value from the sensor data via one or more processors, determining whether the environmental data meets an environmental threshold when the maximum force value exceeds a first force threshold, assigning a first severity level when the maximum force value exceeds the first force threshold and the environmental data meets the environmental threshold, assigning a second severity level when the maximum force value exceeds the first force threshold and the environmental data does not meet the environmental threshold, and generating an alert including the first or second severity level, where no alert is generated when the maximum force value is less than the first force threshold. The method further includes using a confidence level when assigning the first or second severity level. In this case, the confidence level is reduced when the environmental threshold is met. In this case, the environmental threshold is met when the temperature is 0°C or lower. The method further includes updating a record to notify that the alert includes the first or second severity level.

[0013] The present disclosure may be readily understood by reference to the following detailed description, which is provided in connection with the accompanying drawings that illustrate, by way of example, the principles of the present disclosure. The drawings illustrate the design and utility of one or more embodiments of the present disclosure, where like elements are referred to by like reference numerals or symbols. The objects and elements in the drawings are not necessarily drawn to exact scale, proportion, or exact positional relationship. Instead, emphasis is placed on exemplifying the principles of the present disclosure.

Brief Description of the Drawings

[0014]

Figure 1

Figure 2

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Figure 5B

Figure 6

Figure 7

[0015] By referring to the non-limiting examples included in the accompanying drawings and detailed in the following description, the preferred version of the present disclosure presented in the following description and its various features and advantageous details will be further fully described. Descriptions of well-known components are omitted so as not to unnecessarily obscure the main features described herein. The examples used in the following description are intended to facilitate an understanding of how the present disclosure can be implemented and practiced. Accordingly, these examples should not be construed as limiting the scope of the claims.

[0016] Figure 1 shows a schematic diagram of a Wheel Impact Load Detection (WILD) system 100 according to one or more embodiments of the present disclosure. The system 100 can include one or more servers 102 operatively coupled to a database 104. The server 102 can be operatively coupled to one or more clients 110, 112, 114, 116 via a network connection 106. The clients can be physical devices (e.g., a mobile phone 116, computers 110, 112, a tablet 114, a wearable device, or other suitable devices), programs, or applications. In another embodiment, the server 102 can be operatively coupled to a weather station 108 via the network 106. For example, the weather station 108 can be an assembly of weather-related sensors such as a thermometer, a barometer, a gauge, or any other suitable sensor for collecting environmental data. In another example, the weather station 108 can be a network-connected computer 108 in operative connection with a server having the ability to receive and / or acquire environmental data and transmit the environmental data to the server 102. The WILD system 100 can be integrated with a railway system or railway infrastructure to facilitate the detection of defects within railway components. Those skilled in the art will understand that the detections, captured data, measurements, determinations, alerts, etc. encompassed by the WILD system 100 can be transmitted to and / or made accessible from the railway system mainly via the network 106 or other operative connections. In one embodiment, the server 102 can include machine-readable instructions 120, and in another embodiment, the server 102 can access the machine-readable instructions 120. In another embodiment, the machine-readable instructions can include instructions related to a vehicle detection module 122, an environmental data capture module 124, a geolocation module 126, a vehicle data capture module 128, a force determination module 130, a force calibration module 132, an alert generation module 134, and / or an alert delivery module 136.

[0017] The above system components (e.g., one or more servers 102, weather stations 108, and one or more clients 110, 112, 114, 116, etc.) can be communicatively coupled to each other via network 140 so that data can be transmitted. Network 106 can be the Internet, an intranet, or other suitable network. Data transmission can be encrypted / decrypted over a VPN tunnel or other suitable communication means. Network 106 can be a WAN, LAN, PAN, or other suitable network type. Network communication between clients, server 102, or any other system component can be encrypted using PGP, Blowfish, Twofish, AES, 3DES, HTTPS, or other suitable encryption. System 100 can be configured to provide communication via an application programming interface (API), PCI, PCI-Express (registered trademark), ANSI-X12, Ethernet (registered trademark), Wi-Fi (registered trademark), Bluetooth (registered trademark), or other suitable communication protocol or medium, through the various systems, components, and modules disclosed herein. In addition, third-party systems and databases can also be operatively coupled to the system components via network 106.

[0018] Data transmitted between the components of system 100 (e.g., server 102, weather station 108, and clients) can include any format including JSON (JavaScript Object Notation), TCP / IP, XML, HTML, ASCII, SMS, CSV, REST (representational state transfer), or other suitable formats. Data transmission can include a message, flag, header, header property, metadata, and / or body, or can be encapsulated and packaged in any suitable format having these.

[0019] One or more servers 102 can be implemented in hardware, software, or any suitable combination of hardware and software for this purpose, and can have one or more software systems operating on one or more servers having one or more processors 118 in a state involving access to a memory 104. One or more servers 102 can include electronic storage, one or more processors, and / or other components. One or more servers 102 can include communication lines, connections, and / or ports to enable the exchange of information via a network 106 and / or other computing platforms. Also, one or more servers 102 can include multiple hardware, software, and / or firmware components that cooperate to provide the functions ascribed herein to one or more servers 102. For example, one or more servers 102 can be implemented by a cloud of a computing platform that cooperates as one or more servers 102 including Software-as-a-Service (SaaS) and Platform-as-a-Service (PaaS) functions. In addition to this, one or more servers 102 can also include a memory 104 therein.

[0020] Memory 104 can have electronic storage that includes a non-transitory storage medium for electronically storing information. The electronic storage medium of the electronic storage can include system storage that is provided integrally (e.g., substantially non-removably) with one or more servers 102 and / or removable storage that can be removably connected to one or more servers 102 via, for example, a port (e.g., a USB (registered trademark) port, a FireWire port, etc.) or a drive (e.g., a disk drive, etc.). The electronic storage can include one or more of an optically readable storage medium (e.g., an optical disk, etc.), a magnetically readable storage medium (e.g., a magnetic tape, a magnetic hard drive, a floppy drive, etc.), a charge-based storage medium (e.g., an EEPROM, a RAM, etc.), a semiconductor storage medium (e.g., a flash drive, etc.), and / or other electronically readable storage media. The electronic storage can include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and / or other virtual storage resources). The electronic storage can include a database, or a public or private distributed ledger (e.g., a blockchain). The electronic storage can store machine-readable instructions 120, software algorithms, control logic, data generated by one or more processors, data received from one or more servers, data received from one or more computing platforms, and / or other data that can enable one or more servers to function as described herein. Additionally, the electronic storage can include a third-party database that is accessible via network 106.

[0021] One or more processors 118 can be configured to provide data processing capabilities within one or more servers 102. Thus, one or more processors 118 can include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and / or other mechanisms for electronically processing information such as an FPGA or ASIC. One or more processors 118 can be a single entity or can include multiple processing units. These processing units can be physically located within the same device, or alternatively, one or more processors 118 can represent the processing functions of multiple devices or software functions that operate alone or in cooperation.

[0022] One or more processors 118 can be configured to execute machine-readable instructions 106 or machine learning modules via software, hardware, firmware, any combination of software, hardware, and / or firmware, and / or other mechanisms that configure processing capabilities on one or more processors 118. As used herein, the term "machine-readable instructions" can mean any component or combination of components that perform the functions attributed to the machine-readable instruction component 120. This can include one or more physical processors 118, processor-readable instructions, circuits, hardware, storage media, or any other component during the execution of processor-readable instructions.

[0023] One or more servers 102 can be configured by machine-readable instructions 120 having one or more functional modules. The machine-readable instructions 120 can be implemented on one or more servers 102 having one or more processors 118 in a state involving access to a memory 104. The machine-readable instructions 120 can also be a single network-connected node, or can be a machine cluster that can include a distributed architecture of multiple network-connected nodes. The machine-readable instructions 120 can include control logic to implement various functions, as will be described in more detail below. The machine-readable instructions 120 can include specific functions related to the WILD system 100. In addition to this, the machine-readable instructions 120 can include smart contracts or multi-signature contracts that can process data, interpret data, and write data to a database, a distributed ledger, or a blockchain.

[0024] FIG. 2 shows a schematic diagram of a wheel impact load detection system 200 according to one or more exemplary embodiments of the present disclosure. The WILD system 200 can include a WILD data capture system 202, a WILD calibration system 204, and an alert management system 206. In one exemplary embodiment, the WILD data capture system 202 can include a vehicle detection module 122, an environmental data capture module 124, a geolocation module 126, and a vehicle data capture module 128. The vehicle detection module 122, the environmental data capture module 124, the geolocation module 126, and the vehicle data capture module 128 can implement one or more algorithms to facilitate data capture related to wheel impact load detection, including recognition, location acquisition, and detection algorithms. In one embodiment, the WILD data capture system 202 can be configured to activate upon detection of a vehicle on a track and then capture numerous data related to the vehicle, the track, and the environment.

