A railroad wheel impact load detection test panel includes a secondary instrumentation rail proximate a field side of a primary or running rail of a section of railroad track, and elevated a prescribed distance so that the wheels of a rail car traverse the instrumentation rail within the test panel. The instrumentation rail includes an optical strain gauge to sense the wheel impact load. The sensed impact data is correlated with wheel damage signatures to identify wheels to be restored or replaced before failure occurs.

A railroad wheel impact load detection test panel includes a secondary instrumentation rail proximate a field side of a primary or running rail of a section of railroad track, and elevated a prescribed distance so that the wheels of a rail car traverse the instrumentation rail within the test panel. The instrumentation rail includes an optical strain gauge to sense the wheel impact load. The sensed impact data is correlated with wheel damage signatures to identify wheels to be restored or replaced before failure occurs.

Methods and systems for in-service inspection of freight car axles are provided. A camber angle of a wheel attached to a target axle is measured at one or more points in a revolution of the wheel. A determination of whether a defect is present in the target axle is made based on the measured camber angle at the one or more points in the revolution of the wheel. A determination that a defect is present in the target axle is made when a camber angle of the wheel at one or more points in the revolution of the wheel exceeds a threshold, and/or a determination that a defect is not present is made when no camber angle of the wheel at any point in the revolution of the wheel exceeds the predetermined threshold. An alert is generated in response to determining that a defect is present.

A system and method for monitoring the operating condition of a wheelset on a railcar comprising a sealed unit mounted on or near a wheelset of the railcar for collecting data from the wheelset and performing AI analyses on the collected data to determine the operational condition and predict failure modes for the wheelset. Results are communicated off-railcar wirelessly via one or more of several different methods.

A system and method for monitoring the operating condition of a wheelset on a railcar comprising a sealed unit mounted on or near a wheelset of the railcar for collecting data from the wheelset and performing AI analyses on the collected data to determine the operational condition and predict failure modes for the wheelset. Results are communicated off-railcar wirelessly via one or more of several different methods.

A device for monitoring wear of a flange (1) of a roller (2) includes a peripheral rim (3) and intended to cooperate with a homologous flange (1bis) of a roller (2bis) positioned face-to-face to interact with the head of a rolling rail (4). The device includes at least one first recess localized at the level of a peripheral line of constant radius on the flange (1) configured to be positioned at the level at which the point (7) of separation of the flange (1) relative to its homologue (1bis) is the smallest.

An evaluation unit is fed with driving-related diagnostic data of a reference railway vehicle. The diagnostic data contain driving-related properties, on which a parameter of the wheel of the reference railway vehicle depends. The evaluation unit is also fed with wheel measurement data with measured values of the parameter of the wheel of the reference railway vehicle. The evaluation unit determines a function between the driving-related diagnostic data and the wheel measurement data via a supervised learning algorithm. The evaluation unit is fed with driving-related diagnostic data containing driving-related properties of an observed railway vehicle, on which a parameter of the wheel of the observed railway vehicle depends. The parameter of the wheel of the observed railway vehicle is determined by the evaluation unit using the determined function and the diagnostic data of the observed railway vehicle.

An evaluation unit is fed with driving-related diagnostic data of a reference railway vehicle. The diagnostic data contain driving-related properties, on which a parameter of the wheel of the reference railway vehicle depends. The evaluation unit is also fed with wheel measurement data with measured values of the parameter of the wheel of the reference railway vehicle. The evaluation unit determines a function between the driving-related diagnostic data and the wheel measurement data via a supervised learning algorithm. The evaluation unit is fed with driving-related diagnostic data containing driving-related properties of an observed railway vehicle, on which a parameter of the wheel of the observed railway vehicle depends. The parameter of the wheel of the observed railway vehicle is determined by the evaluation unit using the determined function and the diagnostic data of the observed railway vehicle.

A system is provided that includes a detection circuit having a first and second sensor. The first sensor is configured to measure a rotational speed of a first wheel. The second sensor is coupled to a vehicle chassis and configured to measure a position over time of the vehicle chassis. The system further includes a controller circuit configured to determine a shock frequency based on the position of the vehicle chassis. The controller circuit is further configured to determine a condition (e.g., an anomalous condition) of the first wheel based on the shock frequency and the rotational speed, and may be further configured for vehicle control based on the determined condition.

A system is provided that includes a detection circuit having a first and second sensor. The first sensor is configured to measure a rotational speed of a first wheel. The second sensor is coupled to a vehicle chassis and configured to measure a position over time of the vehicle chassis. The system further includes a controller circuit configured to determine a shock frequency based on the position of the vehicle chassis. The controller circuit is further configured to determine a condition (e.g., an anomalous condition) of the first wheel based on the shock frequency and the rotational speed, and may be further configured for vehicle control based on the determined condition.