System and method for wheel impact load detection

Abstract

To provide a wheel impact load detection system for detecting defects in a wheel of railroad vehicles, and, among others, a system for taking into account variable environmental conditions and / or variable rail tension in assigning severity levels in order to mitigate unneeded stoppage of railway traffic.SOLUTION: The system can receive data from sensors and / or a weather station to determine a maximum force applied to a rail, and can subsequently calibrate the determined maximum force to account to environmental conditions. Additionally, the present disclosure can assign severity levels and generate alerts with the assigned severity levels, and such severity levels can facilitate the proper prioritization of the alerts.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present disclosure relates generally to wheel impact load detection in railway infrastructure. [Background technology]

[0002] Current rail infrastructure commonly installs wheel impact load detection systems to notify rail personnel of potential defects in the wheels of vehicles traveling on the railroad track. Wheel impact load detection systems typically include strain gauges coupled to the rail that are operable to measure strain or stress applied to the rail. As a train or other vehicle travels over a section of rail equipped with strain gauges, the strain gauges can measure an increase in strain generated by the vehicle. This increase in strain can be correlated to a measurement of the force exerted by one or more wheels of the vehicle against the rail. The strain gauges are often distributed over a sufficient length of the rail to allow for the collection of force measurements corresponding to the entire circumference of a given wheel, rather than just a specific section.

[0003] Railroad vehicles are carefully loaded to ensure that the combined weight of the vehicles and cargo does not exceed the weight limits of the track and vehicle infrastructure, and if there are no defects in the vehicle wheels or the rails on which they travel, strain gauge measurements will generally reflect that the train moving over the rail is exerting an acceptable force on the rail. However, if a wheel has a defect (e.g., a flat created by sliding or rolling fatigue (mechanical shelling), etc.), the result can be increased forces applied by the vehicle to the track through the defective wheel. These forces can be measured by strain gauges and ultimately detected by a comprehensive wheel impact load detection system, which can alert railroad personnel to the potential defect.

[0004] Due to the need to maintain train movement to meet schedules and delivery timelines, rail systems often prioritize alerts corresponding to many potential issues, including detected wheel impact loads signaling wheel defects. Some alerts may signal serious problems and require the vehicle to be stopped and inspected immediately, while other alerts may indicate that inspection can wait until the next time the train is serviced. In the context of wheel impact load detection, such prioritization generally depends on the amount of force detected by the strain gauge; the greater the force, the more relatively serious the alert and the higher the priority it is given. As an example of this prioritization, the Association of American Railroads (AAR) has promulgated different levels of concern corresponding to various detected force values ​​in wheel impact load detection, which are often measured in units of "kips," or thousands of pounds. The AAR considers a wheel to have a "problem" (i.e., a potential problem for the railroad) if the wheel's kip value is greater than 90. In addition to guidelines from the AAR, railroads will often maintain their own internal priority system for problem wheels to assist them in managing the sheer volume of alerts that can be generated on any given date. However, the time constraints inherent in rail operations mean that incorrect prioritization can be devastating to the efficiency and productivity of rail operations. Therefore, ensuring proper prioritization of alerts is key to the successful and reliable implementation of a wheel impact load detection system. Summary of the Invention [Problem to be solved by the invention]

[0005] The present disclosure provides technical advantages of a system and method for wheel impact load detection (WILD) that can prioritize alerts by considering environmental conditions. The system can consider variability in rail tension created by environmental conditions (e.g., temperature, pressure, humidity, etc.) when assigning severity levels to alerts. The system can receive data indicative of forces exerted by a vehicle against a track, calibrate the received data to consider environmental conditions, utilize the calibrated data when assigning severity levels that control prioritization in a rail system, and then generate alerts having 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 is capable of calculating values ​​from received data, thereby using the received data to calibrate associated values, determining when to use original or calibrated data, and comparing the original or calibrated values ​​to predetermined thresholds (as appropriate) to facilitate appropriate prioritization. Such specialized processing can also provide the benefit of increased operational efficiency for rail systems by mitigating erroneously prioritized alerts that may be generated, for example, by increased rail tension due to linear rail contraction during cold weather.

[0007] The present disclosure improves the performance and functionality of the system itself by implementing specialized algorithms tailored to data related to environmental conditions in the vicinity of sensors (e.g., strain gauges, load cells, accelerometers, etc.). The system can assign a severity level related to received WILD sensor data in light of the environmental conditions related to the received environmental data. In contrast, conventional systems simply rely on data that is often incomplete and assume uniform conditions without adjusting values ​​based on feedback to account for, for example, variable rail tension and / or linear contraction / expansion, resulting in improperly prioritized alerts that can reduce operational efficiency and often lead to catastrophic shutdowns of nearly healthy railcars. In certain embodiments, the disclosed WILD system not only determines when data needs to be calibrated, but can also determine whether the calibration should change the severity level of an alert.

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

[0009] It is an object of the present disclosure to provide a system for wheel impact load detection and alert generation. It is a further object of the present disclosure 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 problem]

[0010] In one embodiment, a system for generating railroad alerts related to wheel impact load detection sensor data may include a memory having a first database having a plurality of sensor data, thresholds, and specifications related to a vehicle and at least a portion of a track; and a network-connected computer processor operatively coupled to the memory and capable of executing machine-readable instructions to perform program steps including detecting a vehicle on the track; receiving environmental data; receiving sensor data corresponding to one or more forces exerted by the vehicle against the track; determining an original maximum peak force from the sensor data; comparing the original maximum peak force to a first force threshold; if the original maximum peak force exceeds the first force threshold, determining whether the environmental data satisfies the environmental threshold; if the environmental data satisfies the environmental threshold, generating, via the processor, a calibrated maximum peak force; utilizing the original maximum peak force or the calibrated maximum peak force in assigning a severity level; and generating an alert including the severity level; and if 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 the manipulated variable. In this case, the program steps may further include generating a plot of the sensor data, comparing points on the plot corresponding to the sensor data, recognizing a static peak force trend in 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 may further include determining a confidence level in the accuracy of the sensor data. In this case, the program steps may further include utilizing the confidence level in assigning a severity level. In this case, the severity level may vary based on the magnitude of the original maximum peak force or the calibrated maximum peak force and the confidence level.In this case, if 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 indicates that the calibrated maximum peak force was used in assigning the severity level. In this case, the vehicle is a train. In this case, if the environmental data does not satisfy the environmental threshold, the original maximum peak force is used in assigning the severity level, and if the environmental data satisfies the environmental threshold, the calibrated maximum peak force is used in 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, via one or more processors, at least one record including a date, a time, a direction of the vehicle, and a number of axles of the vehicle; 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 to a first force threshold; and, if the environmental data satisfies the environmental threshold, generating a calibrated maximum force value by calibrating, via the one or more processors, the original maximum force value with a manipulated variable. the original maximum force value exceeds a first force threshold and a calibrated maximum force value is not generated; utilizing the original maximum force value to assign a first severity level if the original maximum force value exceeds a first force threshold and a calibrated maximum force value is not generated; utilizing the calibrated maximum force value to assign a second severity level if the original maximum force value exceeds the first force threshold and a calibrated maximum force value is generated; generating an alert including the first or second severity level; and updating at least one record, wherein if the original maximum force value is less than the first force threshold, the 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 utilized together with the original maximum force value to assign the first severity level. In this case, the confidence level is utilized together with the calibrated maximum force value to assign the second severity level.