[0025] In one embodiment, the vehicle detection module 122 can detect a vehicle (e.g., a train or other rail moving vehicle) on at least a portion of the track. For example, the WILD data capture system 202 can be in an operable communication state with a strain gauge, camera, LIDAR, radar, or any other device or mechanism suitable for detecting motion (and / or position) on the track, and the vehicle detection module 122 can be configured to receive data from these components and determine whether a vehicle is present. In another embodiment, the environmental data capture module 202 can be configured to receive and / or acquire environmental data (e.g., data related to environmental conditions). For example, the WILD data capture system 202 can be in an operable connection state with a weather station 108 that can collect, store, and provide data related to weather conditions such as temperature, precipitation, pressure, and humidity, among other things. In one example, the detection of a vehicle by the vehicle detection module 122 can activate the environmental data capture module 124 such that the environmental data capture module 124 can receive environmental data corresponding to the time window in which the vehicle was detected. In another embodiment, the weather station 108 can periodically transmit (wired or wirelessly) the captured environmental data to the server 102. In another embodiment, the weather station 108 can transmit (wired or wirelessly) environmental data to the server 102 in an asynchronous manner based on one or more thresholds stored on the weather station 108.

[0026] In another embodiment, the geolocation module 126 can be configured to receive, obtain, generate, transmit, and / or store the location of the detected vehicle and / or track. For example, the WILD data capture system 202 can be operably coupled to a global positioning system that can track the location of a vehicle such that the geolocation module 126 can receive the location of the vehicle from the global positioning system when the vehicle detection module 122 detects the vehicle. In another embodiment, the WILD data capture system 202 can be operably coupled to sensors and other components that maintain static locations that can be transmitted within the system 200. For example, the vehicle detection module 122 can be capable of detecting a vehicle via the coupled sensors, and upon detection, the geolocation module 126 can receive the location from the coupled sensors. In another embodiment, the geolocation module 126 can obtain a plurality of stored locations corresponding to a plurality of vehicles, tracks, sensors, or other components that the geolocation module 126 can associate with the detection from the vehicle detection module 122. In another embodiment, the geolocation module 126 can transmit a location, such as the location of the detected vehicle and / or a portion of the track on which the vehicle is moving, to another system (e.g., a third-party system).

[0027] In another embodiment, the vehicle data capture module 128 can be configured to capture vehicle-specific data. For example, the vehicle data capture module 128 can be operatively coupled with, for example, an RFID reader that can facilitate receipt of data from an RFID chip on the vehicle. In another embodiment, the RFID chip can include a date, a time, the number of axles of the vehicle, the identification of the vehicle, the direction in which the vehicle is moving, and / or the load borne by the vehicle. In another embodiment, the RFID chip can include a plurality of data related to the location where the vehicle was present, the location where the vehicle is currently located, and the location towards which the vehicle is headed. In another example, the vehicle data capture module 128 can be operatively coupled with any other device or component suitable for capturing data related to the vehicle, such as, for example, a strain gauge, a camera, a radar, etc. For example, the vehicle data capture module 128 can be coupled with a camera that can detect the serial number or other identification information of the vehicle. The vehicle data capture module 128 can receive data from, for example, a sensor that can measure the stress applied to a rail or track. In another embodiment, the vehicle data capture module 128 can receive data from a strain gauge coupled to a rail of a track that can measure stress as it changes with the passage of a vehicle on the track. In another example, the vehicle data capture module 128 can receive data from any one or more sensors that can measure the force applied to a rail by, for example, a vehicle moving on the rail. In another embodiment, the vehicle data capture module 128 can receive data related to the force applied to one and / or more rails over a predetermined time period.

[0028] In another embodiment, the WILD impact load detection system 200 can include a WILD calibration system 204. The WILD calibration system 204 can include a force determination module 130 and a force calibration module 132. In one embodiment, the force determination module 130 can be configured to receive data from the WILD data capture system 202 and to use the data when determining one and / or more forces applied to a portion of the track and / or rail. For example, the force determination module 130 can be capable of receiving data from the vehicle data capture module 128, and in another example, the force determination module 130 can be capable of receiving data having a particular unit and can convert the data to reflect the unit of force. In one embodiment, the force determination module 130 can be capable of receiving a plurality of sensor data captured by the vehicle data capture module 128 and can use the sensor data to determine the maximum force (maximum peak force) (original maximum force) (original maximum peak force) applied to the rail detected by the sensor. In one embodiment, the force determination module 130 can determine the weight and impact force of the vehicle from the determined maximum force. In another embodiment, the force determination module 130 can be configured to generate a plot of the sensor data and to identify trends within the plot that may correspond to several related force measurements. For example, and in one embodiment, the force determination module 130 can generate a plot showing the static peak force trend and the maximum force in a graph displaying the magnitude of the measured force over time.

[0029] In one embodiment, the force determination module 130 can consider sensor data and can recognize a static peak force trend (an example of which is shown above). In one embodiment, the static peak force trend can mean a measured value having the largest instance (within the margin of error). In another embodiment, the force determination module 130 can use the static peak force trend and / or sensor data to determine the weight of a vehicle on a rail, such as by averaging all of the plot points located within the trend to obtain the determined weight of the vehicle. In another embodiment, the force determination module 130 can be configured to recognize a trend (e.g., a static peak force trend) in the data without plotting the data.

[0030] In another embodiment, the force determination module 130 can discriminate between impact forces (dynamic forces), such as those that can be generated by imperfections or defects in the wheels, rails, vehicles, or other components participating in the application of force to the rails with respect to the weight of the vehicle. For example, the static peak force trend can be correlated with the weight of the vehicle because most of the vehicle's wheels are likely to generally be free of defects, and the force exerted by the vehicle on the track via the wheels can remain in a mostly constant state as the intact portions of the wheels support the weight of the vehicle. In another example, as the wheels rotate on the track and a wheel defect is positioned between the track and the vehicle, the force exerted by the vehicle on the rail can increase, and such an increase from the static peak force trend (the apex of which can be considered the maximum force value) can be correlated with the dynamic force. In another example, the force determination module can utilize the determined weight of the vehicle and the maximum force of the vehicle when determining the dynamic force. For example, in one embodiment, subtracting the vehicle weight from the maximum force value can result in a dynamic force value.

[0031] In another embodiment, the WILD calibration system 204 can include a force calibration module 132. The force calibration module 132 can be configured to use data from the force determination module 130 and the WILD data capture system 202 to calibrate the force value determined by the force determination module 130, such as to account for environmental conditions. For example, the force calibration module 132 can receive the maximum force value from the force determination module 130, can receive weather data from the WILD data capture system 202, and can algorithmically adjust / calibrate the maximum force value to account for the received weather data. In one embodiment, the force calibration module 132 can calibrate the maximum force value to account for changes in temperature. For example, the force calibration module 132 can be configured to adjust the maximum force value (e.g., for the purposes of alert severity level assignment, generation, and delivery) to account for an increase in rail tension, such as may be produced by linear contraction of the rail due to a cold wave. In another example, the force calibration module 132 can be configured to adjust the maximum force value to account for a decrease in rail tension, such as may be produced by linear expansion of the rail due to warm weather. In another embodiment, the force calibration module 132 can be configured to adjust the maximum force value determined by the force determination module 130 to compensate for or account for any environmental condition, including temperature, pressure, humidity, wind speed, precipitation, UV index, and storm patterns, without limitation.

[0032] In one embodiment, the force calibration module 132 can calibrate the maximum force value using an operating variable. For example, the force calibration module 132 can apply a mathematical formula that implements an operating variable that can vary according to the compensated conditions. In another embodiment, the operating variable can be a constant that can correspond to a specific temperature threshold. In another embodiment, the operating variable can be a constant that corresponds to the material within the rail. In another embodiment, the operating variable can be a constant that corresponds to the normal linear expansion or contraction of the rail at a specific temperature. In one embodiment, the force calibration module 132 can utilize the following formula.

Number

[0033] In one embodiment, K original can mean the maximum force value determined by the force determination module 130. In another embodiment, K original can include a value in units of "kip", that is, a measured value in units of thousand pounds (for example, 1 kip = 1000 lbs). In another embodiment, K adjustedmay mean the calibrated and / or adjusted maximum force value. In another embodiment, V may be the manipulated variable. In one embodiment, V may range from 0.001000 to 0.0018000. In another embodiment, V may range from -0.004000 to -0.005500. In another embodiment, V may be equal to -0.0048809, and in another embodiment, V may be equal to 0.001604. In another embodiment, the manipulated variable can be derived by setting relationships such that the 100 kip value at 0°F can be adjusted downward to 90 kip. In one embodiment, the value of V may vary according to the compensated temperature (e.g., high or low temperature). Also, in another embodiment, V may vary according to a temperature threshold. In another embodiment, the temperature threshold may vary according to whether cold or warm weather is compensated. For example, the temperature threshold can be designed to take into account the reduction in rail tension due to relatively warm weather, and °F deviation from temperature threshold may mean the number of degrees above the temperature threshold (for example, 100°F). For example, when the temperature is 110°F and the temperature threshold is 100°F, °F deviation from temperature threshold as, the integer "10" can be inserted. In another embodiment, the above formula can be modified as follows to calibrate the maximum force value to take into account temperatures below freezing (e.g., below the freezing point of water).

