Method and system for analyzing fault and snapshot operational parameter data for diagnostics of machine malfunctions

• US 6,336,065 B1
• Filed: 11/12/1999
• Issued: 01/01/2002
• Est. Priority Date: 10/28/1999
• Status: Expired due to Term

• Current Assignee: Westinghouse Air Brake Technologies Corporation
• Original Assignee: General Electric Company
• Inventors: Varma, Anil, Roddy, Nicholas Edward, Gibson, David Richard
• Primary Examiner(s): Cuchlinski, Jr., William A.
• Assistant Examiner(s): HERNANDEZ, OLGA
• Application Number:
  US09/438,271
• Time in Patent Office: 781 Days
• Field of Search: 701/29, 701/30, 701/34, 701/36, 701/35, 701/1, 701/107, 340/438, 340/441, 340/459, 340/500, 395/500, 371/30, 371/47.1
• US Class Current: 701/29
• 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 23/0281 Quantitative, e.g. mathematical distance; Clustering; Neural networks; Statistical analysis
• CPC Class Codes: G06F 11/2257 using expert systems
• CPC Class Codes: G06F 11/2268 Logging of test results
• CPC Class Codes: Y02T 30/00 Transportation of goods or passengers via railways, e.g. energy recovery or reducing air resistance

Abstract

A method for analyzing fault log data and snapshot operational parameter data from a machine undergoing diagnostics is provided. A receiving step allows for receiving fault log data comprising a plurality of faults from the machine. Respective executing steps allow for executing a set of noise-reduction filters upon the received fault log data to generate noise-reduced fault log data, and for executing a set of candidate snapshot anomalies upon the noise-reduced data to generate data predictive of malfunctions of the machine.

Claims

1. A method for analyzing fault log data from railroad locomotives and other large land-based, self-powered transport equipment undergoing diagnostics, the method comprising:

receiving fault log data comprising a plurality of faults from the equipment;

executing a set of noise-reduction filters upon the received fault log data to generate noise-reduced fault log data; and

executing a set of candidate snapshot anomalies upon the noise-reduced data to generate data indicative of malfunctions of the equipment.

2. The method of claim 1 further comprising receiving operational parameter data comprising a plurality of snapshot observations of operational parameters from the equipment, executing a set of noise reduction filters upon the operational parameter data so as to generate noise-reduced operational parameter data and executing the set of candidate snapshot anomalies upon the noise-reduced operational parameter data.

3. The method of claim 2 further comprising a step of considering any candidate snapshot anomalies respectively triggered by the noise-reduced fault log data in light of any candidate snapshot anomalies respectively triggered by the noise-reduced parameter data so as to enhance the accuracy of the data indicative of malfunctions of the equipment.

4. The method of claim 1 further comprising selecting at least one repair for each indicated malfunction using a plurality of respective weighted repairs, and respective combinations of distinct clusters of faults.

5. The method of claim 4 further comprising selecting at least one repair for each indicated malfunction using a plurality of respective weighted repairs, and respective combinations of distinct clusters of faults and/or operational parameters.

6. A method for analyzing fault log data from a machine undergoing diagnostics, the method comprising:

receiving fault log data comprising a plurality of faults from the machine;

executing a set of noise-reduction filters upon the received fault log data to generate noise-reduced fault log data; and

executing a set of candidate snapshot anomalies upon the noise-reduced data to generate data predictive of malfunctions of the machine.

7. The method of claim 6 further comprising selecting at least one repair for each predicted malfunction using a plurality of respective weighted repairs, and respective combinations of distinct clusters of faults.

8. The method of claim 6 further comprising receiving operational parameter data comprising a plurality of snapshot observations of operational parameters from the machine.

9. The method of claim 8 wherein the respective snapshot observations of the operational parameters from the machine and the logging of respective faults from the machine are temporally aligned relative to one another.

10. The method of claim 8 wherein the step of executing noise reduction further comprises executing the set of noise reduction filters upon the operational parameter data so as to generate noise-reduced operational parameter data.

11. The method of claim 10 further comprising executing the set of candidate snapshot anomalies upon the noise-reduced operational parameter data.

12. The method of claim 11 further comprising a step of combining any candidate snapshot anomalies respectively triggered by the noise-reduced fault log data with any candidate snapshot anomalies respectively triggered by the noise-reduced parameter data so as to enhance the accuracy of the data predictive of malfunctions of the machine.

13. The method of claim 12 further comprising selecting at least one repair for each predicted malfunction using a plurality of respective weighted repairs, and respective combinations of distinct clusters of faults and/or operational parameters.

