Systems and methods for train consist management are provided. In an aspect, a method includes, but is not limited to, designing a train consist; analyzing the train consist with a physics-based dynamic force model to determine whether the train consist would experience at least one of in-train force imbalances or lateral force imbalances indicative of at least one of a component malfunction or a derailment scenario for a proposed train route; and validating the train consist when it is determined that the train consist would not experience in-train force imbalances indicative of at least one of a component malfunction or a derailment scenario for the proposed train route.

Systems and methods for train consist management are provided. In an aspect, a method includes, but is not limited to, designing a train consist; analyzing the train consist with a physics-based dynamic force model to determine whether the train consist would experience at least one of in-train force imbalances or lateral force imbalances indicative of at least one of a component malfunction or a derailment scenario for a proposed train route; and validating the train consist when it is determined that the train consist would not experience in-train force imbalances indicative of at least one of a component malfunction or a derailment scenario for the proposed train route.

A dynamometer can be used with rail equipment having two pairs of drive wheels. The dynamometer can comprise first and second wheel engagement assemblies, each comprising a frame, a first pair of dynamometer wheels that are spaced relative to a first horizontal dimension, and a second pair of dynamometer wheels that are spaced relative to the first horizontal dimension. The first and second pairs of dynamometer wheels can be spaced relative to a second horizontal dimension. The first pair of dynamometer wheels and second pair of dynamometer wheels can cooperate to define opposing wheel engagement receptacles. A hydraulic motor can couple to the first plurality of support wheels and can be configured to receive a power and an associated torque from a respective pair of the drive wheels of the rail equipment. The first and second wheel engagement assemblies can be movable relative to each other along the second dimension.

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.

A train simulator test set is disclosed that can be operably coupled to a railroad track to measure the resting impedance of that track circuit and simulate a train by varying the railroad track inductance over a set period of time. The test set can select the speed, direction, and number of trains to simulate. By applying a variable inductance on the railroad tracks, the test set can simulate a train moving at variable speeds toward and away from the island. The test set can apply inductances to the railroad tracks to simulate two or more trains moving in each direction of the tracks at the same time, along with multiple looks and routes. The train simulator test set can include simulation software to vary the parameters of the train simulation and couple a variable inductance on the railroad tracks.

According to various aspects, exemplary embodiments are disclosed of systems, methods and devices related to remote control locomotive training. In an exemplary embodiment, a system includes a trainee operator control unit configured for wireless communication with a locomotive control unit for transmitting one or more commands to the locomotive control unit. A trainer is configured for wireless communication with the trainee operator control unit. The trainer is configured for monitoring the trainee operator control unit including the one or more commands transmitted from the trainee operator control unit to the locomotive control unit. The trainer is configured to transmit a brake application override command to the trainee operator control unit. And, the trainee operator control unit is configured to relay the brake application override command and/or a stop command to the locomotive control unit.

The invention discloses a method and a system for state feedback predictive control of a high-speed train based on a forecast error. The method comprises: obtaining a speed prediction model of a high-speed train

[00001]${\hat{y}}_{k+p}=C{A}^p{x}_k+\underset{i=1}{\overset{p}{.Math.}}C{A}^{i-1}B{u}_{k+p-i};$

predicting speeds of the train at times k and k+p according to the speed prediction model; obtaining an actual speed output value of the train; determining a speed prediction error at time k according to the prediction speed of the train and the actual speed output value of the train; correcting a prediction speed of the train at time k+p, according to the speed prediction error, to obtain a corrected prediction speed of the train; calculating a control force u.sub.k of the train according to u.sub.k=λ.sup.−1(p)[y.sub.k+p.sup.4 −y.sub.k−Kx.sub.k+ŷ.sub.k]; and applying a control force to the train based on the control force u.sub.k. The disclosure may have features of strong adaptability and easy calculation, and achieve high-precision tracking of train speed.

A vehicle control signal generator includes emulation circuitry for emulating at least one operation of at least one vehicle control circuit. The emulation circuitry is programmed or configured to determine or receive user input and based at least partially on the user input, generate at least one control signal for controlling at least one of the following onboard vehicle subsystems: a propulsion system, a train line control system, an air brake system, a dynamic braking system, a head of train (HOT) system, an end of train telemetry (EOT) system, a 4-Aspect cab signaling system, a positive train control (PTC) system, an event recorder system, or any combination thereof.

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.

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.