Number

[0034] In another embodiment, the force calibration module 132 can take into account the amount of time that a given environmental condition exists. For example, if the temperature is above or below a temperature threshold (or otherwise meets the temperature threshold) for a set amount of time (e.g., 2 hours), the force calibration module 132 can further calibrate the maximum force value to account for this duration and temperature. In one embodiment, the force calibration module 132 can provide an increased calibrated maximum force value to account for a relatively high temperature that exists over a relatively long period of time in comparison to the original maximum force value, and can provide a decreased calibrated maximum force value to account for a relatively low temperature that exists over a relatively long period of time. In one embodiment, the force calibration module 132 can calibrate the maximum force value only if the temperature drops below freezing or rises above 100°F.

[0035] In another embodiment, the wheel impact load detection system 200 can include an alert management system 206. The alert management system 206 can include an alert generation module 134 and an alert supply module 136. In another embodiment, the alert generation module 134 can receive data from the WILD data capture system 202 and / or the WILD calibration system 204. For example, the alert generation module 134 can receive that a vehicle has been detected, the location of the vehicle, environmental conditions, and / or the characteristics of the vehicle. In another example, the alert generation module 134 can receive the maximum force value (and / or the calibrated maximum force value), and can determine whether the (calibrated) maximum force value exceeds one or more force thresholds. In another embodiment, the alert generation module 134 (and / or the force determination module 130) can determine the confidence level in the data received from one or more systems and / or sensors. In another embodiment, the alert generation module 134 can utilize the received data to generate an alert having an assigned severity level. For example, it is possible to assign a severity level (e.g., levels 1-3) to the alert based on the (calibrated) maximum force value and the confidence level, and an example of its representation is shown below.

[0036]

Table 1

[0037] In one embodiment, the severity level can notify the railway system of the actions recommended to address the alert. For example, a level 1 alert can notify that the vehicle should be immediately stopped and inspected. In another example, a level 2 alert can notify that inspection can be awaited until the next scheduled inspection (e.g., of the vehicle's wheels and / or axles). In another example, a level 3 alert can notify that inspection can be awaited until the vehicle's cargo is completely emptied and / or until the time of the last visit to the mechanical facility before going offline. In another embodiment, severity levels for a plurality of other alerts are possible. For example, a discretionary alert can notify that a vehicle (or a specific part of the vehicle, such as a wheel) requires inspection only when the vehicle is being repaired for different problems. In another example, a level 4 alert can notify that the alert generation module 134 utilized a calibrated maximum force (e.g., instead of the original maximum force) during alert generation so that the level 4 alert can notify the railway system that the alert severity was adjusted as a result of calibration for environmental conditions. In another embodiment, a level 4 alert can notify that the calibrated maximum force value has dropped below a force threshold while the original maximum force value exceeds the force threshold. In another embodiment, a level 4 alert can notify the same recommended actions as a discretionary alert, but can further notify the railway system that the alert was generated by taking environmental conditions into account.

[0038] In another embodiment, the severity level of the alert generated by the alert generation module 134 can be at least partially based on the (calibrated) maximum force value. As an example, and referring to the table above, a kip value of 140 or more (e.g., 140,000 lbs) can be classified as "highest severity", a kip value of 120 - 140 can be "severe", and a kip value of 90 - 120 can be "lowest severity". In another embodiment, a kip value of 80 - 90 can generate a discretionary alert. Instead of the above, other kip values or (calibrated) maximum force values can be used in these categories. In another embodiment, the alert generation module 134 can utilize several force thresholds and confidence thresholds when assigning a severity level. For example, when the maximum force value drops below a first force threshold (e.g., 80 kips), the alert generation module 134 can determine not to generate an alert without considering the confidence level. In another example, when the confidence level drops below a first confidence threshold (e.g., 15%), the alert generation module 134 can determine not to generate an alert without considering the maximum force value. In another embodiment, the alert generation module 134 can utilize several force and / or confidence thresholds to assign a severity level for the generated alert.

[0039] In another embodiment, the alert supply module 136 of the alert management system 206 can send alerts throughout the railway system. For example, the alert supply module 136 can receive alerts with an assigned severity level from the alert generation module 134 and can transmit the alerts to personnel in operable connection with the network or the alert supply module 136, network-connected servers, or any other component. In one embodiment, the alert supply module 136 can send alerts via messages, records, or any other suitable form of communication. In another embodiment, the alert supply module 136 can update records with the generated alerts.

[0040] Figure 3 shows a flowchart diagram 300 that illustrates control logic implementing the functionality of a method for wheel impact load detection (WILD) according to an exemplary embodiment of the present disclosure. The WILD control logic 300 can be implemented as an algorithm on a server (e.g., server 102), a machine learning module, or other suitable system. In addition to this, the WILD control logic 300 can implement or incorporate one or more features of the WILD system 200 including the WILD capture system 202 (having corresponding modules 122, 124, 126, and 128), the WILD calibration system 204 (having corresponding modules 130 and 132), and the alert management system 206 (having corresponding modules 134 and 136). The WILD control logic 300 can be realized by software, hardware, an application programming interface (API), network connections, network transfer protocols, HTML, DHTML, JavaScript®, Dojo, Ruby, Rails, other suitable applications, or suitable combinations thereof.

[0041] The WILD control logic 300 can utilize the capabilities of a computer platform to generate multiple processes and threads by processing data simultaneously. The speed and efficiency of the WILD control logic 300 are significantly improved by instantiating multiple processes to facilitate wheel impact load detection. However, those skilled in the art of programming will understand that the use of a single processing thread can also be utilized and is within the scope of the present disclosure.

[0042] The process flow of the WILD control logic 300 of the present embodiment starts at step 302, where the control logic 300 is being instantiated. In one embodiment, the control logic 300 can be configured to receive data from sensors or other data collection devices on and / or near a railroad track, and the instantiation of the control logic 300 at step 302 can prepare the control logic 300 to receive and process expected data. The control logic then proceeds to step 304.

[0043] At step 304, the control logic 300 can detect a train passing along a particular portion of the track. For example, the control logic 300 can receive data from a motion sensor notifying that a train is passing, and in another embodiment, the control logic 300 can receive data from a strain gauge notifying that a train is exerting a force on a portion of the track. In one embodiment, step 302 can also be associated with and / or considered to be executed by the vehicle detection module 122. The control logic 300 then proceeds to steps 306 and 308.

[0044] In step 306, control logic 300 can capture environmental data such as weather data. In one embodiment, step 306 can also be associated with and / or considered to be executed by environmental data capture module 124. In another embodiment, step 306 can include receiving weather data from a weather station (e.g., weather station 108). In another embodiment, step 306 can include receiving one or more temperature measurements such as a measured value of ambient temperature. Then, control logic 300 proceeds to step 310.

[0045] In step 310, control logic 300 can determine whether a temperature threshold is satisfied. In one embodiment, the temperature threshold can be satisfied when the temperature (e.g., the temperature received in step 306) is less than, greater than, and / or equal to one or more predetermined temperatures. In one exemplary embodiment, the temperature threshold is satisfied when the temperature in step 306 (e.g., ambient temperature) is less than 32°F. If the temperature threshold is satisfied, control logic 300 proceeds to step 316. If the temperature threshold is not satisfied, control logic 300 proceeds to step 336.

[0046] In step 308, control logic 300 can capture wheel impact load detection (WILD) data. In one embodiment, step 308 can also be associated with and / or considered to be executed by vehicle data capture module 128 and / or geolocation module 126. In another embodiment, control logic 300 can receive train characteristics, measurements (e.g., measurements of track distortion and / or stress), the location of the train, and other related data. Then, control logic 300 proceeds to steps 312 and 314.

[0047] In step 314, the data captured in step 308 can be transferred and / or sent to an alert system (e.g., the alert management system 206). For example, and in one embodiment, the control logic 300 can generate a record containing the data and send the record to the alert system to facilitate, for example, the generation of an alert by the alert system. In another embodiment, the control logic 300 can send a message to the alert system that transmits the data. Then, the control logic 300 proceeds to step 318.

[0048] In step 318, the data transferred to 314 in the alert system can be stored in the alert system database or any other database in an operable connection state with the control logic 300. Then, the control logic 300 proceeds to step 332.

