The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Oct. 17, 2023

Filed:

Sep. 05, 2019
Applicant:

Progress Rail Services Corporation, Albertville, AL (US);

Inventors:

Bradley Howard, Northlake, TX (US);

John Brand, Flower Mound, TX (US);

Assignee:

Progress Rail Services Corporation, Albertville, AL (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
B61L 27/53 (2022.01); G05B 13/02 (2006.01); B61L 27/04 (2006.01); B61L 27/40 (2022.01); B61L 27/60 (2022.01); B61L 27/70 (2022.01);
U.S. Cl.
CPC ...
B61L 27/53 (2022.01); B61L 27/04 (2013.01); B61L 27/40 (2022.01); B61L 27/60 (2022.01); B61L 27/70 (2022.01); G05B 13/0265 (2013.01); B61L 2201/00 (2013.01);
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. 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.


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