ONLINE HEALTH MONITORING VIA MULTIDIMENSIONAL TEMPORAL DATA MINING

• US 20100030418A1
• Filed: 07/31/2008
• Published: 02/04/2010
• Est. Priority Date: 07/31/2008
• Status: Abandoned Application

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations Incorporated (General Motors Company)
• Inventors: Holland, Steven W.
• Application Number:
  US12/183,829
• Publication Number:
  US 20100030418A1
• Time in Patent Office: Days
• US Class Current: 701/29
• CPC Class Codes: G06F 11/008 Reliability or availability analysis

Abstract

A system and method for predicting the occurrence of an event in a vehicle. The method includes monitoring a data stream including multi-dimensional tokens having information about one or more vehicle operating parameters. Further, the method includes the identification of a pattern of values of the one or more operating parameters in the data stream by mining the data stream using a temporal data miner, and predicting the occurrence of the event based on the correlation between the detected pattern and a pre-recorded pattern.

Claims

1. A method for predicting an occurrence of an event in a vehicle, the method comprising:

monitoring a string of multi-dimensional tokens, wherein each multi-dimensional token of the string includes values of one or more operating parameters of the vehicle at an instant in time;

identifying a pattern of values of the one or more operating parameters in the string, wherein the pattern is identified by mining the string of multi-dimensional tokens; and

forecasting the occurrence of the event based on a correlation between the identified pattern and a pre-recorded pattern.

2. The method according to claim 1 wherein the multi-dimensional tokens are diagnostic trouble codes (DTCs) of the vehicle.

3. The method according to claim 1 wherein mining the string of multi-dimensional tokens is done using multi-dimensional temporal data mining technique.

4. The method according to claim 1 wherein a time interval between the instants of time corresponding to adjacent multi-dimensional tokens is user selectable.

5. The method according to claim 1 wherein the pre-recorded pattern corresponds to a degraded mode of operation of the vehicle and is obtained by filtering out strings of multi-dimensional tokens corresponding to a normal mode of operation from a degraded mode of operation of the vehicle.

6. The method according to claim 1 further comprising warning a driver of the vehicle when the forecasted event is a failure.

7. A method for detecting performance degradations in operating vehicles, said method comprising:

monitoring a serial data stream on the vehicle where the serial data stream includes logical values and each logical value identifies more than one operating parameter of the vehicle;

detecting patterns of the logical values in the serial data stream;

comparing the detected patterns of logical values to known patterns of logical values; and

determining the occurrence of a degradation event based on the comparison between the detected patterns and the known patterns.

8. The method according to claim 7 wherein the logical values include diagnostics trouble codes of the vehicle.

9. The method according to claim 7 further comprising determining the known patterns of logical values by identifying strings of logical values for normal modes of operations of the vehicle.

10. The method according to claim 9 wherein determining the known patterns includes identifying known patterns of the logical values for degraded modes of operation of the vehicle.

11. The method according to claim 10 wherein determining the known patterns includes removing the normal mode patterns corresponding to degraded mode patterns so that the known patterns that are compared to the detected patterns are for degraded modes of operation.

12. The method according to claim 11 wherein determining the known patterns includes removing patterns that occur in multiple types of failures or degradations.

13. The method according to claim 9 wherein determining the known patterns includes identifying the know patterns by a person having expertise in identifying modes of operation of the vehicle.

14. The method according to claim 7 wherein the detected patterns of logical values and the known patterns of logical values are represented as graphs, and wherein comparing the detected patterns to the known patterns include using a graph matcher.

15. A system for predicting an occurrence of an event in a vehicle, the system comprising:

a data miner for identifying a pattern by mining a string of multi-dimensional tokens, wherein each multi-dimensional token of the string includes values of one or more operating parameters of the vehicle at an instant of time corresponding to the multi-dimensional token; and

a graph matcher to correlate the identified pattern and a pre-recorded pattern, wherein the occurrence of the event is forecasted based on the correlation between the identified pattern and the pre-recorded pattern.

16. The system according to claim 15 wherein the multi-dimensional tokens are diagnostic trouble codes (DTCs) of the vehicle.

17. The system according to claim 15 wherein the data mining engine is a temporal data miner.

18. The system according to claim 15 wherein a time interval between the instants corresponding to the multi-dimensional token is user selectable.

19. The system according to claim 15 further comprising a warning signal generator for warning the driver of the vehicle when the event is a failure.