[0049] In step 332, the control logic 300 can generate and publish an alert using the data stored in the database in step 318. In one embodiment, step 332 can also be associated with and / or considered to be executed by the force determination module 130, the alert generation module 134, and / or the alert supply module 136. For example, the control logic 300 can determine the maximum peak (e.g., the maximum force such as the maximum peak determined in step 312), compare the maximum force with a predetermined force threshold, and determine whether an alert should be generated and published. Also, the control logic 300 can assign a severity level to the alert in 332 based at least in part on the maximum peak and the predetermined force threshold. In one embodiment, the alert can be published to the railway system to notify personnel about the recommended actions.

[0050] In step 312, the control logic 300 can determine the original maximum peak (original maximum peak force). In one embodiment, step 312 can also be associated with and / or considered to be executed by the force determination module 130. In another embodiment, the original maximum peak can mean the original maximum force that can be determined from the data captured in step 308. For example, in step 312, the control logic 300 can determine the original clip value that can be used when assigning a severity level to an alert. Then, the control logic 300 proceeds to step 316.

[0051] In step 316, when the temperature threshold is satisfied in step 310, the control logic 300 can utilize the original maximum peak and the temperature delta (e.g., the amount of deviation of the measured temperature from the temperature threshold) to calculate the adjusted maximum peak (calibrated maximum peak force). In one embodiment, step 316 can also be associated with and / or considered to be executed by the force calibration module 132. When the temperature threshold is not satisfied in step 310, the temperature delta can be zero, which means that the adjusted maximum peak can be equal to the original maximum peak. Then, the control logic 300 proceeds to step 320.

[0052] In step 320, the control logic 300 adds the adjusted maximum peak value determined in step 316 to the WILD data captured in step 308. In one embodiment, step 320 can also be associated with and / or considered to be executed by the force calibration module 132 and / or the alert supply module 136. Then, the control logic 300 proceeds to step 322.

[0053] In step 322, control logic 300 assigns a severity level to an alert based at least in part on the adjusted maximum peak value. In one embodiment, step 322 can be associated with and / or considered to be executed by alert generation module 134. In one embodiment, the severity level assigned in step 322 can be different from the severity level of the alert generated in step 332. For example, when WILD data is captured in step 308, control logic 300 can assign an initial severity level that does not consider environmental conditions (e.g., weather data captured in step 306). Then, after consideration of the temperature threshold in step 310, the control logic can modify the severity level in step 322. In one embodiment, the severity level assigned in step 322 can function as an "internal" alert, thereby notifying railroad personnel who need to know about the recommended actions, while the alert generated and published in step 332 can remain in an undecided state until the time when it is closed according to the process flow of control logic 300. In another embodiment, the severity level assigned in step 322 can override the severity level assigned in step 332 such that the alert generated and published in step 332 can be modified to reflect the new severity level assigned in step 322. Then, control logic 300 proceeds to step 324.

[0054] Next, in step 324, control logic 300 can receive an input regarding whether a defect (e.g., a claimable defect) has been discovered after the alert has been applied. For example, if an inspection is performed according to the assigned severity level, control logic 300 can receive a command notifying whether a defect has been found. If a defect is found, control logic 300 proceeds to step 326. If no defect is found, the control logic proceeds to step 328.

[0055] In step 326, control logic 300 can receive an input notifying that the inspection has been performed. In one embodiment, the inspection input can be an email, text, flag, message, or other appropriate notification. Next, control logic 300 proceeds to step 330. In step 328, control logic 300 can receive an input notifying that a repair has been performed to address the defect. In one embodiment, the repair input can be an email, text, flag, message, or other appropriate notification. Next, control logic 300 proceeds to step 330.

[0056] In step 330, an alert with the assigned severity level can be closed. For example, if it is notified to the control logic in step 326 that the defect has been repaired, control logic 300 can invalidate the alert so that control logic 300 can close the alert. Similarly, if it is notified to control logic 300 in step 328 that the inspection has been performed, control logic 300 can close the alert. Next, control logic 300 proceeds to steps 332 and 334. In step 332, control logic 300 publishes that the alert has been closed. In step 334, control logic 300 can end, or can wait for a new train detection and repeat the above steps.

[0057] In one embodiment, steps 304, 306, and 308 of control logic 300 may correspond to the WILD data capture system 202. In another embodiment, steps 310, 312, 316, and 320 may correspond to the WILD calibration system 204. In another embodiment, steps 314, 318, 322, 324, 326, 328, 330, and 332 may correspond to the alert management system 206.

[0058] FIG. 4 shows a flowchart 400 illustrating control logic that implements the functions of a method for wheel impact load detection (WILD) and calibration according to an exemplary embodiment of the present disclosure. The detection and calibration control logic 400 can be implemented as an algorithm on a server (e.g., server 102), a machine learning module, or other suitable system. In addition to this, the detection and calibration control logic 400 can implement or incorporate one or more functions of the WILD system 200 including the WILD capture system 202 (having corresponding modules 122, 124, 126, and 128), the WILD calibration system 204 (having corresponding modules 130 and 132), and the alert management system 206 (having corresponding modules 134 and 136). The control logic 400 can be realized by software, hardware, an application programming interface (API), a network connection, a network transfer protocol, HTML, DHTML, JavaScript, Dojo, Ruby, Rails, other suitable applications, or a suitable combination thereof.

[0059] The control logic 400 can utilize the capabilities of a computer platform to generate multiple processes and threads by processing data simultaneously. The speed and efficiency of the control logic 400 are significantly improved by instantiating multiple processes to facilitate wheel impact load detection. However, those skilled in programming will understand that the use of a single processing thread can also be utilized and is included within the scope of the present invention.

[0060] The process flow of the control logic 400 of this embodiment starts at 402, where the control logic 400 is detecting a vehicle, such as a vehicle moving on a track. In one embodiment, step 402 can also be associated with and / or considered to be executed by the vehicle detection module 122. Then, the control logic 400 proceeds to step 404.

[0061] At step 404, the control logic 400 can generate a record. In one embodiment, step 404 can also be associated with and / or considered to be executed by the vehicle data capture module 128. In one embodiment, the record can include data related to the vehicle, such as the time and date of detection, the characteristics of the vehicle, the number of axles of the vehicle, and / or the direction in which the vehicle is moving. The record can be stored on a client, server, or database. Then, the control logic 400 proceeds to step 406.

[0062] At step 406, the control logic 400 can receive locations, such as the location of the vehicle, the location of the track, etc. In one embodiment, step 406 can also be associated with and / or considered to be executed by the geolocation module 126. For example, the control logic 400 can receive location data from sensors on the vehicle or on the track, and in another example, the control logic 400 can receive the location from a railway system. Then, the control logic 400 proceeds to step 408.

[0063] In step 408, the control logic can receive environmental data. In one embodiment, step 408 can also be associated with and / or considered to be executed by the environmental data capture module 124. The environmental data can include humidity, pressure, temperature, or any other type of environmental data. Then, the control logic proceeds to step 410.

[0064] In step 410, the control logic 400 can receive data from sensors such as sensors on the track or vehicle. In one embodiment, step 410 can also be associated with and / or considered to be executed by the vehicle data capture module 128. Preferably, the control logic 400 is receiving data from a sensor that can notify strain / stress corresponding to the weight or mass of the vehicle. In one example, the sensor can be a strain gauge coupled to the track. Then, the control logic 400 proceeds to step 412.

[0065] In step 412, the control logic 400 can determine the original maximum force value from the sensor data. In one embodiment, step 412 can also be associated with and / or considered to be executed by the force determination module 130. Then, the control logic 400 proceeds to step 414.

[0066] In step 414, the control logic 400 can determine the confidence level. In one embodiment, step 414 can also be associated with and / or considered to be executed by the force determination module 130 and / or the alert generation module 134. In another embodiment, the confidence level can be a measure of the confidence in the accuracy of the sensor data received in step 410. For example, and in one embodiment, the data received in step 410 can be received from one or more sensors. If multiple sensors are providing similar measurements, the control logic 400 can determine in step 414 that the confidence level should be relatively high. On the other hand, if only one sensor is providing usable measurements, the control logic 400 can determine in step 414 that the confidence level should be relatively low. In another embodiment, if the measurements from the sensors are not uniform but rather are relatively abnormal, the control logic 400 can determine that the confidence level should be relatively low. In another embodiment, if the received data provides a plurality of data points that appear to be appropriately uniform, the control logic 400 can determine that the confidence level should be relatively high. In one embodiment, the control logic 400 can assign a percentage value to the determined confidence level (as in the table described above, for example, in relation to the alert generation module 134). Then, the control logic 400 can proceed to step 416.

