A system for simulating a railroad track. The system comprises one or more modular track simulation units that are smaller than conventional track simulators and are easy to use with and be connected to a device being tested (e.g., grade crossing predictor). Each track simulation unit may have one of a plurality of impedances associated with a corresponding railroad track length. The units are combinable such that the system can simulate multiple, different track lengths. Each unit has a plurality of test points that can be connected to the device under test and/or used to alter conditions of the simulated track.

A system may include a data acquisition hub connected to databases and sensors associated with locomotives, systems, or components of a train and configured to acquire real-time and historical configuration, structural, and operational data in association with inputs derived from real time and historical contextual data relating to a plurality of trains. The system may include a virtual system modeling engine configured to receive results of a non-destructive evaluation of a train component, simulate in-train forces, determine a predicted time of failure for the train component based on an evaluation of stresses that have already been applied to the component and expected future stresses, and implement repair, replacement, or operational protocols for the train component before or at a repair facility that will be reached by the train ahead of a predetermined minimum threshold time period before the predicted time of failure.

A method and an apparatus for a train control system are disclosed, and are based on virtualization of train control logic and the use of cloud computing resources. A train control system is configured into two main parts. The first part includes physical elements of the train control system, and the second part includes a virtual train control system that provides the computing resources for the required train control application platforms. The disclosed architecture can be used with various train control technologies, including communications based train control, cab-signaling and fixed block, wayside signal technology. Further, the disclosure describes methodologies to convert cab-signaling and manual operations into distance to go operation.

A system and method is disclosed for predicting and comparing wear scenarios in a rail system. The method can include generating and running a contact model of the interaction between a rail and a train car to produce a simulated loading on the rail for a predetermined time period; generating and running a wear model based on the material properties and/or friction modifier properties of the rail using the simulated loading to produce a simulated wear profile of the rail for the predetermined time period; obtaining a grinding profile to be performed on the rail during the predetermined time period; and generating an updated rail profile by modifying the rail profile by the simulated wear profile and the grinding profile. The method can predict and compare crack growth over time, and provide a financial model and comparison of costs and benefits for different maintenance protocols for the rail system.

The present disclosure provides a method and system for controlling a heavy-haul train based on reinforcement learning. The method includes: obtaining operation state information of a heavy-haul train at a current time point; obtaining a heavy-haul train action of a next time point according to the operation state information of the heavy-haul train at the current time point and a heavy-haul train virtual controller, and sending the heavy-haul train action of the next time point to a heavy-haul train control unit to control operation of the heavy-haul train. The heavy-haul train virtual controller is obtained by training a reinforcement learning network according to operation state data of the heavy-haul train and an expert strategy network; the reinforcement learning network includes one actor network and two critic networks; the reinforcement learning network is constructed according to a soft actor-critic (SAC) reinforcement learning algorithm.

A train control system uses artificial intelligence for maintaining synchronization between centralized and distributed train control models. A machine learning engine receives training data from a data acquisition hub, a first set of output control commands from a centralized virtual system modeling engine, and a second set of output control commands from a distributed virtual system modeling engine. The machine learning engine compares the first set of output control commands and the second set of output control commands, and trains a learning system using the training data to enable the machine learning engine to safely mitigate any difference between the first and second sets of output control commands using a learning function including at least one learning parameter.

For diagnosing a railroad switch with a point machine, a first and a second time series of a sensor signal of the point machine are received. Moreover, changes in the first and the second time series are detected indicating changes of operational conditions of the point machine. Furthermore, an event point of a respective change in the first and in the second time series is allocated to a respective component of the railroad switch or of the point machine based on a simulation modelling the respective component. Then for a respective component: event points allocated to that respective component are identified, the sensor signal at a first identified event point in the first time series is compared with the sensor signal at a second identified event point in the second time series, and depending on the comparison a component-specific fault information and an identification of the respective component are output.

The present disclosure provides a speed tracking control method and system for a heavy-haul train. According to the present disclosure, a multi-particle unit-displacement model of the train is established and a robust-adaptive active disturbance rejection control method is adopted, so that an error between an actual speed of the train and a target speed is minimized, an anti-interference capacity of the heavy-haul train is improved, and high-precision tracking control over the target speed of the train is realized.

The Station Time Grade Time Stop Time and Relay Time Testing Device (SGSRTD) is a portable device that allows one to test whether various railroad-based components and elements, circuits, controls, relays, and systems are operating optimally or pursuant to manufacturers' specifications. SGSRTD comprises a multitude of components, including a power switch, electronic programmable timers, connectors, shunt switch, DC relays, AC relay, and leads. When coupled with a limit switch, an SGSRTD system is formed.

The Station Time Grade Time Stop Time and Relay Time Testing Device (SGSRTD) is a portable device that allows one to test whether various railroad-based components and elements, circuits, controls, relays, and systems are operating optimally or pursuant to manufacturers' specifications. SGSRTD comprises a multitude of components, including a power switch, electronic programmable timers, connectors, shunt switch, DC relays, AC relay, and leads. When coupled with a limit switch, an SGSRTD system is formed.