Abstract: A system for actuating a safety control for a device including: (1) at least one sensor adapted to measure a parameter of the device and provide an output; (2) a Kalman filter adapted to receive the output of the at least one sensor and provide a state estimate and covariance value of the device; (3) a Lyapunov function adapted to receive the state estimate and covariance value from the Kalman filter and provide a time derivative; and (4) a comparator adapted to compare the time derivative to a reference value and provide a command decision to the device.

Abstract: A machine learning system for maintaining distributed computer control systems for a train may include a data acquisition hub communicatively connected to a plurality of sensors configured to acquire real-time configuration data from one or more of the computer control systems. The machine learning system may also include an analytics server communicatively connected to the data acquisition hub. The analytics server may include a virtual system modeling engine configured to model an actual train control system comprising the distributed computer control systems, a virtual system model database configured to store one or more virtual system models of the distributed computer control systems, wherein each of the one or more virtual system models includes preset configuration settings for the distributed computer control systems, and a machine learning engine configured to monitor the real-time configuration data and the preset configuration settings.

Abstract: A train control system may include a data acquisition hub connected to a database and sensors associated with one or more locomotives, systems, or components of a train and configured to acquire data in association with inputs derived from contextual data relating to a plurality of trains being operated by experienced train engineers under a variety of different conditions and in different geographical areas for use as training data. A machine learning engine of the train control system may receive the training data from the data acquisition hub, encode a modified learning function as a statistical model of desirable train handling behavior, evaluate train handling behavior by comparing to the statistical model, and update a certification of one or more of a train engineer, a semi-autonomous control system, or a fully autonomous control system.

Abstract: 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.

Abstract: 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.

Abstract: A train control system uses machine learning for implementing handovers between centralized and distributed train control models. A machine learning engine receives training data from a data acquisition hub, receives a centralized train control model from a centralized virtual system modeling engine, and receives an edge-based train control model from an edge-based virtual system modeling engine. The machine learning engine trains a learning system using the training data to enable the machine learning engine to predict when a locomotive of the train will enter a geo-fence where communication between the edge-based computer processing system and the centralized computer processing system will be inhibited.

Abstract: A train control system controls the ramp rate at which a train accelerates after braking. A machine learning engine receives training data from a data acquisition hub, including a plurality of input conditions of the train and a plurality of outputs associated with the input conditions. A virtual system modeling engine simulates in-train forces and train operational characteristics using physics-based equations, kinematic or dynamic modeling of behavior of the train or components of the train during operation of the train when the train is accelerating after braking, and inputs derived from stored historical contextual data related to the train. The machine learning engine trains a learning system using the training data to generate an output based on an input using a learning function including at least one learning parameter. The learning parameter is modified as needed to improve the accuracy of the learning function in generating the output.

Abstract: 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.

Abstract: A train control system may include a data acquisition hub connected to a database and sensors associated with one or more locomotives, systems, or components of a train and configured to acquire data in association with inputs derived from contextual data relating to a plurality of trains being operated by experienced train engineers under a variety of different conditions and in different geographical areas for use as training data. A machine learning engine of the train control system may receive the training data from the data acquisition hub, encode a modified learning function as a statistical model of desirable train handling behavior, evaluate train handling behavior by comparing to the statistical model, and update a certification of one or more of a train engineer, a semi-autonomous control system, or a fully autonomous control system.

Abstract: A train control system uses machine learning for implementing handovers between centralized and distributed train control models. A machine learning engine receives training data from a data acquisition hub, receives a centralized train control model from a centralized virtual system modeling engine, and receives an edge-based train control model from an edge-based virtual system modeling engine. The machine learning engine trains a learning system using the training data to enable the machine learning engine to predict when a locomotive of the train will enter a geo-fence where communication between the edge-based computer processing system and the centralized computer processing system will be inhibited.

Abstract: 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.

Abstract: A train control system using machine learning for development of train control strategies includes a machine learning engine. The machine learning engine receives training data from a data acquisition hub, including a plurality of first input conditions and a plurality of first response maneuvers associated with the first input conditions. The machine learning engine trains a learning system using the training data to generate a second response maneuver based on a second input condition using a learning function including at least one learning parameter.

Abstract: A machine learning system for maintaining distributed computer control systems for a train may include a data acquisition hub communicatively connected to a plurality of sensors configured to acquire real-time configuration data from one or more of the computer control systems. The machine learning system may also include an analytics server communicatively connected to the data acquisition hub. The analytics server may include a virtual system modeling engine configured to model an actual train control system comprising the distributed computer control systems, a virtual system model database configured to store one or more virtual system models of the distributed computer control systems, wherein each of the one or more virtual system models includes preset configuration settings for the distributed computer control systems, and a machine learning engine configured to monitor the real-time configuration data and the preset configuration settings.

Abstract: Methods and systems are provided for processing data generated in a vehicle group. One example embodiment comprises selectively off-boarding a data set generated at a first vehicle in the vehicle group for storage purposes, the first data set off-boarded to one or more of a second vehicle in the vehicle group and a remote storage device.

Abstract: Methods and systems are provided for processing data generated in a vehicle group. One example embodiment comprises selectively off-boarding a data set generated at a first vehicle in the vehicle group for storage purposes, the first data set off-boarded to one or more of a second vehicle in the vehicle group and a remote storage device.

Abstract: Methods and systems are provided for processing data generated in a vehicle group. One example embodiment comprises selectively off-boarding a data set generated at a first vehicle in the vehicle group for storage purposes, the first data set off-boarded to one or more of a second vehicle in the vehicle group and a remote storage device.

Abstract: A system for automatically updating route information to a locomotive that includes a route computer and voice radio encoder that merges data representing temporary changes in a route with a voice radio call placed by a dispatcher to a locomotive engineer. A voice radio decoder coupled to the locomotive cab voice radio extracts the data representing the temporary changes from the voice radio signal and provides the data directed to the energy management system so that there is no need for the engineer to manually enter the temporary data.

Abstract: A system for automatically updating route information to a locomotive that includes a route computer and voice radio encoder that merges data representing temporary changes in a route with a voice radio call placed by a dispatcher to a locomotive engineer. A voice radio decoder coupled to the locomotive cab voice radio extracts the data representing the temporary changes from the voice radio signal and provides the data directed to the energy management system so that there is no need for the engineer to manually enter the temporary data.

Abstract: A control system includes an onboard controller, a location determination system, and a speed sensor. The controller identifies a designated area along a route that includes a downhill section, an airflow restricted area, a banked section, a section to be cleaned, an adverse environmental area, an adverse vehicular condition area, and/or a section where travel is restricted. The designated area is associated with an operating rule that requires the vehicle to travel at least as fast as a lower speed limit. The location determination system monitors actual locations of the vehicle as the vehicle travels along the route. The speed sensor obtains speed data representative of an actual velocity of the vehicle. The controller restricts control of the vehicle such that the actual velocity of the vehicle is at least as fast as the lower speed limit of the designated area when the vehicle is in the designated area.

Abstract: Methods and systems are provided for controlling a locomotive (or other rail vehicle) owned by a first entity when borrowed and operated by a second entity. The locomotive includes an operating control system having a first mode and a second, different mode of operation, the first mode including settings configured for the first entity, the second mode including settings configured for a second, different entity. The method includes enabling the first mode and disabling the second mode of the operating control system when the locomotive is operated by the first entity, and enabling the second mode and disabling the first mode of the operating control system when the locomotive is operated by the second entity.