General Electric Company

Progress Rail Services Corporation

Transportation IP Holdings, LLC

New York Air Brake Corporation

John William Brand

Jared Klineman Cooper

Todd William Goodermuth

Robert James Foy

Joseph Forrest Noffsinger

David Allen Eldredge

Mark Bradshaw Kraeling

Bradley Howard

Wolfgang Daum

Brian Joseph McManus

Kevin Kapp

Eugene Andrew Smith

Nick David Nagrodsky

David Michael Peltz

Scott Daniel Nelson

Joseph Daniel Wakeman

David Carroll Teeter

Sammy Akif

Ajith Kuttannair Kumar

James D Brooks

Brian Nedward Meyer

Aaron Richard Mitti

Frank Richard Wawrzyniak

Robert Francis Bryant

David Lowell McKay

Kaitlyn Ann Hrdlicka

Eric Vorndran

Keith Gilbertson

Saravanan Thiyagarajan

Jeffrey A Twichel

William Martin

Stephen Don Smith

Juli-Ann Luther

Swapna Rameshbabu Gaikwad

James Andrew Napolitano

Gregory Paul Hrebek

Christopher Louis Rekucki

Evan Paul Sevel

Glenn Robert Shaffer

Steven Andrew Kellner

William Cherrick Schoonmaker

Samuel William Golden

Patricia Sue Lacy

Brian William Schroeck

Daniel Malachi Ballesty

Daniel Joseph Rush

Sidarta Fornari Beltramin

Scott William Dulmage

John Welsh McElroy

Daniel Walter Plawecki

Marc David Miller

Kristopher Ryan Smith

Mikhail Meltser

Gabriel G Abreo

Christopher Mcnally

Wade Goforth

Robert Bremmer

Timothy John Medema

Joseph Maverick White

Armin H Pfoh

David Mckay

Greg Hrebek

Michael Barrett

Scott C Sherin

Frederick Gary Silcox, Jr

Marcos Blanco Fernandes

A system includes one or more processors and memory storing processor-executable instructions that cause the one or more processors to perform operations. The operations include generating a driving strategy for a traveling route of a train comprising at least one battery-electric locomotive (BEL) based on saved data in the system for optimizing one or more aspects for the train over the traveling route; operating the train according to the driving strategy; receiving update data associated with a current location of the train; revising the driving strategy based on the saved data and the update data for optimizing energy consumption efficiency for a segment of the traveling route by identifying active powered components of the train that are non-essential for traversing the segment and turning off power to one or more of the identified active powered components for a duration of traversing the segment.

Managing vehicle energy use based on future energy demand

A train control system uses sensory inputs related to operational parameters of a train for automatically scoring or classifying particular train driving strategies implemented by a machine learning model for a particular train operating on a predefined route or route segment. The train control system includes one or more predefined rules related to one or more of a first set of the operational parameters, wherein each of the rules defines a Boolean, true or false classification based on whether a particular train driving strategy results in one or more of the first set of operational parameters complying with the rule. One or more comparative key performance indicators are related to one or more of a second set of operational parameters, and are used to rank the particular train driving strategy for the predefined route or route segment relative to a different train driving strategy for the same or comparable route or route segment.

System and method for scoring train runs

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.

System and method for controlling operations of a train using energy management machine learning models

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

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.

Train control with centralized and edge processing handovers

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.

Method and system for vehicle communications

Method and system for data processing in a vehicle group

An energy management system and method for a vehicle system operate the vehicle system according to a current trip plan as the vehicle system travels along a route during a trip. The current trip plan designates operational settings of the vehicle system. The system and method also revise the current trip plan into a revised trip plan responsive to current, actual operation of the vehicle system differing from the current trip plan by at least a designated threshold amount. The revised trip plan designates operational settings of the vehicle system and includes an initial designated operational setting that matches the current, actual operation of the vehicle system.

Energy management system and method for vehicle systems

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.

System for providing temporary speed restrictions to locomotives

A detection system for a rail vehicle includes a control module, which has a sensor input for receiving a signal from a sensor. The control module is configured to receive the signal and to output information responsive to the signal. In operation, the control module and a sensor are deployed on board the rail vehicle. The sensor is configured to generate the signal responsive to detecting a designated condition on board the rail vehicle. When the condition occurs, the sensor outputs the signal, which is received by the control module. Responsive to the signal, the control module outputs the information, such as communicating information of the signal content to another rail vehicle, or to an off board location. The designated condition may be unauthorized use of a handheld wireless device in a rail vehicle, or intruder entry into an unmanned rail vehicle.

Detection system and method for rail vehicle

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.

System and method for controlling velocity of a vehicle

Growing community of inventors

Melbourne, FL, United States of America

John William Brand