Gm Global Technolgoy Operations, Inc.

Caterpillar Inc.

Rahul G Chougule

Pulak Bandyopadhyay

Yilu Zhang

Mutasim Abdurrahman Salman

Satnam Singh

Steven W Holland

Vineet Khare

Soumen De

Sugato Chakrabarty

Yuen-Kwok Chin

Mark N Howell

Xidong Tang

Xiaodong Zhang

Dnyanesh Rajpathak

Sabyasachi Bhattacharya

Debprakash Patnaik

Andrea M Simon

Abhishek Ramaswamy

Ankurkumar D Gandhi

Jeanne T Prichodko

Dhakshinamoorthy A Mani

Prithvi Mohan

Andrew G Jirovec

An analytical model training method includes generating a modified set of machine repair records by associating a machine with at least one of i) one or more defects associated with the machine, ii) a repair of the one or more defects, and iii) one or more repair characteristics. An analytical model training system generates a training model based from the modified set of repair records and a set of survey records indicative of reported experience values. The system isolates at least one time-based machine operation parameter associated with machine operation from a set of machine operation records. A modified set of machine operation records is generated that includes a plurality of values associated with the at least one time-based machine operation parameter. An analytical model is configured to output an estimate of customer experience based at least partly on a second set of machine repair records.

Analytical model training method for customer experience estimation

A document may be received at a processing module. One or more tags may be applied to the document, each tag applied to a term, each tag representing a part of speech. One or more terms may be extracted from the document based on the tag. A weighting assignment parameter may be determined for each of the one or more extracted terms. Based on the weighting assignment parameter associated with each of the extracted terms, it may be determined whether the domain ontology includes the one or more extracted terms. If the domain ontology does not include the one or more extracted terms, the domain ontology may be augmented such that the domain ontology comprises the one or more extracted terms.

Method and system to augment vehicle domain ontologies for vehicle diagnosis

A system and method for fault diagnosis includes receiving information defining a relationship between failure modes and diagnostic trouble codes and extracting diagnostic trouble code data, including set times, frequency data and diagnostic trouble code sequence information for a plurality of diagnostic trouble codes relating to a plurality of failure modes. The system and method further include constructing a Markov chain using the diagnostic trouble code data for each of the plurality of failure modes, training the Markov chain to learn a set of state parameters using the diagnostic trouble code data, and computing a likelihood of a diagnostic trouble code sequence for each of the plurality of failure modes using the trained Markov chains.

Fault diagnosis and prognosis using diagnostic trouble code markov chains

A method is provided for enhancing service diagnostics utilizing service repair data of previously serviced vehicles. Service repair data of previously serviced vehicles is obtained from a memory storage device. The service data is compiled into a service diagnostic code dataset and a service labor code dataset. The service diagnostic code dataset and service labor code dataset are categorized into an electronic data table. Respective combinations are formed in the electronic data table. An aggregate count is determined for each respective combination in the electronic data table. Either of a respective diagnostic code or a respective service labor code is identified having a correlation with more than one of either service diagnostic codes or service labor codes. At least one of a service repair procedure used to repair the vehicle or a respective service diagnostic code used to identify the fault is modified in response to analyzing the respective combinations.

Process for service diagnostic and service procedures enhancement

Systems and methods for capturing and analyzing significant parameter data from vehicle systems whenever a diagnostic trouble code (DTC) is triggered. A multi-dimensional matrix is constructed, with vehicles, DTCs, and parameter data comprising three dimensions of the matrix. The data matrix is populated with DTC and parameter data from many different vehicles, either when vehicles are taken to a dealer for service, or via wireless data download. Time can be added as a fourth dimension of the matrix, providing an indication of whether a particular system or component is temporally degrading. When sufficient data is accumulated, the data matrix is pre-processed, features are extracted from the data, and the features are classified, using a variety of mathematical techniques. Trained classifiers are then used to diagnose the root cause of any particular fault signal, and also to provide a prognosis of system health and remaining useful life.

Event-driven fault diagnosis framework for automotive systems

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

Growing community of inventors

Bengaluru, India

Rahul G Chougule