[0067] In step 416, the control logic 400 can determine whether the environmental threshold is met. In one embodiment, step 416 can also be associated with and / or considered to be executed by the force calibration module 132. In one embodiment, the environmental threshold can be a pressure threshold such that when the environmental data received in step 408 indicates a pressure that is less than, equal to, or greater than the pressure threshold, the pressure threshold can or cannot be met. In another embodiment, the environmental threshold can be a temperature threshold such as the temperature threshold described in relation to the force calibration module 132 and the alert generation module 134 described above. When the environmental threshold is met, the control logic 400 proceeds to step 418. When the environmental threshold is not met, the control logic 400 proceeds to step 420.

[0068] In step 418, the control logic 400 can generate a calibrated maximum force value. In one embodiment, step 418 can also be associated with and / or considered to be executed by the force calibration module 132. In another embodiment, the control logic 400 can generate a calibrated maximum force value by referring to the original maximum force value determined in step 412 and the environmental data received in step 408. In another embodiment, the calibrated maximum force value can be generated by adjusting the original maximum force value by an operating variable. Then, the control logic 400 proceeds to step 420.

[0069] In step 420, the control logic 400 can determine whether the original maximum force value exceeds the force threshold. In one embodiment, step 420 can also be associated with and / or considered to be executed by the alert generation module 134. In another embodiment, the control logic 400 can refer to a force threshold stored in a memory that can function as a kill switch. For example, if the original maximum force does not exceed the force threshold, the control logic 400 can determine that no alert will be generated, regardless of the remaining process flow steps. If the original maximum force value does not exceed the force threshold, the control logic 400 proceeds to step 422. If the original maximum force value exceeds the force threshold, the control logic 400 proceeds to step 424. In step 422, the control logic 400 can determine that no alert will be generated. In one embodiment, step 422 can also be associated with and / or considered to be executed by the alert generation module 134. Then, the control logic 400 proceeds to step 432. In step 432, the control logic 400 can update a record such as the record generated in step 404. In one embodiment, step 420 can also be associated with and / or considered to be executed by the alert supply module 136.

[0070] In step 424, the control logic 400 can determine whether a calibrated maximum force value has been generated (e.g., whether step 418 has been executed). In one embodiment, step 424 can also be associated with and / or considered to be executed by the alert generation module 134. If the control logic 400 has generated a calibrated maximum force value, the control logic 400 proceeds to step 426. If the control logic 400 has not generated a calibrated maximum force value, the control logic 400 proceeds to step 428. In step 426, the control logic 400 can utilize the calibrated maximum force value generated in step 418 and the confidence level determined in step 414 to assign a severity level to the alert. In one embodiment, step 426 can also be associated with and / or considered to be executed by the alert generation module 134. In another embodiment, the severity level can be assigned according to the table described above in relation to the alert generation module 134. In one example, the severity level of the alert can vary with both the calibrated maximum force value and the confidence level. The control logic 400 then proceeds to step 430.

[0071] In step 428, the control logic 400 can utilize the original maximum force value determined in step 412 and the confidence level determined in step 414 to assign a severity level to the alert. In one embodiment, step 428 can also be associated with and / or considered to be executed by the alert generation module 134. In another embodiment, the severity level can be assigned according to the table described above in relation to the alert generation module 134. In one example, the severity level of the alert can vary with both the original maximum force value and the confidence level. Then, the control logic 400 proceeds to step 430. In step 430, the control logic 400 can generate an alert having the severity level assigned in step 426 or step 428. Then, the control logic 400 proceeds to step 432. In step 432, the control logic 400 can update the record generated in step 404 to reflect that the alert was generated by the severity level assigned in step 426 or step 428 or that no alert was generated. Then, the control logic 400 can end, or can wait for a new vehicle detection and repeat the steps described above.

[0072] In one embodiment, steps 402, 404, 406, 408, and 410 of the control logic 400 can correlate with the WILD data capture system 202. In another embodiment, steps 412, 414, 416, and 418 can correlate with the WILD calibration system 204. In another embodiment, steps 420, 422, 424, 426, 428, 430, and 432 can correlate with the alert management system 206.

[0073] Figures 5A - 5B illustrate flowchart 500 which depicts the control logic for implementing the features and program steps of a wheel impact load detection and calibration system according to an exemplary embodiment of the present disclosure. The control logic 500 of the wheel impact load detection and calibration system can be implemented as an algorithm on a server (e.g., server 102), a machine learning module, or other suitable systems. In addition to this, the control logic 500 of the wheel impact detection and calibration system can implement or incorporate one or more features of the WILD system 200 including the WILD capture system 202 (having corresponding modules 122, 124, 126, and 128), the WILD calibration system 204 (having corresponding modules 130 and 132), and the alert management system 206 (having corresponding modules 134 and 136). The control logic 500 can be realized by software, hardware, an application programming interface (API), a network connection, a network transfer protocol, HTML, DHTML, JavaScript, Dojo, Ruby, Rails, other suitable applications, or a suitable combination of these.

[0074] By processing data simultaneously, the control logic 500 can utilize the capabilities of a computer platform to generate multiple processes and threads. The speed and efficiency of the control logic 500 are significantly improved by instantiating multiple processes to facilitate wheel impact load detection. However, those skilled in programming will understand that the use of a single processing thread can also be utilized and is within the scope of the present invention.

[0075] The process flow of the control logic 500 of the present embodiment starts at step 502, where the control logic 500 can detect a vehicle, such as a vehicle moving on a track. Then, the control logic 500 proceeds to step 504. At step 504, the control logic 500 can generate a record. Then, the control logic 500 can proceed to step 506. At step 506, the control logic 500 can receive the location of the vehicle and / or the track. Then, the control logic 500 proceeds to step 508. At step 508, the control logic 500 can receive environmental data 508. Then, the control logic 500 proceeds to step 510. At step 510, the control logic 500 can receive data from one or more sensors, and preferably, the received data is related to the force exerted by the vehicle on the track. Then, the control logic 500 proceeds to step 512. At step 512, the control logic 500 can determine the original maximum peak force from the received sensor data. In one embodiment, the maximum peak force can correspond to the maximum force exerted by the vehicle on the track. Then, the control logic 500 proceeds to steps 514 and 516.

[0076] In step 514, control logic 500 can determine the level of confidence in the sensor data received in accordance with the principles of the present disclosure. Then, control logic 500 proceeds to step 518. In step 518, control logic 500 can determine whether the original maximum peak force determined in step 512 exceeds a first force threshold. If the original maximum peak force does not exceed the first force threshold, control logic 500 proceeds to step 520. If the original maximum peak force exceeds the first force threshold, control logic 500 proceeds to step 522. In step 520, control logic 500 can determine that no alert will be generated. In step 522, control logic 500 can determine whether an environmental threshold is satisfied in accordance with the principles of the present disclosure. If the environmental threshold is not satisfied, control logic 500 proceeds to step 524. If the environmental threshold is satisfied, control logic 500 proceeds to step 526.

[0077] In step 526, the control logic 500 can generate a calibrated maximum peak force in accordance with the principles of the present disclosure. The control logic 500 then proceeds to step 528. In step 528, the control logic 500 can determine whether the calibrated maximum peak force generated in step 526 exceeds a first force threshold. If the calibrated maximum peak force does not exceed the first force threshold, the control logic 500 proceeds to step 540. If the calibrated maximum peak force exceeds the first force threshold, the control logic 500 proceeds to step 524. In step 540, the control logic 500 can generate an alert having an assigned severity level. In one embodiment, the severity level of the alert can be a level 4 severity level. In one embodiment, the severity level (e.g., level 4) can mean that the original maximum peak force exceeded the first force threshold and the calibrated maximum peak force did not exceed the first force threshold. In another embodiment, the level 4 alert can have a lower severity level (and priority) than level 1, level 3, and level 3 alerts. The control logic 500 can then end, or can wait for a new vehicle detection and repeat the steps described above.

[0078] In step 524, the control logic 500 can determine whether the maximum peak force (e.g., the original maximum peak force or the calibrated maximum peak force) exceeds a third force threshold. In one embodiment, the control logic 500 can determine whether the original maximum peak force or the calibrated maximum peak force should be used after step 524. For example, the control logic 500 can determine that the calibrated maximum peak force should be used starting from the state where it begins in step 524 if the calibrated maximum peak force has been generated in step 526. In another example, the control logic 500 can determine that the original maximum peak should be used starting from the state where it begins in step 524 if the calibrated maximum peak force has not been generated in step 526. Preferably, the third force threshold can be greater than the first force threshold. For example, the first force threshold can be such that in comparison with a maximum peak force where the maximum peak force exceeds the third force threshold and as a result the severity level of the resulting alert generally does not exceed the third force threshold but exceeds the first force threshold, it can correspond to a force (e.g., a relatively small kip value) smaller than the third threshold. If the maximum peak force being used (e.g., the original or calibrated one) exceeds the third force threshold, the control logic 500 proceeds to step 532. If the maximum peak force being used does not exceed the third force threshold, the control logic 500 proceeds to step 530.