14. A method for analyzing fault log data and operational parameter data from a machine undergoing diagnostics, the method comprising:

receiving fault log data comprising a plurality of faults from the machine;

receiving operational parameter data comprising a plurality of snapshot observations of respective operational parameters from the machine;

executing a set of noise-reduction filters upon the received fault log data and upon the received operational parameter data to generate noise-reduced fault log data and noise-reduced operational parameter data; and

executing a set of candidate snapshot anomalies upon the noise-reduced fault log data and upon the noise-reduced operational parameter data to generate data predictive of malfunctions of the machine.

15. The method of claim 14 further comprising a step of combining any candidate snapshot anomalies respectively triggered by the noise-reduced fault log data with any candidate snapshot anomalies respectively triggered by the noise-reduced parameter data.

16. The method of claim 14 further comprising selecting at least one repair for each predicted malfunction using a plurality of respective weighted repairs, and respective combinations of distinct clusters of faults.

17. The method of claim 14 further comprising selecting at least one repair for each predicted malfunction using a plurality of respective weighted repairs, and respective combinations of distinct clusters of faults and/or operational parameters.

18. The method of claim 14 wherein the respective snapshot observations of the operational parameters from the machine and the logging of respective faults from the machine are temporally aligned relative to one another.

19. A method for analyzing fault log data and operational parameter data from a machine undergoing diagnostics, the method comprising:

receiving fault log data comprising a plurality of faults from the machine;

receiving operational parameter data comprising a plurality of snapshot observations of operational parameters from the machine;

executing a set of candidate snapshot anomalies upon the fault log data and upon the operational parameter data; and

combining any candidate snapshot anomalies respectively triggered by the fault log data with any candidate anomalies respectively triggered by the parameter data to generate data predictive of malfunctions of the machine.

20. The method of claim 19 wherein prior to executing the set of candidate snapshot anomalies a step of executing a set of noise-reduction filters is performed upon the fault log data and the operational parameter data.

21. A system for analyzing fault log data and operational parameter data from a machine undergoing diagnostics, the system comprising:

means for receiving fault log data comprising a plurality of faults from the machine;

a memory unit configured to store a set of noise-reduction filters and a set of candidate snapshot anomalies; and

a processor respectively coupled to the means for receiving fault log data and to the memory unit, the processor comprising;

means for executing the set of noise-reduction filters upon the received fault log data to generate noise-reduced fault log data; and

means for executing the set of candidate snapshot anomalies upon the noise-reduced data to generate data predictive of malfunctions of the machine.

22. The system of claim 21 further comprising means for selecting at least one repair for each predicted malfunction using a plurality of respective weighted repairs, and respective combinations of distinct clusters of faults.

23. The system of claim 21 further comprising means for receiving operational parameter data comprising a plurality of snapshot observations of respective operational parameters from the machine.

24. The system of claim 23 wherein the respective snapshot observations of the operational parameters from the machine and the logging of respective faults from the machine are temporally aligned relative to one another.

25. The system of claim 23 wherein the processor further comprises means for executing the set of noise reduction filters upon the operational parameter data so as to generate noise-reduced operational parameter data.

26. The system of claim 25 wherein the processor further comprises means for executing the set of candidate snapshot anomalies upon the noise-reduced operational parameter data.

27. The system of claim 26 further comprising means for combining any candidate snapshot anomalies respectively triggered by the noise-reduced fault log data with any candidate snapshot anomalies respectively triggered by the noise-reduced parameter data so as to enhance the accuracy of the data predictive of malfunctions of the machine.

28. The system of claim 27 further comprising means for selecting at least one repair for each predicted malfunction using a plurality of respective weighted repairs, and respective combinations of distinct clusters of faults and/or operational parameters.

29. A system for analyzing fault log data and operational parameter data from a machine undergoing diagnostics, the system comprising:

means for receiving fault log data comprising a plurality of faults from the machine;

means for receiving operational parameter data comprising a plurality of operational parameters;

a memory unit configured to store a set of noise-reduction filters and a set of candidate snapshot anomalies; and

a processor respectively coupled to the means for receiving fault log data and to the memory unit, the processor comprising;

means for executing the set of noise-reduction filters upon the received fault log data and upon the operational parameter data to generate noise-reduced hybrid data; and

means for executing the set of candidate snapshot anomalies upon the noise-reduced hybrid data to generate data predictive of malfunctions of the machine.

