FAULT PREDICTION FRAMEWORK USING TEMPORAL DATA MINING

• US 20110172874A1
• Filed: 01/13/2010
• Published: 07/14/2011
• Est. Priority Date: 01/13/2010
• Status: Active Grant

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations Incorporated (Motors Liquidation Co. GUC Trust)
• Inventors: Patnaik, Debprakash, Montgomery, George Paul JR., Unnikrishnan, Kootaala P., Bandyopadhyay, Pulak, Holland, Steven W.
• Granted Patent: US 8,676,432 B2
• Time in Patent Office: Days
• US Class Current: 701/33
• CPC Class Codes: G06F 11/008 Reliability or availability analysis
• CPC Class Codes: G06F 16/2474 Sequence data queries, e.g. querying versioned data

Abstract

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.

Claims

1. A vehicle fault diagnosis and prognosis system, comprising:

a computing platform configured to receive a fault 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 associated with a particular fault.

2. The system of claim 1, wherein the data sequences include one or more the following:

sequenced diagnostic trouble codes, time stamped diagnostic trouble codes and vehicle parameters.

3. The system of claim 1, wherein the classifier is trained to associate a plurality of data sequence clusters to a particular failure mode.

4. The system of claim 1, wherein the data sequences in the classifier are derived from a population of vehicles using temporal data mining.

5. The system of claim 1, wherein the computing platform is internal to the vehicle.

6. The system of claim 1, wherein the computing platform is external to the vehicle.

7. A method for fault diagnosis and prognosis, comprising:

extracting data sequences from a first population of vehicles;

applying a temporal data mining application to the data sequences extracted from the first population of vehicles to detect patterns in the data sequences;

retrieving repair data related to a second population of vehicles;

comparing the data sequences to the repair data to identify data sequences that are related to a particular failure mode.

8. The method of claim 7, wherein applying the temporal data mining application includes identifying frequent data sequences.

9. The method of claim 7, wherein the first population of vehicles is similar to the second population of vehicles.

10. The method of claim 7, further comprising applying a clustering algorithm to assign a confidence measure to each of the identified data sequences related to a particular failure mode.

11. The method of claim 10, wherein applying a clustering algorithm includes clustering the data sequences according to a particular failure mode.

12. The method of claim 10, wherein applying a clustering algorithm includes assigning a probability to each data sequence and failure mode combination.

13. The method of claim 11, further including training a classifier to learn the clusters associated with each failure mode.

14. A computer-readable medium tangibly embodying computer-executable instructions for:

extracting data sequences from a first population of vehicles;

applying a temporal data mining application to the data sequences extracted from the first population of vehicles to detect patterns in the data sequences;

retrieving repair data related to a second population of vehicles;

comparing the data sequences to the repair data to identify data sequences that are related to a failure mode.

15. The computer-readable medium of claim 14, wherein applying the temporal data mining application includes identifying frequent data sequences.

16. The computer-readable medium of claim 14, further comprising applying a clustering algorithm to assign a confidence measure to each of the identified data sequences related to a failure mode.

17. The computer-readable medium of claim 16, wherein applying a clustering algorithm includes clustering the data sequences according to a particular failure mode.

18. The computer-readable medium of claim 16, wherein applying a clustering algorithm includes assigning a probability to each data sequences and failure mode combination.

19. The computer-readable medium of claim 17, further including training a classifier to learn the clusters associated with each failure mode.

20. The computer-readable medium of claim 19, further including downloading the classifier to an on-board flexible computing platform.

21. The computer-readable medium of claim 19, further including downloading the classifier to an off-board computing platform.