[0079] In step 532, the control logic 500 can determine whether the confidence level determined in step 514 exceeds a first confidence threshold. For example, the confidence threshold can be in the form of a statistical probability that the received sensor data (and, consequently, the resulting maximum peak force value) is accurate. In another embodiment, the confidence threshold can be similar to that depicted in the above table corresponding to the general likelihood that the received data and the determined maximum peak force are accurate. Preferably, the first confidence threshold can be 50%, that is, if the confidence level determined in step 514 is less than 50%, the first confidence threshold will be considered not to be exceeded by the determined confidence level. If the confidence level exceeds the first confidence threshold, the control logic 500 proceeds to step 546. If the confidence level does not exceed the first confidence threshold, the control logic 500 can proceed to step 544. In step 544, the control logic 500 can generate an alert with an assigned severity level. In one embodiment, the severity level can be level 2. In another embodiment, the severity level of the alert generated in step 544 can be greater (e.g., relatively more critical) than the severity level of the alert generated in step 540. Then, the control logic 500 can end, or can wait for a new vehicle detection and repeat the above steps. In step 546, the control logic 500 can generate an alert with an assigned severity level. In one embodiment, the severity level can be level 1. In one embodiment, the severity level assigned to the alert generated in step 546 can be greater than the severity levels of the alerts generated in steps 544 and 540. Then, the control logic 500 can end, or can wait for a new vehicle detection and repeat the above steps.

[0080] In step 530, the control logic 500 can determine whether the maximum peak force (e.g., the original maximum peak force or the calibrated maximum peak force) exceeds a second force threshold. Preferably, the second force threshold can be between the first force threshold and the third force threshold. For example, the second force threshold can be greater than the first force threshold and less than the third force threshold. If the maximum peak force exceeds the second force threshold, the control logic 500 proceeds to step 534. If the maximum peak force does not exceed the second force threshold, the control logic 500 proceeds to step 542. In step 542, the control logic 500 can generate an alert having an assigned severity level. In one embodiment, the severity level can be level 3. In one embodiment, the severity level of the alert generated in step 542 can be lower than the severity levels of the alerts generated in steps 544 and 546 but higher than the severity level of the alert generated in step 540. The control logic 500 can then end, or can wait for a new vehicle detection and repeat the steps described above.

[0081] In step 534, the control logic 500 can determine whether the confidence level determined in step 514 exceeds the first confidence threshold. In one embodiment, the first confidence threshold in step 534 can be the same as the first confidence threshold in step 532. If the first confidence threshold is exceeded, the control logic 500 proceeds to step 536. If the first confidence level is not exceeded, the control logic 500 proceeds to step 542. In step 536, the control logic 500 can determine whether the confidence level determined in step 514 exceeds the second confidence threshold. The second confidence threshold can be similar to the first confidence threshold, and preferably, the second confidence threshold can indicate a relatively high level of confidence in comparison to the first confidence threshold. In one embodiment, the second confidence threshold can be 85%. For example, if the confidence level determined in step 514 does not exceed 85%, the confidence level will not exceed the second confidence threshold. In another embodiment, the 85% second confidence threshold can indicate that the control logic 500 is 85% confident that the data received from the sensor in step 510 (and consequently the resulting maximum peak force value) is accurate. If the confidence level exceeds the second confidence threshold, the control logic 500 proceeds to step 538. If the confidence level does not exceed the second confidence threshold, the control logic 500 proceeds to step 542.

[0082] In step 538, the control logic 500 can determine whether the confidence level determined in step 514 exceeds a third confidence threshold. The third confidence threshold may be similar to the first confidence threshold and / or the second confidence threshold. Preferably, the third confidence threshold can indicate a relatively high level of confidence in comparison to the first confidence threshold and the second confidence threshold. In one embodiment, the third confidence threshold may be 97%. For example, if the confidence level determined in step 514 does not exceed 97%, the confidence level will not exceed the third confidence threshold. If the confidence level exceeds the third confidence threshold, the control logic 500 proceeds to step 546. If the confidence level does not exceed the third confidence threshold, the control logic 500 proceeds to step 544.

[0083] In step 516, control logic 500 can plot the data from the sensors received in step 510. For example, control logic 500 can generate a plot such as those described above in relation to force determination module 130. Preferably, the plot can include a force axis and a time axis such that points on the plot can be positioned by the magnitude of the force and the time at which the force was applied. In another embodiment, the data received in step 510 can be data from one or more strain gauges, and control logic 500 can convert the units of the measurements provided by the strain gauges (e.g., με, inch / inch, mm / mm, etc.) to force measurement and / or mass measurement values such as newtons, pounds, kilograms, etc., and then plot the force measurements according to the time at which the data was received. Control logic 500 then proceeds to step 548. In step 548, control logic 500 can compare the points from the plot. Preferably, control logic 500 can search for similarities within the points and trends within the data. In one example, control logic 500 can determine the force value having the maximum instance (within the margin of error) on the plot. In one embodiment, control logic 500 can recognize that a particular force measurement value (e.g., 75 kip ± 5 kip) occurred most frequently. In another embodiment, control logic 500 can not only recognize spikes in the plotted data but also determine the maximum force measurement value received by the sensor in step 510. Control logic 500 then proceeds to step 550.

[0084] In step 550, control logic 500 can recognize the static peak force trend in the data. In one embodiment, the static peak force trend can mean the number of measured values that can be correlated with each other, such as being close in value to each other. In another embodiment, the static peak force trend can mean the range of force values having the maximum instance. In another embodiment, the static peak force trend can mean force measurement values within a given deviation from each other. As an example, in step 510, control logic 500 can receive 10 different force measurement values occurring at 10 different times. In this example, 7 of these measurement values can be in the range of 63 - 65 kip, and the other 3 measurement values can be 90 kip, 120 kip, and 100 kip respectively. In this example, control logic 500 can recognize that 7 of the measurement values are correlated with each other due to the characteristic that all of them are included within a range spanning only 3 kip. As a result, control logic 500 can recognize the static peak force trend in the data. In this example, control logic 500 can further recognize that the maximum force value scale was 120 kip. Then, control logic 500 proceeds to step 552. In step 552, control logic 500 can determine the weight value of the vehicle by using the static peak force trend. In one embodiment, control logic 500 can average all of the force values within the static peak for the trend in order to determine the weight of the vehicle. Then, control logic 500 proceeds to step 554. In step 554, control logic 500 can calculate the dynamic force value by using the original maximum peak force determined in step 512 and the weight value determined in 552. In one embodiment, control logic 500 can thereby determine the proportion of the maximum peak force due to a defect as contrasted with the weight of the vehicle.

[0085] During operation, in one exemplary embodiment, control logic 500 can begin at step 502, where the train can be detected on the track. For example, the train can be detected via at least one strain gauge coupled to the track on which the train moves, and in another example, the train can be detected by LIDAR such that the control logic 500 can be prepared to receive data. The train can include identification information that can be received by the control logic 500. For example, the train can have an RFID tag that can be read by an RFID reader in operable connection with the control logic 500. In another embodiment, the RFID tag can include the date and time the RFID tag was read by the reader, the number of axles of the train, and the direction the train is moving on the vehicle. In another example, the date and time the RFID tag was read can be generated by the control logic 500. Next, the control logic 500 proceeds to step 504. At step 504, the control logic 500 can generate a record 504 that can include data found from the reading of the train's RFID tag. Next, the control logic 500 proceeds to step 506. At step 506, the control logic 500 can receive the location of the vehicle, the track, and / or the spot of detection from, for example, coordinates programmed into a sensor (RFID reader, strain gauge, etc.), from a GPS beacon on the train, or from the RFID tag. Next, the control logic 500 proceeds to step 508. At step 508, the control logic 500 can receive environmental data such as the temperature (e.g., ambient temperature) in the portion of the track where the train was detected. In this example, the temperature received at step 508 can be 0°F. Next, the control logic 500 proceeds to step 510.

[0086] In step 510, in this example, the control logic 500 can receive data from a plurality of strain gauges coupled to the train. Then, the control logic 500 proceeds to step 512. In step 512, the control logic 500 can use the strain gauge data to determine the original maximum peak force. In this embodiment, the control logic 500 can determine that the original maximum peak force can be 140 kips. Then, the control logic proceeds to steps 514 and 516.

[0087] In step 516, the control logic 500 can determine the confidence level. When determining the confidence level, the control logic 500 can incorporate several factors such as the number of data points received in step 510 (which can mean the number of wheel rotations that occurred at a given time and distance in one embodiment), the range of measured values, environmental data, etc. In this embodiment, the control logic 500 can determine that the confidence level is 75%. Then, the control logic 500 proceeds to step 518.

