METHODOLOGY TO IMPROVE FAILURE PREDICTION ACCURACY BY FUSING TEXTUAL DATA WITH RELIABILITY MODEL

• US 20120232905A1
• Filed: 12/16/2011
• Published: 09/13/2012
• Est. Priority Date: 03/10/2011
• Status: Abandoned Application

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations LLC (General Motors Company)
• Inventors: Rajpathak, Dnyanesh, De, Soumen
• Application Number:
  US13/328,726
• Publication Number:
  US 20120232905A1
• Time in Patent Office: Days
• US Class Current: 704/257
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

A method and system for developing reliability models from unstructured text documents, such as text verbatim descriptions from service technicians. An ontology, or data model, and heuristic rules are used to identify and extract failure modes and parts from the text verbatim comments associated with specific labor codes from service events. Like-meaning but differently-worded terms are then merged using text similarity scoring techniques. The resultant failure modes are used to create enhanced reliability models, where component reliability is predicted in terms of individual failure modes instead of aggregated for the component. The enhanced reliability models provide improved reliability prediction for the component, and also provides insight into aspects of the component design which can be improved in the future.

Claims

1. A method for creating reliability models for a component or system, said method comprising:

providing a text document containing technician comments about service events on the component or system;

extracting failure modes from the text document using a digital computing device encoded with text mining techniques; and

creating reliability models for the component or system using the failure modes which were extracted.

2. The method of claim 1 wherein providing a text document includes exporting the text document from a database of service events.

3. The method of claim 1 wherein extracting failure modes includes detecting sentence boundaries, removing non-descriptive words, identifying failure modes, and merging failure modes which are worded differently but have the same meaning.

4. The method of claim 3 wherein detecting sentence boundaries includes identifying full-stop punctuation marks, using the full-stop punctuation marks to define sentence boundaries, and defining correlations between the failure modes and parts based on the sentence boundaries.

5. The method of claim 3 wherein merging failure modes includes using text similarity techniques to assign a similarity score to a pair of failure modes, comparing the similarity score to a threshold value, and equating the pair of failure modes if the similarity score exceeds the threshold value.

6. The method of claim 1 wherein extracting failure modes includes using an ontology and heuristic rules.

7. The method of claim 6 wherein the ontology is a data model describing elements of the component or system, including parts, symptoms, and failure modes, and relationships between the parts, the symptoms, and the failure modes.

8. The method of claim 1 wherein creating reliability models for the component or system includes creating a separate reliability model for each of the failure modes which were extracted.

9. The method of claim 1 wherein the component or system is part of a vehicle.

10. The method of claim 1 wherein the reliability models are Weibull reliability models.

11. A method for creating reliability models for a vehicle sub-system or component, said method comprising:

providing a database containing information about vehicle service events;

exporting a text document from the database, said text document containing technician comments about the service events;

extracting failure modes from the text document using a digital computing device encoded with text mining techniques;

creating reliability models for the vehicle sub-system or component using the failure modes which were extracted, including a reliability model for each of the failure modes; and

using the reliability models to predict a number of failures of the vehicle sub-system or component at a given exposure.

12. The method of claim 11 wherein extracting failure modes includes detecting sentence boundaries, removing non-descriptive words, identifying failure modes, and merging failure modes which are worded differently but have the same meaning.

13. The method of claim 11 wherein extracting failure modes includes using an ontology and heuristic rules.

14. The method of claim 13 wherein the ontology is a data model describing elements of the vehicle sub-system or component, including parts, symptoms, and failure modes, and relationships between the parts, the symptoms, and the failure modes.

15. A system for creating reliability models for a vehicle sub-system or component, said system comprising:

means for providing a text document containing technician comments about service events on the vehicle sub-system or component;

a digital computing device encoded with text mining techniques for extracting failure modes from the text document; and

means for creating reliability models for the vehicle sub-system or component using the failure modes which were extracted.

16. The system of claim 15 wherein the text mining techniques for extracting failure modes include detecting sentence boundaries, removing non-descriptive words, identifying failure modes, and merging failure modes which are worded differently but have the same meaning.

17. The system of claim 16 wherein detecting sentence boundaries includes identifying full-stop punctuation marks, using the full-stop punctuation marks to define sentence boundaries, and defining correlations between the failure modes and parts based on the sentence boundaries.

18. The system of claim 16 wherein merging failure modes includes using text similarity techniques to assign a similarity score to a pair of failure modes, comparing the similarity score to a threshold value, and equating the pair of failure modes if the similarity score exceeds the threshold value.

19. The system of claim 15 wherein the text mining techniques for extracting failure modes include an ontology and heuristic rules, where the ontology is a data model describing elements of the vehicle sub-system or component, including parts, symptoms, and failure modes, and relationships between the parts, the symptoms, and the failure modes.

20. The system of claim 15 wherein the means for creating reliability models creates a separate reliability model for each of the failure modes which were extracted.