[0012] In another embodiment, a method for compensating for variable rail tensions generated by environmental conditions in wheel impact load detection may include detecting a vehicle on a track, receiving environmental data, receiving sensor data from at least one strain gauge coupled to the track, determining, via one or more processors, a maximum force value from the sensor data, determining if the environmental data satisfies an environmental threshold if the maximum force value exceeds a first force threshold, assigning a first severity level if the maximum force value exceeds the first force threshold and the environmental data satisfies the environmental threshold, assigning a second severity level if the maximum force value exceeds the first force threshold and the environmental data does not satisfy the environmental threshold, and generating an alert including the first or second severity level, wherein no alert is generated if the maximum force value is less than the first force threshold. Further, the method may include utilizing a confidence level in assigning the first or second severity level, wherein the confidence level is reduced if the environmental threshold is satisfied. In this case, the environmental threshold is met when the temperature is equal to or less than 0° C. Further included is the step of updating the record to indicate that the alert includes the first or second severity level.

[0013] The present disclosure can be readily understood from the following detailed description, provided in conjunction with the accompanying drawings, which 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 characters. Objects and elements in the drawings are not necessarily drawn to scale, proportion, or precise relationships. Instead, emphasis is placed upon illustrating the principles of the present disclosure. [Brief explanation of the drawings]

[0014] [Figure 1] 1 shows a schematic diagram of an example wheel impact load detection system according to one or more example embodiments of the present disclosure. [Figure 2]1 shows an example block diagram of a wheel impact load detection system according to one or more example embodiments of the present disclosure. [Figure 3] 1 shows a flowchart illustrating wheel impact load detection system flow control logic in accordance with one or more exemplary embodiments of the present disclosure. [Figure 4] 1 shows a flowchart illustrating the control logic of a wheel impact load detection system according to one or more example embodiments of the present disclosure. [Figure 5A] 1 shows a flowchart illustrating the control logic of a wheel impact load detection system according to one or more example embodiments of the present disclosure. [Figure 5B] 1 shows a flowchart illustrating the control logic of a wheel impact load detection system according to one or more example embodiments of the present disclosure. [Figure 6] 1 shows a flowchart illustrating the control logic of a wheel impact load detection system according to one or more example embodiments of the present disclosure. [Figure 7] 1 shows a flowchart illustrating the control logic of a wheel impact load detection system according to one or more example embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0015] The preferred version of the present disclosure and its various features and advantageous details presented in the following written description will be more fully explained by reference to the non-limiting examples contained in the accompanying drawings and detailed in the following description. Descriptions of well-known components have been omitted so as not to unnecessarily obscure the main features described herein. The examples used in the following description are intended to facilitate understanding of how the present disclosure can be implemented and practiced. Therefore, these examples should not be interpreted as limiting the scope of the claims.

[0016] FIG. 1 illustrates a schematic diagram of a wheel impact load detection (WILD) system 100 in accordance with one or more embodiments of the present disclosure. The system 100 may include one or more servers 102 operably coupled to a database 104. The server 102 may be operably coupled to one or more clients 110, 112, 114, 116 via a network connection 106. The clients may be physical devices (e.g., a mobile phone 116, a computer 110, 112, a tablet 114, a wearable device, or other suitable devices), programs, or applications. In another embodiment, the server 102 may be operably coupled to a weather station 108 via the network 106. For example, the weather station 108 may be a collection 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 may be a networked computer 108 in operative connection with a server capable of receiving and / or acquiring environmental data and transmitting the environmental data to the server 102. The WILD system 100 may be integrated with a rail system or rail infrastructure to facilitate detection of defects in rail components. Those skilled in the art will appreciate that the detections, captured data, measurements, determinations, alerts, etc. embodied by the WILD system 100 may be communicated to and / or accessible from the rail system primarily via the network 106 or other operative connection. In one embodiment, the server 102 may include machine-readable instructions 120, and in another embodiment, the server 102 may have access to the machine-readable instructions 120. In another embodiment, the machine-readable instructions may include instructions related to the vehicle detection module 122, the environmental data capture module 124, the geo-positioning module 126, the vehicle data capture module 128, the force determination module 130, the force calibration module 132, the alert generation module 134, and / or the alert delivery module 136.

[0017] The above-described 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 one another via a network 140 so that data can be transmitted. The network 106 can be the Internet, an intranet, or other suitable network. Data transmissions can be encrypted or unencrypted over VPN tunnels or other suitable communication means. The network 106 can be a WAN, LAN, PAN, or other suitable network type. Network communications between clients, the server 102, or any other system components can be encrypted using PGP, Blowfish, Twofish, AES, 3DES, HTTPS, or other suitable encryption. The system 100 can be configured to provide communications via the various systems, components, and modules disclosed herein via application programming interfaces (APIs), PCI, PCI-Express, ANSI-X12, Ethernet, Wi-Fi, Bluetooth, or other suitable communications protocols or media. Additionally, third party systems and databases may be operably coupled to the system components via network 106 .

[0018] Data transmitted to and from components of the system 100 (e.g., server 102, weather station 108, and clients) can include any format, including JavaScript Object Notation (JSON), TCP / IP, XML, HTML, ASCII, SMS, CSV, representational state transfer (REST), or other suitable formats. Data transmissions can include, or be encapsulated and packaged in any suitable format having, messages, flags, headers, header properties, metadata, and / or bodies.

[0019] The one or more servers 102 may be implemented in hardware, software, or any suitable combination of hardware and software for this purpose, and may include one or more software systems operating on one or more servers having one or more processors 118 with access to memory 104. The one or more servers 102 may include electronic storage, one or more processors, and / or other components. The one or more servers 102 may include communication lines, connections, and / or ports to enable the exchange of information over the network 106 and / or other computing platforms. The one or more servers 102 may also include multiple hardware, software, and / or firmware components that cooperate to provide the functionality attributed to the one or more servers 102 herein. For example, the one or more servers 102 may be implemented by a cloud of computing platforms that cooperate as the one or more servers 102, including Software-as-a-Service (SaaS) and Platform-as-a-Service (PaaS) capabilities. Additionally, one or more of the servers 102 may also include memory 104 therein.

