A system and method for inspecting a railway track bed using a plurality of sensors that are synchronized for rapid interrogation of a railway track bed while the sensors are in motion at a high rate of speed.

A monitoring system includes optical sensors disposed on one or more fiber optic waveguides. Each optical sensor is spaced apart from other optical sensors and is disposed at a location along a route defined by a transportation structure that supports a moveable conveyance. The plurality of optical sensors are mechanically coupled to one or both of the transportation structure and the moveable conveyance. Each optical sensor provides an optical output signal responsive to vibrational emissions of one or both of the transportation structure and the conveyance. The monitoring system includes a detector unit configured to convert optical output signals from the optical sensors to electrical signals. A data acquisition controller synchronizes recordation of the electrical signals with movement of the conveyance.

A rail position measurement device that measures a three-dimensional position of a rail using a measurement vehicle includes: a position posture measurement device to measure a position and a posture of the measurement vehicle; and a laser scanner that is a sensor installed on the measurement vehicle so as to be capable of irradiating at least a web and a bottom of a side surface of the rail with laser light and used for measuring the three-dimensional position of the rail.

An onsite steel rail laser processing engineering vehicle, including a laser processing power engineering vehicle and a laser processing cart, the laser processing power engineering vehicle is connected to the laser processing cart; the onsite steel rail laser processing engineering vehicle further comprises a transport mechanism disposed on the laser processing power engineering vehicle; through movement and rotation, the transport mechanism transports the laser processing cart into the laser processing power engineering vehicle or transports the laser processing cart out from the laser processing power engineering vehicle and places it on rails.

A system and method for inspecting a rail is provided. The system includes an ultrasonic transducer positioned to emit an ultrasonic beam onto the rail and receive a refraction beam, the ultrasonic transducer being movable between a first position and a second position. A sensor is operable to measure an angle of a carriage, the carriage being positioned on the rail. A controller is operably coupled to the sensor, the controller having a processor that is responsive to executable computer instructions when executed on the processor to cause the ultrasonic transducer to move to receive refraction beam in response to the measured angle indicating a rail radius of less than a predetermined first threshold.

An aspect includes a vehicle that includes rail inspection sensors configured for capturing transducer data describing the rail, and a processor configured for receiving and processing the transducer data in near-real time to determine whether the captured transducer data identifies a suspected rail flaw. The processing includes inputting the captured transducer data to a machine learning system that has been trained to identify patterns in transducer data that indicate rail flaws. The processing also includes receiving an output from the machine learning system, the output indicating whether the captured transducer data identifies a suspected rail flaw. An alert is transmitted to an operator of the vehicle based at least in part on the output indicating that the captured transducer data identifies a suspected rail flaw. The alert includes a location of the suspected rail flaw and instructs the operator to stop the vehicle and to perform a repair action.

In example embodiments, techniques are provided for using machine learning to predict railroad track geometry exceedances to enable proactive maintenance. A machine learning model of a rail operational analytics application may be trained to directly output a probability of future railroad track geometry exceedances for each portion of track of a railroad. Training may be performed using all available railroad track data, and the task of selecting which data is relevant to predicting probability of railroad track geometry exceedances may be devolved to the machine learning model. Further, assumptions about the specific railroad and data characteristics may be avoided, providing the machine learning model flexibility, and allowing for dynamic changes in the problem formulation.

The present disclosure discloses a rail corrugation recognition method and apparatus based on a support vector machine, a device, and a medium. The method includes: obtaining wheel-rail noise signals in different time periods, and obtaining wheel-rail noise time domain information; dividing the wheel-rail noise time domain information into segmented wheel-rail noise time domain information corresponding to each of the different time periods; preprocessing each piece of segmented wheel-rail noise time domain information, and extracting a time domain statistical characteristic quantity and frequency domain eigenmode energy of each piece of segmented wheel-rail noise time domain information, to obtain a multi-dimensional wheel-rail noise characteristic vector; constructing a rail corrugation state recognition model based on a support vector machine, and training the rail corrugation state recognition model; and recognizing to-be-recognized wheel-rail noise data by using the rail corrugation state recognition model based on a support vector machine, to obtain a rail corrugation state.

A train forewarning braking system is disposed between a train and a track for the train to travel on, is electrically connected to a traffic control center, and has an image monitoring module. The image monitoring module is disposed around the track, is electrically connected to a control system of the train and the traffic control center, and has multiple image capture units and multiple display units. The image capture units are disposed at spaced intervals near the track and are electrically connected to the control system of the train to capture images of the environment around the track. The display units are disposed on the train and the traffic control center, and are electrically connected to the control system to display the images captured by each one of the image capture units.

A system for railroad directive management is presented. The system can receive a myriad of data related to a directive, track segments, and/or vehicle events on the track and/or track segments. Vehicle- and/or event-specific data can be compared with one or more thresholds, including force thresholds, temporal thresholds, environmental thresholds, and/or event thresholds to determine whether and what kind of directive modification should be instantiated. Specialized algorithms can be implemented to trace vehicle paths along the track to determine whether directive-related segments are traversed, and specialized clustering algorithms can be utilized to cluster data unique to a particular segment on a per-segment basis. The system can be integrated with existing track infrastructure and can further generate alerts to notify coupled systems and/or personnel of directives and/or modification thereof.