Progress Rail Services Corporation

Bradley Howard

John William Brand

Keith Wait

Marc David Miller

Wade Winchip

Gabriel G Abreo

Devon Robert Hall

Emily Joy Winchip

Laura Lee Winchip

A thread-wrapping device for a percussion mallet is disclosed. The thread-wrapping device includes a frame, a spindle, and a wrapping arm. The spindle is designed to retain the percussion mallet and is rotatably supported by the frame. The wrapping arm is supported by the frame and designed to rotate when the spindle rotates to wrap thread around a head of the percussion mallet. Further, a hand crank is provided to rotate the spindle and wrapping arm.

Device for wrapping thread around a head of a percussion mallet

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. The machine learning engine may warn when there is a difference between the real-time configuration data and the preset configuration settings, the difference being indicative of at least two of the distributed computer control systems being out of synchronization by more than a threshold deviation.

Maintenance of distributed train control systems using machine learning

Systems and methods for adjusting operation of a train are disclosed. A method may include: receiving one or more infrared images of a brake line of the train; detecting one or more leaks of the brake line based on the one or more infrared images; and adjusting a brake command based on the detected one or more leaks.

Systems and methods for adjusting train operation

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.

Machine learning based train handling evaluation

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.

System and method for predicting failures of train components

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.

Artificial intelligence watchdog for distributed system synchronization

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.

Growing community of inventors

Ft. Worth, TX, United States of America

Bradley Howard