[0020] The memory 104 may include electronic storage, which may include non-transitory storage media that electronically store information. The electronic storage media of the electronic storage may include one or both of system storage, which may be provided integrally (e.g., substantially non-removably) with one or more servers 102, and / or removable storage, which may be removably connectable to one or more servers 102, for example, via a port (e.g., a USB port, a Firewire port, etc.) or a drive (e.g., a disk drive, etc.). The electronic storage may 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, RAM, etc.), a semiconductor storage medium (e.g., a flash drive, etc.), and / or other electronically readable storage medium. The electronic storage may include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and / or other virtual storage resources). The electronic storage may include a database or a public or private distributed ledger (e.g., a blockchain). The electronic storage may 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 may enable the one or more servers to function as described herein. The electronic storage may also include third-party databases accessible via the network 106.

[0021] One or more processors 118 may be configured to provide data processing capabilities within one or more servers 102. Thus, one or more processors 118 may 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 may be a single entity or may include multiple processing units. These processing units may be physically located within the same device, or one or more processors 118 may represent the processing functionality of multiple devices or software functions acting alone or in concert.

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

[0023] One or more servers 102 may be configured with machine-readable instructions 120 having one or more functional modules. The machine-readable instructions 120 may be implemented on one or more servers 102 having one or more processors 118 with access to memory 104. The machine-readable instructions 120 may be a single networked node or a machine cluster that may include a distributed architecture of multiple networked nodes. The machine-readable instructions 120 may include control logic for implementing various functions, as described in further detail below. The machine-readable instructions 120 may include specific functions associated with the WILD system 100. In addition, the machine-readable instructions 120 may include smart contracts or multi-signature contracts that may process data, read data, and write data to a database, a distributed ledger, or a blockchain.

[0024] 2 illustrates a schematic diagram of a wheel impact load detection system 200 according to one or more example embodiments of the present disclosure. WILD system 200 may include a WILD data capture system 202, a WILD calibration system 204, and an alert management system 206. In one example embodiment, WILD data capture system 202 may include a vehicle detection module 122, an environmental data capture module 124, a geolocation module 126, and a vehicle data capture module 128. Vehicle detection module 122, environmental data capture module 124, geolocation module 126, and vehicle data capture module 128 may implement one or more algorithms to facilitate data capture related to wheel impact load detection, including recognition, location, and detection algorithms. In one embodiment, WILD data capture system 202 may be configured to activate upon detection of a vehicle on the track and subsequently capture a myriad of 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 operative communication with strain gauges, cameras, LIDAR, radar, or any other device or mechanism suitable for detecting movement (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 operative communication with a weather station 108, which can collect, store, and provide data related to weather conditions such as temperature, precipitation, pressure, and humidity, among other things. In one example, detection of a vehicle by the vehicle detection module 122 can activate the environmental data capture module 124 so 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 asynchronously transmit (wired or wirelessly) the environmental data to the server 102 based on one or more thresholds stored on the weather station 108.

[0026] In another embodiment, the geo-positioning module 126 can be configured to receive, obtain, generate, transmit, and / or store locations of detected vehicles and / or tracks. For example, the wild data capture system 202 can be operatively coupled to a global positioning system that can track vehicle locations, such that when the vehicle detection module 122 detects a vehicle, the geo-positioning module 126 can receive a vehicle location from the global positioning system. In another embodiment, the wild data capture system 202 can be operatively 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 detect a vehicle via the coupled sensor, and upon detection, the geo-positioning module 126 can receive a location from the coupled sensor. In another embodiment, the geo-positioning module 126 can obtain multiple stored locations corresponding to multiple vehicles, tracks, sensors, or other components that the geo-positioning module 126 can associate with detections from the vehicle detection module 122. In another embodiment, the geo-positioning module 126 may transmit the location, such as the location of the detected vehicle and / or the portion of the track on which the vehicle is traveling, to another system (e.g., a third-party system).

[0027] In another embodiment, vehicle data capture module 128 can be configured to capture vehicle-specific data. For example, vehicle data capture module 128 can be operatively coupled to an RFID reader, which can facilitate receiving data from, for example, an RFID chip on the vehicle. In another embodiment, the RFID chip can include the date, time, the number of axles on the vehicle, the vehicle's identification, the direction the vehicle is traveling, and / or the load the vehicle is bearing. In another embodiment, the RFID chip can include multiple pieces of data related to where the vehicle has been, where the vehicle is currently located, and where the vehicle is headed. In another example, vehicle data capture module 128 can be operatively coupled to any other device or component suitable for capturing data related to the vehicle, such as a strain gauge, a camera, radar, or the like. For example, vehicle data capture module 128 can be coupled to a camera that can detect the vehicle's serial number or other identifying information. Vehicle data capture module 128 can receive data from, for example, a sensor that can measure stress applied to a rail or track. In another embodiment, vehicle data capture module 128 can receive data from strain gauges coupled to rails of the track that can measure stress as it changes with the passage of vehicles on the track. In another example, vehicle data capture module 128 can receive data from any one or more sensors that can measure forces applied to the rails, such as by vehicles moving on the rails. In another embodiment, vehicle data capture module 128 can receive data related to forces applied to one and / or more rails over a predetermined period of time.

[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 use the data in determining one or more forces applied to a portion of the track and / or rail. For example, the force determination module 130 can receive data from the vehicle data capture module 128; in another example, the force determination module 130 can receive data having particular units and convert the data to reflect the units of force. In one embodiment, the force determination module 130 can receive multiple sensor data captured by the vehicle data capture module 128 and use the sensor data to determine the maximum force (maximum peak force) applied to the rail detected by the sensor (original maximum force). In one embodiment, force-determination module 130 can determine the vehicle weight and impact force from the determined maximum force. In another embodiment, force-determination module 130 can be configured to generate a plot of the sensor data and identify trends within the plot that may correspond to several related force measurements. For example, and in one embodiment, force-determination module 130 can generate a plot showing static peak force trends and maximum forces in a graph displaying the magnitude of the measured force over time.

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

[0030] In another embodiment, the force determination module 130 can distinguish between vehicle weight and impact forces (dynamic forces), such as those that may be generated by imperfections or defects in the wheels, rails, vehicle, or other components participating in the application of force to the rails. For example, the static peak force trend can be correlated with vehicle weight because the majority of a vehicle's wheels may generally be defect-free, and the force exerted by the vehicle against the track via the wheels may remain largely unchanged as intact portions of the wheels support the vehicle's weight. In another example, as the wheels rotate on the track and position wheel defects between the track and the vehicle, the force exerted by the vehicle against the rails may increase, and such increase from the static peak force trend (the apex of which may be considered a maximum force value) may correlate with dynamic force. In another example, the force determination module can utilize the determined vehicle weight and vehicle maximum force in determining 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 values ​​determined by the force determination module 130, such as to account for environmental conditions. For example, the force calibration module 132 can receive a maximum force value from the force determination module 130 and 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 purposes of assigning, generating, and providing alert severity levels) to account for increased rail tension, such as that which may be produced by linear rail contraction due to a cold snap. In another example, the force calibration module 132 can be configured to adjust the maximum force value to account for reduced rail tension, such as that which 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 conditions, including, without limitation, temperature, pressure, humidity, wind speed, precipitation, UV index, and storm patterns.