30. An article of manufacture comprising:

a computer program product comprising a computer-usable medium having a computer-readable code therein for executing the analysis of fault log data and operational parameter data from a machine undergoing diagnostics, the computer-readable code in the article of manufacture comprising;

a computer-readable program code module for receiving fault log data comprising a plurality of faults from the machine;

a computer-readable program code module for receiving operational parameter data comprising a plurality of snapshot observations of operational parameters;

a computer-readable program code module embodying therein a set of noise-reduction filters and a set of candidate snapshot anomalies;

a computer-readable program code module for executing the set of noise-reduction filters jointly upon the received fault log data and upon the operational parameter data to generate noise-reduced hybrid data; and

a computer-readable program code module for executing the set of candidate snapshot anomalies upon the noise-reduced hybrid data to generate data predictive of malfunctions of the machine.

31. A method for analyzing fault log data and operational parameter data from railroad locomotives and other large land-based, self-powered transport equipment undergoing diagnostics, the method comprising:

receiving fault log data comprising a plurality of faults from the equipment;

receiving operational parameter data comprising a plurality of snapshot observations of operational parameters from the equipment;

executing a set of candidate snapshot anomalies upon the fault log data and upon the operational parameter data; and

considering any candidate snapshot anomalies respectively triggered by the fault log data in light of any candidate anomalies respectively triggered by the parameter data to generate data predictive of malfunctions of the equipment.

32. The method of claim 31 wherein prior to executing the set of candidate snapshot anomalies a step of executing a set of noise-reduction filters is performed upon the fault log data and the operational parameter data.

33. The method of claim 31 wherein the considering of data comprises comparing the fault log data and parameter data.

34. The method of claim 31 wherein the considering of data comprises combining the fault log data and parameter log data.

35. A system for analyzing fault log data and operational parameter data from railroad locomotives and other large land-based, self-powered transport equipment undergoing diagnostics, the system comprising:

a module for receiving fault log data comprising a plurality of faults from the equipment;

a memory unit configured to store a set of noise-reduction filters and a set of candidate snapshot anomalies; and

a processor respectively coupled to the module for receiving fault log data and to the memory unit, the processor comprising;

a processor for executing the set of noise-reduction filters upon the received fault log data to generate noise-reduced fault log data; and

a processor for executing the set of candidate snapshot anomalies upon the noise-reduced data to generate data predictive of malfunctions of the equipment.

36. The system of claim 35 further comprising a memory unit receiving operational parameter data comprising a plurality of snapshot observations of respective operational parameters from the equipment;

a processor for executing the set of noise reduction filters upon the operational parameter data so as to generate noise-reduced operational parameter data; and

a processor for executing the set of candidate snapshot anomalies upon the noise-reduced operational parameter data.

37. The system of claim 35 further comprising a processor for considering any candidate snapshot anomalies respectively triggered by the noise-reduced fault log data in light of any candidate snapshot anomalies respectively triggered by the noise-reduced parameter data so as to enhance the accuracy of the data indicative of malfunctions of the equipment.

38. A process for analyzing fault log data from railroad locomotives and other large land-based, self-powered transport equipment so as to identify respective faults and/or fault combinations indicative of machine malfunctions, the process comprising:

a) collecting a set of new fault log data from the equipment;

b) retrieving prior fault log data of the equipment held in memory, the prior fault log data having been collected at an earlier time than the data collected in step a);

c) considering the new fault log data in light of the prior fault log data; and

d) adjusting any repair recommendations based on the prior fault log data in light of the consideration of the new fault log data.

39. The process of claim 38 wherein the considering of data comprises determining respective occurrences of any new faults since the last collection of data.

40. The process of claim 38 wherein the considering of data comprises determining respective occurrences of any repeat faults.

41. The process of claim 38 wherein the repair recommendation is based upon considering respective repair entries in an externally-derived file of repairs, each fault being associated with at least one or more repairs.

42. The process of claim 38 wherein the fault log data further comprises snapshot observations of predetermined operational parameters from the equipment.

43. A system for analyzing fault log data from railroad locomotives and other large land-based, self-powered transport equipment to identify respective faults and/or fault combinations indicative of equipment malfunctions, the system comprising:

memory for collecting and storing new fault log data from the equipment;

a processor for retrieving prior fault log data of the equipment, the prior fault log data obtained during an earlier collection of data relative to the collection of new fault log data;

a processor for considering the new fault log data in light of the prior fault log data; and

a processor for adjusting any repair recommendations for the earlier fault log data based in light of the consideration of the new fault log data.

44. The system of claim 43 further comprising memory for an externally-derived file of repairs, each active fault being associated with at least one or more repairs.