Method and system for analyzing continuous parameter data for diagnostics and repairs

• US 6,636,771 B1
• Filed: 10/13/2000
• Issued: 10/21/2003
• Est. Priority Date: 04/02/1999
• Status: Expired due to Fees

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Varma, Anil, Roddy, Nicholas Edward, Gibson, David Richard
• Primary Examiner(s): Gordon, Paul P.
• Application Number:
  US09/688,105
• Time in Patent Office: 1,103 Days
• Field of Search: 700/21, 700/79, 700/110, 700/177, 701/19, 701/29, 701/35, 701/36, 702/57, 702/81, 714/2, 714/25, 714/26, 714/37
• US Class Current: 700/79
• CPC Class Codes: B61C 17/04 Arrangement or disposition of driving cabins, footplates or engine rooms; Ventilation thereof driving cabins and accessories B61D
• CPC Class Codes: B61C 17/12 Control gear; Arrangements for controlling locomotives from remote points in the train or when operating in multiple units control from points outside the train B61L3/00; fluid-actuated telemotors, servomotors F15B; control devices in general G05
• CPC Class Codes: G05B 23/0235 based on a comparison with predetermined threshold or range, e.g. "classical methods", carried out during normal operation; threshold adaptation or choice; when or how to compare with the threshold
• CPC Class Codes: G05B 23/0286 Modifications to the monitored process, e.g. stopping operation or adapting control
• CPC Class Codes: G06F 11/2257 using expert systems
• CPC Class Codes: G06F 11/2268 Logging of test results

Abstract

The present invention discloses system and method for analyzing continuous parameter data from a malfunctioning locomotive or other large land-based, self-powered transport equipment. The method allows for receiving new continuous parameter data comprising a plurality of anomaly definitions from the malfunctioning equipment. The method further allows for selecting a plurality of distinct anomaly definitions from the new continuous parameter data. Respective generating steps allow for generating at least one distinct anomaly definition cluster from the plurality of distinct anomaly definitions and for generating a plurality of weighted repair and distinct anomaly definition cluster combinations. An identifying step allows for identifying at least one repair for the at least one distinct anomaly definition cluster using the plurality of weighted repair and distinct anomaly definition cluster combinations.

Claims

1. A method for analyzing continuous parameter data from a malfunctioning locomotive or other large land-based, self-powered transport equipment, comprising:

receiving new continuous parameter data comprising a plurality of anomaly definitions from the malfunctioning equipment;

selecting a plurality of distinct anomaly definitions from the new continuous parameter data;

generating at least one distinct anomaly cluster from the plurality of distinct anomaly definitions;

generating a plurality of weighted repair and distinct anomaly cluster combinations; and

identifying at least one repair for the at least one distinct anomaly cluster using the plurality of weighted repair and distinct anomaly cluster combinations.

2. The method of claim 1 wherein the at least one distinct anomaly cluster comprises at least one of a single distinct anomaly and a plurality of distinct anomaly definitions.

3. The method of claim 1 wherein each of the plurality of weighted repair and distinct anomaly cluster combinations are generated from a plurality of cases, each case comprising a repair and at least one distinct anomaly, and each of the plurality of weighted repair and distinct anomaly cluster combinations being assigned a weight determined by dividing the number of times the combination occurs in cases comprising related repairs by the total number of times the combination occurs in said plurality of cases.

4. The method of claim 3 wherein identifying the at least one repair comprises selecting at least one repair using the plurality of weighted repair and anomaly cluster combinations and adding assigned weights for distinct anomaly clusters for related repairs.

5. The method of claim 1 said generating a plurality of weighted repair and distinct anomaly cluster combinations utilizes a plurality of repairs and continuous parameter data comprising a plurality of anomaly definitions.

6. The method of claim 1 wherein the receiving continuous parameter data comprises receiving a new continuous parameter data and comparing the new continuous parameter data to a prior continuous parameter data.

7. A system for analyzing continuous parameter data from a malfunctioning locomotive or other large land-based, self-powered transport equipment, comprising:

a directed weight data storage unit adapted to store a plurality of weighted repair and distinct anomaly cluster combinations;

a processor adapted to receive new continuous parameter data comprising a plurality of anomaly definitions from the malfunctioning equipment;

a processor for selecting a plurality of distinct anomaly definitions from the new continuous parameter data;

a processor for generating at least one distinct anomaly definition cluster from the selected plurality of distinct anomaly definitions;

a processor for generating a plurality of weighted repair and distinct anomaly definition cluster combinations; and

a processor for identifying at least one repair for the at least one distinct anomaly definition cluster using the plurality of predetermined weighted repair and distinct anomaly definition cluster combinations.

8. The system of claim 7 wherein a single processor unit constitutes said processors.

9. A system of claim 7 further comprising:

a processor for generating a plurality of cases from the repair data and the continuous parameter data, each case comprising a repair and a plurality of distinct anomaly definitions;

a processor for generating, for each of the plurality of cases, at least one repair and distinct anomaly definition cluster combination, and

a processor for assigning, to each of the repair and distinct anomaly definition cluster combinations, a weight, whereby weighted repair and distinct anomaly definition cluster combinations facilitate identification of at least one repair for the malfunctioning equipment.

10. The system of claim 9 wherein the processor for generating the plurality of cases comprises a processor for selecting a repair from the repair data and selecting a plurality of distinct anomaly definitions from the continuous parameter data over a period of time prior to the repair.

11. The system of claim 9 wherein the processor for assigning weights comprises a processor for determining, for each repair and distinct anomaly definition cluster combination, a number of times the combination occurs in cases comprising related repairs, and a number of times the combination occurs in the plurality of cases.

12. The system of claim 11 wherein the processor for assigning a weight, for each repair and distinct anomaly definition cluster combination, comprises a processor for dividing the number of times the combination occurs in cases comprising related repairs by the number of times the combination occurs in the plurality of cases.

13. The system of claim 11 further comprising;

a processor for generating a new case from repair data and continuous parameter data, the case comprising a repair and a plurality of distinct anomaly definitions;

a processor for generating, for the new case, a plurality of anomaly definition clusters for the plurality of distinct anomaly definitions; and

a processor for redetermining a weight for each of the plurality of repair and anomaly definition cluster combinations to include the new case.

14. The system of claim 11 further comprising:

a repair log data storage unit adapted to store a plurality of repairs; and

a continuous parameter data storage unit adapted to store a plurality of anomaly definitions.