[0032] In one embodiment, the force calibration module 132 can calibrate the maximum force value using a manipulated variable. For example, the force calibration module 132 can apply a mathematical formula that implements the manipulated variable, which can vary depending on the condition being compensated for. In another embodiment, the manipulated variable can be a constant that can correspond to a particular temperature threshold. In another embodiment, the manipulated variable can be a constant that corresponds to the material in the rail. In another embodiment, the manipulated variable can be a constant that corresponds to the normal linear expansion or contraction of the rail at a particular temperature. In one embodiment, the force calibration module 132 can utilize the following formula:

number

[0033] In one embodiment, K original may refer to the maximum force value determined by the force determination module 130. original may include a value in units of "kips", i.e., a measurement in thousands of pounds (e.g., 1 kip = 1000 lbs). adjustedmay refer to the calibrated and / or adjusted maximum force value. In another embodiment, V may be a 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 may be derived by establishing a relationship, such as a 100 kip value at 0°F being adjusted downward to 90 kip. In one embodiment, the value of V may vary depending on the temperature being compensated (e.g., hot or cold). Also, in another embodiment, V may vary depending on a temperature threshold. In another embodiment, the temperature threshold may vary depending on whether cold or warm weather is being compensated. For example, a temperature threshold may be designed to account for reduced rail tension due to relatively warm weather, and may be adjusted to account for reduced rail tension at 0°F. deviation from temperature threshold may mean a number of degrees above a temperature threshold (for example, 100°F). For example, if the temperature is 110°F and the temperature threshold is 100°F, then °F deviation from temperature threshold In another embodiment, the above formula can be modified to calibrate the maximum force value to account for temperatures below freezing (e.g., below the freezing point of water) as follows:

number

[0034] In another embodiment, the force calibration module 132 can take into account the amount of time that a predetermined environmental condition has existed. For example, if the temperature is above or below (or otherwise meets) the temperature threshold for a set amount of time (e.g., two 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 relatively higher temperatures present for a relatively longer period of time, and can provide a decreased calibrated maximum force value to account for relatively lower temperatures present for a relatively longer period of time, compared to the original maximum force value. 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 delivery 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 information about a vehicle detection, the vehicle's location, environmental conditions, and / or the vehicle's identity. In another example, the alert generation module 134 can receive a maximum force value (and / or a calibrated maximum force value) and 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 a 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, alerts can be assigned a severity level (e.g., levels 1-3) based on the (calibrated) maximum force value and confidence level, a tabulated example of which is shown below:

[0036] [Table 1]

[0037] In one embodiment, the severity level can inform the rail system of recommended actions to address the alert. For example, a level 1 alert may inform the rail system that the vehicle should be stopped and inspected immediately. In another example, a level 2 alert may inform the rail system that inspection (e.g., of the vehicle's wheels and / or axles) can wait until the next scheduled inspection. In another example, a level 3 alert may inform the rail system that inspection can wait until the vehicle is completely emptied of cargo and / or until its last visit to a machine facility before going offline. In another embodiment, multiple other alert severity levels are possible. For example, an opportunistic alert may inform the rail system that a vehicle (or a specific part of the vehicle, such as a wheel) needs inspection only when the vehicle is undergoing repair for a different issue. In another example, a level 4 alert may inform the rail system that the alert severity has been adjusted as a result of calibration for environmental conditions. In another embodiment, a Level 4 alert may indicate that the calibrated maximum force value has dropped below the force threshold while the original maximum force value has exceeded the force threshold. In another embodiment, a Level 4 alert may indicate the same recommended action as an opportunistic alert, but may further inform the rail system that the alert was generated by considering environmental conditions.

[0038] In another embodiment, the severity level of an alert generated by the alert generation module 134 may be based at least in part on a (calibrated) maximum force value. As an example, and with reference to the table above, a kip value of 140 or greater (e.g., 140,000 lbs) may be classified as “most severe,” a kip value between 120 and 140 may be “severe,” and a kip value between 90 and 120 may be “least severe.” In another embodiment, a kip value between 80 and 90 may generate an opportunistic alert. Other kip values ​​or (calibrated) maximum force values ​​may be used in these categories instead of those described above. In another embodiment, the alert generation module 134 may use several force and confidence thresholds when assigning severity levels. For example, if the maximum force value drops below a first force threshold (e.g., 80 kip), the alert generation module 134 may determine not to generate an alert without considering the confidence level. In another example, if the confidence level drops below a first confidence threshold (e.g., 15%), the alert generation module 134 may determine not to generate an alert without considering the maximum force value. In another embodiment, the alert generation module 134 may utilize several force and / or confidence thresholds to assign a severity level for the generated alert.

[0039] In another embodiment, the alert delivery module 136 of the alert management system 206 can transmit alerts throughout the rail system. For example, the alert delivery module 136 can receive alerts with assigned severity levels from the alert generation module 134 and communicate the alerts to a network or personnel, a networked server, or any other component in operative connection with the alert delivery module 136. In one embodiment, the alert delivery module 136 can transmit the alerts via messages, records, or any other suitable form of communication. In another embodiment, the alert delivery module 136 can update records with the generated alerts.

[0040] 3 shows a flowchart 300 illustrating control logic for implementing functions of a method for wheel impact load detection (WILD) in accordance with an exemplary embodiment of the present disclosure. The WILD control logic 300 may be implemented as an algorithm on a server (e.g., server 102), a machine learning module, or other suitable system. Additionally, the WILD control logic 300 may implement or incorporate one or more features of the WILD system 200, including the WILD capture system 202 (with corresponding modules 122, 124, 126, and 128), the WILD calibration system 204 (with corresponding modules 130 and 132), and the alert management system 206 (with corresponding modules 134 and 136). The WILD control logic 300 may be implemented using software, hardware, an application programming interface (API), a network connection, a network transfer protocol, HTML, DHTML, JavaScript, Dojo, Ruby, Rails, or other suitable application, or any suitable combination thereof.

[0041] The WILD control logic 300 can exploit the capabilities of a computer platform to create multiple processes and threads by processing data simultaneously. The speed and efficiency of the WILD control logic 300 is 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 may also be utilized and is within the scope of the present disclosure.

[0042] The process flow of the WILD control logic 300 of this embodiment begins at step 302, where the control logic 300 is instantiated. In one embodiment, the control logic 300 may be configured to receive data from sensors or other data collection devices on and / or near the railroad track, and instantiating the control logic 300 at step 302 prepares the control logic 300 to receive and process the expected data. The control logic then proceeds to step 304.

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

[0044] In step 306, control logic 300 may capture environmental data, such as weather data. In one embodiment, step 306 may be associated with and / or considered to be performed by environmental data capture module 124. In another embodiment, step 306 may include receiving weather data from a weather station (e.g., weather station 108). In another embodiment, step 306 may include receiving one or more temperature measurements, such as an ambient temperature measurement. Control logic 300 then proceeds to step 310.