[0088] In step 518, control logic 500 determines whether the original maximum peak force exceeds a first force threshold. In this example, the first force threshold can be 90 kip, which in this example then means that control logic 500 proceeds to step 522 instead of step 520 (e.g., because 140 kip exceeds 90 kip). In step 522, control logic 500 can determine whether an environmental threshold is satisfied. In this example, the environmental threshold can be a temperature threshold of 32°F, and the temperature threshold can be satisfied if the temperature received in step 508 is less than 32°F. In this example, control logic 500 can determine in step 522 that the temperature received in step 508 (0°F) satisfies the environmental threshold, and then control logic 500 proceeds to step 526. In step 526, control logic 500 can generate a calibrated maximum peak force. In this example, control logic 500 can generate a calibrated maximum peak force according to the following equation,

Number

[0089] In step 528, control logic 500 can determine whether the calibrated maximum peak force (126 kip) exceeds the first force threshold (90 kip). Since this is true, control logic 500 then proceeds to step 524. In step 524, control logic 500 can determine whether the calibrated maximum peak force exceeds a third force threshold, which can be 140 kip in this case. In this example, control logic 500 can determine that the calibrated maximum peak value should be used as opposed to the original maximum peak value. Since 126 kip is less than 140 kip, control logic 500 then proceeds to step 530. In step 530, control logic 500 can determine whether the calibrated maximum peak force exceeds a second force threshold, which can be 120 kip in this example. Since 126 kip exceeds 120 kip, control logic 500 then proceeds to step 534.

[0090] In step 534, control logic 500 can determine whether the first confidence threshold is exceeded, and in this example, the first confidence threshold can be 50%. Since the confidence level determined in step 514 was 75% in this example, control logic 500 then proceeds to step 536. In step 536, control logic 500 can determine whether the second confidence threshold is exceeded, and in this example, the second confidence threshold can be 85%. Since control logic 500 determined that the confidence level is 75% in this example, control logic 500 then proceeds to step 542. In step 542, control logic 500 can generate an alert with an assigned level 3 severity level. Control logic 500 can then end, or can wait for a new vehicle detection and repeat the steps described above.

[0091] Advantageously, the force threshold and confidence threshold described herein can be any suitable values depending on the desired alert severity level assignment. For example, decreasing the force threshold, in one embodiment, causes alerts with a relatively high severity level to be generated for vehicles applying relatively poor force to the track. Also, in another example, decreasing the confidence threshold can cause alerts with a relatively high severity level to be generated from received data with a decidedly low fidelity. In contrast, increasing the force threshold and / or confidence threshold can cause the control logic 500 to assign a relatively low severity level for a relatively large impact force determined from relatively inaccurate data.

[0092] FIG. 6 illustrates a flowchart diagram 600 that illustrates control logic for implementing the functions and program steps of a wheel impact load detection (WILD) system according to an exemplary embodiment of the present disclosure. The control logic 600 of the WILD system can be implemented as an algorithm on a server (e.g., server 102), a machine learning module, or other suitable system. In addition, the control logic 500 of the WILD system can implement or incorporate one or more functions of the WILD system 200 including a WILD capture system 202 (having corresponding modules 122, 124, 126, and 128), a WILD calibration system 204 (having corresponding modules 130 and 132), and an alert management system 206 (having corresponding modules 134 and 136). The control logic 600 can be realized by software, hardware, an application programming interface (API), a network connection, a network transfer protocol, HTML, DHTML, JavaScript, Dojo, Ruby, Rails, other suitable applications, or a suitable combination thereof.

[0093] The control logic 600 can utilize the capabilities of a computer platform to generate multiple processes and threads by processing data simultaneously. The speed and efficiency of the control logic 600 are significantly improved by instantiating multiple processes to facilitate wheel impact load detection. However, those skilled in the art of programming will understand that the use of a single processing thread can also be utilized and is within the scope of the present invention.

[0094] The process flow of the control logic 600 of the present embodiment starts at step 602, where the control logic 600 can detect a vehicle, such as a vehicle moving on a track. In one embodiment, the vehicle can be detected by measuring a value that exceeds a rest state or threshold from one or more strain gauges operably coupled to the train track. Next, the control logic 600 proceeds to step 604. At step 604, the control logic 600 can receive environmental data according to the principles of the present disclosure. Next, the control logic 600 proceeds to step 606. At step 606, the control logic 600 can receive sensor data according to the principles of the present disclosure. Next, the control logic 600 proceeds to step 608. At step 608, the control logic 600 can determine a maximum force value from the sensor data received at step 606 according to the principles of the present disclosure. Next, the control logic 600 proceeds to step 610. At step 610, the control logic 600 can determine an original confidence level according to the principles of the present disclosure. Next, the control logic proceeds to step 612.

[0095] In step 612, control logic 600 can determine whether the maximum force value determined in step 608 exceeds a first force threshold. If the maximum force value exceeds the first force threshold, control logic 600 proceeds to step 616. If the maximum force value does not exceed the first force threshold, control logic 600 proceeds to step 614. In step 614, control logic 600 can determine that an alert will not be generated. In step 616, control logic 600 can determine whether the environmental data received in step 604 meets an environmental threshold. If the environmental data meets the environmental threshold, control logic 600 proceeds to step 618. If the environmental data does not meet the environmental threshold, control logic 600 proceeds to step 620.

[0096] In step 618, control logic 600 can reduce the original confidence level. In one embodiment, the confidence level can be reduced corresponding to the deviation of the environmental data from the environmental threshold. For example, if the environmental threshold is a temperature threshold of 32°F and the received environmental data notifies a temperature of 0°F, the confidence level can be further reduced compared to when the received environmental data notified a temperature of, for example, 30°F. In another embodiment, control logic 600 can utilize an equation similar to the one described above in relation to force determination module 130 to adjust the confidence level in contrast to the maximum force value. In another embodiment, control logic 600 can reduce the confidence level such that the confidence level drops below a confidence threshold. For example, if a first confidence threshold is 50%, a second confidence threshold is 85%, and the original confidence level is determined to be 75% in step 610, control logic 600 can reduce the confidence level to 49% so as not to exceed the first confidence threshold. Then, control logic 600 proceeds to step 622.

[0097] In step 622, control logic 600 can utilize the reduced confidence level determined in step 618 and the maximum force value determined in step 608 to assign a severity level in accordance with the principles of the present disclosure. Next, control logic 600 proceeds to step 624. In step 624, control logic 600 can generate an alert having the severity level assigned in step 622 in accordance with the principles of the present disclosure. In step 620, control logic 600 can utilize the original confidence level determined in step 610 and the maximum force value determined in step 608 to assign a severity level in accordance with the principles of the present disclosure. Next, control logic 600 proceeds to step 624.

[0098] FIG. 7 illustrates a flowchart diagram 700 that illustrates the features and program steps of a wheel impact load detection (WILD) method and control logic implementing the same, according to an exemplary embodiment of the present disclosure. The WILD method control logic 700 can be implemented as an algorithm on a server (e.g., server 102), a machine learning module, or other suitable system. In addition to this, the WILD method control logic 700 can implement or incorporate one or more features of the WILD system 200 including the WILD capture system 202 (having corresponding modules 122, 124, 126, and 128), the WILD calibration system 204 (having corresponding modules 130 and 132), and the alert management system 206 (having corresponding modules 134 and 136). The control logic 700 can be realized by software, hardware, and application programming interfaces (APIs), network connections, network transfer protocols, HTML, DHTML, JavaScript, Dojo, Ruby, Rails, other suitable applications, or suitable combinations thereof.

[0099] The control logic 700 can utilize the capabilities of a computer platform to generate multiple processes and threads by processing data simultaneously. The speed and efficiency of the control logic 700 are significantly improved by instantiating multiple processes to facilitate wheel impact load detection. However, those skilled in the art of programming will understand that the use of a single processing thread can also be utilized and is within the scope of the present invention.

[0100] The control logic 700 process flow of the present embodiment starts at step 702, where the control logic 700 can detect a vehicle, such as a vehicle moving on a track. Next, the control logic 700 proceeds to step 704. At step 704, the control logic 700 can receive environmental data according to the principles of the present disclosure. Next, the control logic 700 proceeds to step 706. At step 706, the control logic 700 can receive sensor data according to the principles of the present disclosure. Next, the control logic 700 proceeds to step 708. At step 708, the control logic 700 can determine a maximum force value from the sensor data received at step 706 according to the principles of the present disclosure. Next, the control logic 700 proceeds to step 710.

[0101] At step 710, the control logic 700 can determine whether the maximum force value determined at step 708 exceeds a force threshold according to the principles of the present disclosure. If the maximum force value exceeds the force threshold, the control logic 700 proceeds to step 714. If the maximum force value does not exceed the force threshold, the control logic 700 proceeds to step 712. At step 712, the control logic 700 can determine that an alert will not be generated.

