Gm Global Technolgoy Operations, Inc.

Debprakash Patnaik

Pulak Bandyopadhyay

Steven W Holland

Yilu Zhang

Mutasim Abdurrahman Salman

Yuen-Kwok Chin

Xiaodong Zhang

Soumen De

Sabyasachi Bhattacharya

George Paul Montgomery, Jr

Ansaf Ibrahem Alrabady

Balarama Vempaty Murty

Mark N Howell

Xidong Tang

Rami I Debouk

Rahul G Chougule

Sugato Chakrabarty

Kootaala P Unnikrishnan

Andrea M Simon

A vehicle fault diagnosis and prognosis system includes a computing platform configured to receive a classifier from a remote server, the computing platform tangibly embodying computer-executable instructions for evaluating data sequences received from a vehicle control network and applying the classifier to the data sequences, wherein the classifier is configured to determine if the data sequences define a pattern that is associated with a particular fault.

Fault prediction framework using temporal data mining

An integrated diagnosis and prognosis system that collects vehicle information over the life of a vehicle and its development. The system provides the collected vehicle information to supplier management, product development management, service/dealership management, customer relations departments and production facilities, which use the information to take certain action for existing vehicles, fleets of vehicles or future vehicles to improve vehicle reliability and quality.

Integrated diagnosis and prognosis system as part of the corporate value chain

A system and method for enhancing vehicle diagnostic and prognostic algorithms and improving vehicle maintenance practices. The method includes collecting data from vehicle components, sub-systems and systems, and storing the collected data in a database. The collected and stored data can be from multiple sources for similar vehicles or similar components and can include various types of trouble codes and labor codes as well as other information, such as operational data and physics of failure data, which are fused together. The method generates classes for different vehicle components, sub-systems and systems, and builds feature extractors for each class using data mining techniques of the data stored in the database. The method also generates classifiers that classify the features for each class. The feature extractors and feature classifiers are used to determine when a fault condition has occurred for a vehicle component, sub-system or system.

Aggregated information fusion for enhanced diagnostics, prognostics and maintenance practices of vehicles

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Bengaluru, India

Debprakash Patnaik