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

[0046] In step 308, control logic 300 may capture wheel impact load detection (WILD) data. In one embodiment, step 308 may be associated with and / or considered to be performed by vehicle data capture module 128 and / or geolocation module 126. In another embodiment, control logic 300 may receive train identity, measurements (e.g., strain and / or stress measurements on the track), train location, and other relevant data. Control logic 300 then proceeds to steps 312 and 314.

[0047] At step 314, the data captured at step 308 may be forwarded and / or transmitted to an alert system (e.g., alert management system 206). For example, and in one embodiment, control logic 300 may generate a record including the data and transmit the record to the alert system, such as to facilitate the alert system generating an alert. In another embodiment, control logic 300 may send a message to the alert system conveying the data. Control logic 300 then proceeds to step 318.

[0048] In step 318, the data transferred to the alert system 314 may be stored in an alert system database or any other database in operative connection with the control logic 300. The control logic 300 then proceeds to step 332.

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

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

[0051] In step 316, if the temperature threshold is met in step 310, the control logic 300 can use the original max-peak and the temperature delta (e.g., the amount of deviation of the measured temperature from the temperature threshold) to calculate an adjusted max-peak (calibrated max-peak force). In one embodiment, step 316 can be associated with and / or considered to be performed by the force calibration module 132. If the temperature threshold is not met in step 310, the temperature delta can be zero, meaning that the adjusted max-peak can be equal to the original max-peak. The control logic 300 then 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 may also be associated with and / or considered to be performed by the force calibration module 132 and / or the alert delivery module 136. The control logic 300 then proceeds to step 322.

[0053] In step 322, control logic 300 assigns a severity level to the 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 performed by alert generation module 134. In one embodiment, the severity level assigned in step 322 can differ 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, 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 with a need to know of recommended action, while the alert generated and published in step 332 can remain in a pending state until it is closed according to the process flow of control logic 300. In another embodiment, the severity level assigned in step 322 may override the severity level assigned in step 332, such that the alert generated and published in step 332 may be modified to reflect the new severity level assigned in step 322. Control logic 300 then proceeds to step 324.

[0054] Then, in step 324, control logic 300 may receive input as to whether defects (e.g., billable defects) were found after the alert was applied. For example, once an inspection has been performed according to the assigned severity level, control logic 300 may receive a command notifying it whether defects were found. If defects are found, control logic 300 proceeds to step 326. If no defects are found, control logic proceeds to step 328.

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

[0056] At step 330, the alert having the assigned severity level may be closed. For example, if control logic is notified at step 326 that the defect has been repaired, control logic 300 may disable the alert so that control logic 300 can close the alert. Similarly, if control logic 300 is notified at step 328 that an inspection has been performed, control logic 300 may close the alert. Control logic 300 then proceeds to steps 332 and 334. At step 332, control logic 300 announces that the alert has been closed. At step 334, control logic 300 may exit or may wait for a new train detection and repeat the steps described above.

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

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

[0059] Control logic 400 can exploit the capabilities of a computer platform to create multiple processes and threads by processing data simultaneously. The speed and efficiency of control logic 400 is 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 may also be utilized and is within the scope of the present invention.

[0060] The process flow of the control logic 400 in this embodiment begins at 402, where the control logic 400 detects 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 performed by the vehicle detection module 122. The control logic 400 then proceeds to step 404.

[0061] In step 404, control logic 400 may generate a record. In one embodiment, step 404 may be associated with and / or considered to be performed by vehicle data capture module 128. In one embodiment, the record may include data related to the vehicle, such as the time and date of detection, the identity of the vehicle, the number of axles on the vehicle, and / or the direction the vehicle is traveling. The record may be stored on a client, a server, or a database. Control logic 400 then proceeds to step 406.

[0062] In step 406, control logic 400 may receive a location, such as a vehicle location, a track location, etc. In one embodiment, step 406 may be associated with and / or considered to be performed by geo-positioning module 126. For example, control logic 400 may receive location data from sensors on the vehicle or on the track, or in another example, control logic 400 may receive a location from a rail system. Control logic 400 then proceeds to step 408.

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

[0064] In step 410, control logic 400 may receive data from sensors, such as sensors on the track or vehicle. In one embodiment, step 410 may be associated with and / or considered to be performed by vehicle data capture module 128. Preferably, control logic 400 receives data from sensors that may report strain / stress corresponding to the weight or mass of the vehicle. In one example, the sensors may be strain gauges coupled to the track. Control logic 400 then proceeds to step 412.

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

[0066] At step 414, the control logic 400 may determine a confidence level. In one embodiment, step 414 may be associated with and / or considered to be performed by the force-determination module 130 and / or the alert-generation module 134. In another embodiment, the confidence level may be a measure of confidence in the accuracy of the sensor data received at step 410. For example, and in one embodiment, the data received at step 410 may be received from one or more sensors. If multiple sensors are providing similar measurements, the control logic 400 may determine at 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 may determine at step 414 that the confidence level should be relatively low. In another embodiment, if the measurements from the sensors are not uniform but instead are relatively anomalous, the control logic 400 may determine that the confidence level should be relatively low. In another embodiment, if the received data provides multiple data points that appear to be reasonably uniform, control logic 400 may determine that the confidence level should be relatively high. In one embodiment, control logic 400 may assign a percentage value to the determined confidence level (e.g., as in the table above in connection with alert generation module 134). Control logic 400 may then proceed to step 416.

[0067] At step 416, the control logic 400 may determine whether an environmental threshold has been met. In one embodiment, step 416 may be associated with and / or considered to be performed by the force calibration module 132. In one embodiment, the environmental threshold may be a pressure threshold that may or may not be met if the environmental data received in step 408 indicates a pressure that is less than, equal to, or greater than the pressure threshold. In another embodiment, the environmental threshold may be a temperature threshold, such as the temperature thresholds described above in connection with the force calibration module 132 and the alert generation module 134. If the environmental threshold has been met, the control logic 400 proceeds to step 418. If the environmental threshold has not been met, the control logic 400 proceeds to step 420.

[0068] In step 418, the control logic 400 may generate a calibrated maximum force value. In one embodiment, step 418 may be associated with and / or may be considered to be performed by the force calibration module 132. In another embodiment, the control logic 400 may generate the calibrated maximum force value by referencing 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 may be generated by adjusting the original maximum force value with a manipulated variable. The control logic 400 then proceeds to step 420.

[0069] At step 420, control logic 400 may determine whether the original maximum force value exceeds a force threshold. In one embodiment, step 420 may be associated with and / or may be considered to be performed by alert generation module 134. In another embodiment, control logic 400 may reference a force threshold stored in memory, which may function as a kill switch; for example, if the original maximum force does not exceed the force threshold, control logic 400 may determine that an alert will not be generated, regardless of the remaining process flow steps. If the original maximum force value does not exceed the force threshold, control logic 400 proceeds to step 422. If the original maximum force value exceeds the force threshold, control logic 400 proceeds to step 424. At step 422, control logic 400 may determine that an alert will not be generated. In one embodiment, step 422 may be associated with and / or may be considered to be performed by alert generation module 134. The control logic 400 then proceeds to step 432. In step 432, the control logic 400 may update a record, such as the record generated in step 404. In one embodiment, step 420 may also be associated with and / or considered to be performed by the alert delivery module 136.