[0102] In step 714, the control can determine whether the environmental data received in step 704 meets the environmental threshold. If the environmental data meets the environmental threshold, the control logic 700 proceeds to step 716. If the environmental data does not meet the environmental threshold, the control logic 700 proceeds to step 718. In step 716, the control logic 700 can assign a severity level. In one embodiment, the control logic 700 can assign a severity level without referring to the maximum force value. In another embodiment, the control logic 700 can assign a severity level that notifies that the environmental threshold is met. For example, if the environmental threshold is met in step 714, the control logic 700 can assign the same severity level to all potential alerts, regardless of the maximum force value. In step 718, the control logic 700 can utilize the maximum force value determined in step 708 when assigning a severity level, in accordance with the principles of the present disclosure. Then, the control logic 700 proceeds to step 720. In step 720, the control logic 700 generates an alert having the severity level assigned in step 716 or step 718, in accordance with the principles of the present disclosure.

[0103] Those skilled in the art will understand that the systems and methods disclosed herein can implement multiple force thresholds, confidence thresholds, and environmental thresholds within the same process flow to adapt alert generation and severity level assignment to fit specific needs. In one embodiment, it is possible to use different types of environmental thresholds within the same flow. For example, the process flow can include temperature thresholds, pressure thresholds, humidity thresholds, and / or any other type of environmental threshold related to environmental conditions that can affect wheel impact load detection. In another embodiment, a time factor can be included as an environmental threshold or, alternatively, can be the threshold itself. For example, the environmental threshold can require that a specific temperature be exceeded over a specific period of time before the threshold can be satisfied, and in another example, the environmental threshold can require that the temperature be always below a specific temperature over a specific period of time before the threshold can be satisfied. In another embodiment, the systems and methods disclosed herein can incorporate time thresholds that can affect the assignment of severity levels. For example, when an environmental threshold is satisfied over a predetermined duration, the assigned severity level can be lower than when the environmental threshold is satisfied over a relatively long duration. In another embodiment, the environmental data and environmental thresholds can refer to the environment of the rail. For example, the environmental data can include the temperature of the rail, and the environmental threshold can include a temperature threshold related to the temperature of the rail.

[0104] The present disclosure realizes at least the following advantages. 1. Optimization of severity level assignment in wheel impact load detection alerts for considering environmental conditions; 2. Prioritization of wheel impact load detection alerts for considering environmental conditions; 3. Calibration of the measured maximum force value applied to the rail for adjusting for extreme temperatures; and 4. Provision of a method for ranking alerts considering environmental conditions.

[0105] Those skilled in the art will readily understand that these advantages of the present system (as well as the advantages shown in the overview) and the objectives cannot be achieved without a specific combination of the computer hardware and other structural components and mechanisms assembled within and described in the present invention system. It should be further understood that various programming tools known to those skilled in the art are available for implementing the features and operation controls described above. Furthermore, the specific choice of one or more programming tools can be determined by the specific objectives and constraints imposed on the implementation plan selected to realize the concepts described in this specification and the appended claims.

[0106] The description in this patent document should not be construed as meaning that any particular element, step, or function must be an essential or important element that must be included in the scope of the claims. Also, the claims are not intended to invoke 35 U.S.C. § 112(f) in relation to any of the appended claims or claim elements unless the exact terms "means for" or "step for" are expressly used in a particular claim following a participle phrase that identifies a function. The use of terms such as "mechanism", "module", "device", "unit", "component", "element", "member", "apparatus", "machine", "system", "processor", "processing device", or "controller" in the claims, without limitation, can be understood and interpreted by those skilled in the relevant technical field as meaning a structure known to them in a state further modified or improved by the characteristics of the claims themselves, and is not intended to invoke 35 U.S.C. § 112(f).

[0107] The present disclosure can be implemented in other specific forms without departing from its spirit or essential characteristics. For example, each of the new structures described herein can be modified to suit specific local changes or requirements while maintaining its basic configuration or mutual structural relationship or while performing the same or similar functions described herein. Accordingly, the present embodiment is to be regarded in all aspects as illustrative and not restrictive. Therefore, the scope of the present invention can be established by the appended claims rather than the above description. Accordingly, all modifications included within the meaning and scope of the equivalence of the claims are to be construed as being included therein. Furthermore, the individual elements of the claims are not well understood, nor are they ordinary or conventional. Instead, the claims are directed to inventive concepts that are not conventional as described herein.

Claims

1. A system for generating a railway alert related to wheel impact load detection sensor data, having a memory with a first database having a plurality of sensor data, a threshold value, and specifications related to at least a part of a vehicle and a track, a network-connected computer processor operably coupled to the memory and having the ability to execute machine-readable instructions to execute program steps, and having, the program steps including, detecting the vehicle on the track, receiving environmental data, receiving sensor data corresponding to one or more forces exerted by the vehicle on the track, determining an original maximum peak force from the sensor data, comparing the original maximum peak force with a first force threshold, when the original maximum peak force exceeds the first force threshold, determining whether the environmental data meets an environmental threshold, when the environmental data meets the environmental threshold, generating a calibrated maximum peak force via the processor, using the original maximum peak force or the calibrated maximum peak force when assigning a severity level, generating an alert including the severity level, and including, when the original maximum peak force is less than the first force threshold, no alert is generated, the system.

2. The system according to claim 1, wherein the calibrated maximum peak force is generated by normalizing the original maximum peak force using an operating variable.

3. The program steps include, The step of generating a plot of the sensor data; The step of comparing a plurality of points on the plot corresponding to the sensor data; The step of recognizing a static peak force trend within the plot; The step of determining a weight value using the static peak force trend; The step of calculating a dynamic force value using the original maximum peak force and the weight value; The system according to claim 1, further comprising:

4. The system according to claim 1, wherein the program step further comprises the step of determining a confidence level of the sensor data accuracy.

5. The system according to claim 4, wherein the program step further comprises the step of utilizing the confidence level when assigning the severity level.

6. The system according to claim 4, wherein the severity level can vary based on the magnitude of the original maximum peak force or the calibrated maximum peak force and the confidence level.

7. The system according to claim 1, wherein when the original maximum peak force exceeds the first force threshold and the calibrated maximum peak force is less than the first force threshold, the severity level notifies that the calibrated maximum peak force was utilized when assigning the severity level.

8. The system according to claim 1, wherein the vehicle is a train.

9. When the environmental data does not meet the environmental threshold, the original maximum peak force is utilized when assigning the severity level, and When the environmental data meets the environmental threshold, the calibrated maximum peak force is utilized when assigning the severity level. The system according to claim 1.

10. The system according to claim 1, wherein the environmental data includes weather data.

11. A method for compensating for environmental conditions in wheel impact load detection, comprising: detecting a vehicle on a track; generating at least one record including a date, a time, a direction of the vehicle, and a number of axles of the vehicle via one or more processors; receiving environmental data; receiving sensor data from at least one strain gauge coupled to the track; determining an original maximum force value from the sensor data; comparing the original maximum force value with a first force threshold; when the environmental data meets an environmental threshold, generating a calibrated maximum force value by calibrating the original maximum force value with an operating variable via the one or more processors; when the original maximum force value exceeds the first force threshold and the calibrated maximum force value is not generated, using the original maximum force value to assign a first severity level; when the original maximum force value exceeds the first force threshold and the calibrated maximum force value is generated, using the calibrated maximum force value to assign a second severity level; generating an alert including the first or second severity level; updating the at least one record; and when the original maximum force value is less than the first force threshold, the at least one record is updated without generating an alert.

12. The method according to claim 11, wherein the environmental data includes temperature, and the environmental threshold is a temperature threshold.

13. The method according to claim 11, further comprising the step of determining a confidence level in the accuracy of the sensor data.

14. The method according to claim 13, wherein the confidence level is utilized with the original maximum force value to assign the first severity level.

15. The method according to claim 13, wherein the confidence level is utilized with the calibrated maximum force value to assign the second severity level.

16. A method for compensating for variable rail tension generated by environmental conditions in wheel impact load detection, comprising: detecting a vehicle on a track; receiving environmental data; receiving sensor data from at least one strain gauge coupled to the track; determining a maximum force value from the sensor data via one or more processors; determining whether the environmental data meets an environmental threshold when the maximum force value exceeds a first force threshold; assigning a first severity level when the maximum force value exceeds the first force threshold and the environmental data meets the environmental threshold; assigning a second severity level when the maximum force value exceeds the first force threshold and the environmental data does not meet the environmental threshold; generating an alert including the first or second severity level; and no alert is generated when the maximum force value is less than the first force threshold.

17. The method according to claim 16, further comprising the step of utilizing a confidence level when assigning the first or second severity level.

18. The method according to claim 17, wherein the confidence level is reduced when the environmental threshold is satisfied. **Claim 19** The method according to claim 16, wherein the environmental threshold is satisfied when the temperature is 0 °C or lower. **Claim 20** The method according to claim 16, further comprising the step of updating a record to notify that the alert includes the first or second severity level.

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