[0070] In step 424, the control logic 400 may determine whether a calibrated maximum force value has been generated (e.g., whether step 418 has been performed). In one embodiment, step 424 may be associated with and / or may be considered to be performed 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 may 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 may be associated with and / or may be considered to be performed by the alert generation module 134. In another embodiment, the severity level may be assigned according to the table described above in connection with the alert generation module 134. In one example, the severity level of the alert may vary with both the calibrated maximum force value and the confidence level. The control logic 400 then proceeds to step 430.

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

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

[0073] 5A-5B show a flowchart diagram 500 illustrating control logic implementing features and program steps of a wheel impact load detection and calibration system in accordance with an exemplary embodiment of the present disclosure. The wheel impact load detection and calibration system control logic 500 may be implemented as an algorithm on a server (e.g., server 102), a machine learning module, or other suitable system. In addition, the wheel impact detection and calibration system control logic 500 may implement or incorporate one or more features of the WILD system 200, including the WILD capture system 202 (with corresponding modules 122, 124, 126, and 128), the WILD calibration system 204 (with corresponding modules 130 and 132), and the alert management system 206 (with corresponding modules 134 and 136). The control logic 500 may be implemented using software, hardware, an application programming interface (API), a network connection, a network transfer protocol, HTML, DHTML, JavaScript, Dojo, Ruby, Rails, or other suitable application, or any suitable combination thereof.

[0074] The control logic 500 can exploit the capabilities of a computer platform to create multiple processes and threads by processing data simultaneously. The speed and efficiency of the control logic 500 is 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 may also be utilized and is within the scope of the present invention.

[0075] The process flow of the control logic 500 of this embodiment begins at step 502, where the control logic 500 may detect a vehicle, such as a vehicle moving on a track. The control logic 500 then proceeds to step 504. In step 504, the control logic 500 may generate a record. The control logic 500 may then proceed to step 506. In step 506, the control logic 500 may receive a location of the vehicle and / or the track. The control logic 500 then proceeds to step 508. In step 508, the control logic 500 may receive environmental data 508. The control logic 500 then proceeds to step 510. In step 510, the control logic 500 may receive data from one or more sensors, preferably related to forces being exerted by the vehicle relative to the track. The control logic 500 then proceeds to step 512. In step 512, control logic 500 may determine an original maximum peak force from the received sensor data. In one embodiment, the maximum peak force may correspond to the maximum force being exerted by the vehicle against the track. Control logic 500 then proceeds to steps 514 and 516.

[0076] At step 514, control logic 500 may determine a confidence level in the received sensor data in accordance with the principles of the present disclosure. Control logic 500 then proceeds to step 518. At step 518, control logic 500 may determine whether the original maximum peak force determined at 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. At step 520, control logic 500 may determine that an alert will not be generated. At step 522, control logic 500 may determine whether an environmental threshold is met in accordance with the principles of the present disclosure. If the environmental threshold is not met, control logic 500 proceeds to step 524. If the environmental threshold is met, control logic 500 proceeds to step 526.

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

[0078] In step 524, the control logic 500 may 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 may determine whether the original maximum peak force or the calibrated maximum peak force should be used from step 524 onwards. For example, the control logic 500 may determine that if a calibrated maximum peak force is generated in step 526, the calibrated maximum peak force should be used starting from step 524. In another example, the control logic 500 may determine that if a calibrated maximum peak force is not generated in step 526, the original maximum peak should be used starting from step 524. Preferably, the third force threshold may be greater than the first force threshold. For example, the first force threshold may correspond to a force less than the third threshold (e.g., a relatively small kip value) such that if the maximum peak force exceeds the third threshold, the severity level of the resulting alert may be relatively greater compared to maximum peak forces that do not generally exceed the third force threshold but do exceed the first force threshold. If the maximum peak force being used (e.g., original or calibrated) exceeds the third force threshold, control logic 500 proceeds to step 532. If the maximum peak force being used does not exceed the third force threshold, control logic 500 proceeds to step 530.

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

[0080] At step 530, control logic 500 may 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 may be between the first and third force thresholds. For example, the second force threshold may be greater than the first force threshold and less than the third force threshold. If the maximum peak force exceeds the second force threshold, control logic 500 proceeds to step 534. If the maximum peak force does not exceed the second force threshold, control logic 500 proceeds to step 542. At step 542, control logic 500 may generate an alert having an assigned severity level. In one embodiment, the severity level may be level 3. In one embodiment, the severity level of the alert generated at step 542 may be lower than the severity levels of the alerts generated at steps 544 and 546 but higher than the severity level of the alert generated at step 540. Control logic 500 may then exit or may wait for a new vehicle detection and repeat the steps described above.

[0081] In step 534, control logic 500 may determine whether the confidence level determined in step 514 exceeds a first confidence threshold. In one embodiment, the first confidence threshold in step 534 may be the same as the first confidence threshold in step 532. If the first confidence threshold is exceeded, control logic 500 proceeds to step 536. If the first confidence level is not exceeded, control logic 500 proceeds to step 542. In step 536, control logic 500 may determine whether the confidence level determined in step 514 exceeds a second confidence threshold. The second confidence threshold may be similar to the first confidence threshold, and preferably, the second confidence threshold may signal a relatively higher degree of confidence compared to the first confidence threshold. In one embodiment, the second confidence threshold may 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, a second confidence threshold of 85% may signal that the control logic 500 is only 85% confident that the data received from the sensor in step 510 (and 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 force threshold, the control logic 500 proceeds to step 542.

[0082] In step 538, control logic 500 may determine whether the confidence level determined in step 514 exceeds a third confidence threshold. The third confidence threshold may be similar to the first and / or second confidence threshold. Preferably, the third confidence threshold may signal a relatively high degree of confidence compared to the first and second confidence thresholds. 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 would not exceed the third confidence threshold. If the confidence level exceeds the third confidence threshold, control logic 500 proceeds to step 546. If the confidence level does not exceed the third confidence threshold, control logic 500 proceeds to step 544.

[0083] At step 516, control logic 500 may plot the data from the sensors received at step 510. For example, control logic 500 may generate a plot such as that described above in connection with force determination module 130. Preferably, the plot includes a force axis and a time axis so 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 at step 510 may be data from one or more strain gauges, and control logic 500 may convert the units of measurements provided by the strain gauges (e.g., με, in / in, mm / mm, etc.) to force and / or mass measurements such as Newtons, pounds, kilograms, etc., and may then plot the force measurements according to the time at which the data was received. Control logic 500 then proceeds to step 548. At step 548, control logic 500 may compare points from the plots. Preferably, control logic 500 may search for similarities within the points and trends in the data. In one example, control logic 500 can determine the force value (within a margin of error) that has the greatest instances on the plot. In one embodiment, control logic 500 can recognize that a particular force measurement (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 can also determine the maximum force measurement received by the sensor in step 510. Control logic 500 then proceeds to step 550.

[0084] At step 550, control logic 500 can recognize a static peak force trend within the data. In one embodiment, static peak force trend can refer to the number of measurements that can correlate to one another, such as by being close in value to one another; in another embodiment, static peak force trend can refer to the range of force values ​​that have the largest instances. In another embodiment, static peak force trend can refer to force measurements that are within a predetermined deviation from one another. As an example, control logic 500 can receive ten different force measurements that occur at ten different times at step 510. In this example, seven of these measurements can be between 63 and 65 kip, and the other three measurements can be 90 kip, 120 kip, and 100 kip, respectively. In this example, control logic 500 can recognize that seven of the measurements are correlated to one another by virtue of the fact that they all fall within a range that spans only three kip, thereby enabling control logic 500 to recognize a static peak force trend within the data. In this example, control logic 500 may further recognize that the maximum force value scale was 120 kips. Control logic 500 then proceeds to step 552. In step 552, control logic 500 may determine a vehicle weight value by using the static peak force trend. In one embodiment, control logic 500 may average all of the force values ​​within the static peak for the trend to determine the vehicle weight. Control logic 500 then proceeds to step 554. In step 554, control logic 500 may calculate a dynamic force value using the original maximum peak force determined in step 512 and the weight value determined in 552. In one embodiment, control logic 500 may thereby determine the percentage of the maximum peak force that was due to imperfections as opposed to the vehicle weight.

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

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

[0087] In step 516, control logic 500 may determine a confidence level. Control logic 500 may incorporate several factors in determining the confidence level, such as the number of data points received in step 510 (which in one embodiment may mean the number of wheel rotations that occurred at a given time and distance), the range of measurements, environmental data, etc. In this embodiment, control logic 500 may determine that the confidence level should be 75%. Control logic 500 then 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 may be 90 kips, which means that in this example, control logic 500 then proceeds to step 522 instead of step 520 (e.g., because 140 kips exceeds 90 kips). In step 522, control logic 500 may determine whether an environmental threshold is met. In this example, the environmental threshold may be a temperature threshold of 32°F, and the temperature threshold may be met if the temperature received in step 508 is less than 32°F. In this example, control logic 500 may determine in step 522 that the temperature received in step 508 (0°F) meets the environmental threshold, and then control logic 500 proceeds to step 526. In step 526, the control logic 500 may generate a calibrated maximum peak force, in this example the control logic 500 may generate the calibrated maximum peak force according to the following formula:

number

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

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

[0091] Advantageously, the force thresholds and confidence thresholds described herein can be any suitable values ​​depending on the desired alert severity level assignment. For example, lowering the force threshold, in one embodiment, can cause alerts with relatively high severity levels to be generated for vehicles applying relatively poor forces to the track. In another example, decreasing the confidence threshold can cause alerts with relatively high severity levels to be generated from received data with significantly lower fidelity. In contrast, raising the force threshold and / or confidence threshold can cause control logic 500 to assign relatively lower severity levels to relatively large impact forces determined from relatively less accurate data.

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

[0093] Control logic 600 can exploit the capabilities of a computer platform to create multiple processes and threads by processing data simultaneously. The speed and efficiency of control logic 600 is 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 may also be utilized and is within the scope of the present invention.

[0094] The process flow of the control logic 600 of this embodiment begins at step 602, where the control logic 600 may detect a vehicle, such as a vehicle moving on a track. In one embodiment, the vehicle may be detected by measuring rest or threshold-exceeding values ​​from one or more strain gauges operably coupled to the train track. The control logic 600 then proceeds to step 604. In step 604, the control logic 600 may receive environmental data in accordance with the principles of the present disclosure. The control logic 600 then proceeds to step 606. In step 606, the control logic 600 may receive sensor data in accordance with the principles of the present disclosure. The control logic 600 then proceeds to step 608. In step 608, the control logic 600 may determine a maximum force value from the sensor data received in step 606 in accordance with the principles of the present disclosure. The control logic 600 then proceeds to step 610. In step 610, the control logic 600 may determine the original trust level in accordance with the principles of this disclosure. The control logic then proceeds to step 612.

[0095] At step 612, control logic 600 may determine whether the maximum force value determined at 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. At step 614, control logic 600 may determine that an alert will not be generated. At step 616, control logic 600 may determine whether the environmental data received at step 604 meets the 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] At step 618, control logic 600 may reduce the original confidence level. In one embodiment, the confidence level may be reduced commensurate with 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 reports a temperature of 0°F, the confidence level may be reduced more than if the reported received environmental data reports, for example, a temperature of 30°F. In another embodiment, control logic 600 may utilize a formula similar to that described above in connection with force-determination module 130 to adjust the confidence level as opposed to the maximum force value. In another embodiment, control logic 600 may reduce the confidence level such that the confidence level drops below the confidence threshold. For example, if the first confidence threshold is 50%, the second confidence threshold is 85%, and the original confidence level is determined to be 75% at step 610, control logic 600 may reduce the confidence level to 49% so that the first confidence threshold is not exceeded. Control logic 600 then proceeds to step 622.

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

[0098] 7 shows a flowchart 700 illustrating control logic for implementing the features and program steps of a wheel impact load detection (WILD) method in accordance with an exemplary embodiment of the present disclosure. The WILD method control logic 700 may be implemented as an algorithm on a server (e.g., server 102), a machine learning module, or other suitable system. Additionally, the WILD method control logic 700 may implement or incorporate one or more features of the WILD system 200, including the WILD capture system 202 (with corresponding modules 122, 124, 126, and 128), the WILD calibration system 204 (with corresponding modules 130 and 132), and the alert management system 206 (with corresponding modules 134 and 136). The control logic 700 may be implemented using software, hardware, and / or application programming interfaces (APIs), network connections, network transfer protocols, HTML, DHTML, JavaScript, Dojo, Ruby, Rails, or other suitable applications, or any suitable combination thereof.

[0099] Control logic 700 can exploit the capabilities of a computer platform to create multiple processes and threads by processing data simultaneously. The speed and efficiency of control logic 700 is 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 may also be utilized and is within the scope of the present invention.

[0100] The control logic 700 process flow of this embodiment begins at step 702, where the control logic 700 may detect a vehicle, such as a vehicle moving on a track. The control logic 700 then proceeds to step 704. In step 704, the control logic 700 may receive environmental data in accordance with the principles of the present disclosure. The control logic 700 then proceeds to step 706. In step 706, the control logic 700 may receive sensor data in accordance with the principles of the present disclosure. The control logic 700 then proceeds to step 708. In step 708, the control logic 700 may determine a maximum force value from the sensor data received in step 706 in accordance with the principles of the present disclosure. The control logic 700 then proceeds to step 710.

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

[0102] At step 714, control may determine whether the environmental data received at step 704 satisfies the environmental threshold. If the environmental data satisfies the environmental threshold, control logic 700 proceeds to step 716. If the environmental data does not satisfy the environmental threshold, control logic 700 proceeds to step 718. At step 716, control logic 700 may assign a severity level. In one embodiment, control logic 700 may assign a severity level without reference to the maximum force value. In another embodiment, control logic 700 may assign a severity level that signals that the environmental threshold has been met. For example, if the environmental threshold is met at step 714, control logic 700 may assign the same severity level to all potential alerts, regardless of the maximum force value. At step 718, control logic 700 may utilize the maximum force value determined at step 708 in assigning severity levels in accordance with the principles of the present disclosure. Control logic 700 then proceeds to step 720. In step 720, 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 tailor alert generation and severity level assignment to fit specific needs. In one embodiment, different types of environmental thresholds can be used within the same flow; for example, a 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, an environmental threshold can require a certain temperature to be exceeded for a certain period of time before the threshold can be satisfied; in another example, an environmental threshold can require the temperature to be consistently below a certain temperature for a certain 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, if an environmental threshold is satisfied for a predetermined duration, the assigned severity level can be lower than if the environmental threshold is satisfied for a relatively long duration. In another embodiment, the environmental data and environmental thresholds may refer to the environment of the rail. For example, the environmental data may include the temperature of the rail, and the environmental thresholds may include temperature thresholds related to the temperature of the rail.

[0104] The present disclosure achieves at least the following advantages: 1. Optimization of severity level assignment in wheel impact load detection alerts to consider environmental conditions; 2. Prioritization of wheel impact load detection alerts to take environmental conditions into account; 3. Calibration of the measured maximum force applied to the rail to adjust for temperature extremes; and 4. Providing a method for ranking alerts that takes environmental conditions into account.

[0105] Those skilled in the art will readily appreciate that these advantages (as well as those set forth in the Summary) and objectives of the present system would not be possible without the particular combination of computer hardware and other structural components and mechanisms assembled in the inventive system and described herein. It should further be appreciated that various programming tools known to those skilled in the art can be used to implement the features and operational control described above. Moreover, the particular choice of programming tool(s) may be determined by the particular objectives and constraints imposed on the implementation strategy chosen to realize the concepts described in this specification and the appended claims.

[0106] Nothing in this patent document should be construed to imply that any particular element, step, or function may be an essential or critical element required for inclusion in the scope of a claim. Nor may a claim be intended to invoke 35 U.S.C. 112(f) with respect to any of the appended claims or claim elements unless the precise terms "means for" or "step for" are expressly used in a particular claim, any of which is followed by a participial phrase identifying the function. Use of terms such as "mechanism," "module," "device," "unit," "component," "element," "member," "apparatus," "machine," "system," "processor," "processing unit," or "controller" in the claims (without limitation) may be understood and construed to mean structures known to those skilled in the relevant art, as further modified or improved by the features of the claim itself, and may not be intended to invoke 35 U.S.C. 112(f).

[0107] The present disclosure may be embodied in other specific forms without departing from its spirit or essential characteristics. For example, each of the novel structures described herein can be modified to suit particular local variations or requirements while retaining its basic configuration or mutual structural relationships, or while performing the same or similar functions described herein. The present embodiments, therefore, must be regarded in all respects as illustrative and not restrictive. The scope of the invention is, therefore, established by the appended claims, rather than the foregoing description. Accordingly, all changes that come within the meaning and range of equivalency of the claims are to be construed as embraced therein. Moreover, individual elements of the claims are not well-understood, conventional, or conventional. Instead, the claims are directed to the non-conventional inventive concepts described herein.

Claims

1. 1. A system for generating railroad alerts related to wheel impact load detection sensor data, comprising: a memory having a first database having a plurality of sensor data, thresholds, and specifications relating to at least a portion of a vehicle and a track; a networked computer processor operatively coupled to the memory and capable of executing machine-readable instructions to perform program steps; and The program steps include: detecting the vehicle on the track; receiving environmental data; receiving sensor data corresponding to one or more forces being exerted by the vehicle relative to the track; determining an original maximum peak force from the sensor data; comparing the original maximum peak force to a first force threshold; If the original maximum peak force exceeds the first force threshold, determining whether the environmental data satisfies an environmental threshold; generating, via the processor, a calibrated maximum peak force if the environmental data satisfies the environmental threshold; utilizing the original maximum peak force or the calibrated maximum peak force when assigning a severity level; generating an alert including said severity level; Including, If the original maximum peak force is less than the first force threshold, no alert is generated by the system.

2. The system of claim 1 , wherein the calibrated maximum peak force is generated by normalizing the original maximum peak force using a manipulated variable.

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

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

5. The system of claim 4 , wherein the programming step further comprises utilizing the trust level in assigning the severity level.

6. The system of 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. 2. The system of claim 1, wherein if 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 indicates that the calibrated maximum peak force was utilized in assigning the severity level.

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

9. if the environmental data does not meet the environmental threshold, the original maximum peak force is utilized in assigning the severity level; and The system of claim 1 , wherein if the environmental data meets the environmental threshold, the calibrated maximum peak force is utilized in assigning the severity level.

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

11. 1. A method of compensating for environmental conditions in wheel impact load detection, comprising: detecting a vehicle on a track; generating, via one or more processors, at least one record including a date, a time, a direction of the vehicle, and a number of axles of the vehicle; 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; if the environmental data satisfies an environmental threshold, generating a calibrated maximum force value by calibrating the original maximum force value with a manipulated variable via the one or more processors; utilizing the original maximum force value to assign a first severity level if the original maximum force value exceeds the first force threshold and if the calibrated maximum force value was not generated; utilizing the calibrated maximum force value to assign a second severity level if the original maximum force value exceeds the first force threshold and if the calibrated maximum force value is generated; generating an alert including the first or second severity level; updating the at least one record; and If 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 of claim 11 , wherein the environmental data includes a temperature and the environmental threshold is a temperature threshold.

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

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

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

16. 1. A method for compensating for variable rail tensions produced 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, via one or more processors, a maximum force value from the sensor data; If the maximum force value exceeds a first force threshold, determining whether the environmental data satisfies an environmental threshold; assigning a first severity level if the maximum force value exceeds the first force threshold and if the environmental data satisfies the environmental threshold; assigning a second severity level if the maximum force value exceeds the first force threshold and if the environmental data does not satisfy the environmental threshold; generating an alert including the first or second severity level; and If the maximum force value is less than the first force threshold, no alert is generated.

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

18. The method of claim 17 , wherein the confidence level is reduced if the environmental threshold is met.

19. 17. The method of claim 16, wherein the environmental threshold is met when the temperature is at or below 0°C.

20. 17. The method of claim 16, further comprising updating a record to indicate that the alert includes the first or second severity level.

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