Root cause diagnostics using temporal data mining

• US 7,509,234 B2
• Filed: 08/16/2007
• Issued: 03/24/2009
• Est. Priority Date: 08/16/2007
• Status: Active Grant

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations Incorporated (General Motors Company)
• Inventors: Laxman, Srivatsan, Shadid, Basel Q., Sastry, P. S., Unnikrishnan, K. P.
• Primary Examiner(s): Nghiem; Michael P
• Application Number:
  US11/839,775
• Publication Number:
  US 20090049338A1
• Time in Patent Office: 586 Days
• Field of Search: 702/58, 702/59, 702/183, 702/185, 714/5, 714/31, 714/48
• US Class Current: 702/183
• CPC Class Codes: G05B 23/0281 Quantitative, e.g. mathematical distance; Clustering; Neural networks; Statistical analysis

Abstract

A method, system, and computer program product for fault data correlation in a diagnostic system are provided. The method includes receiving the fault data including a plurality of faults collected over a period of time, and identifying a plurality of episodes within the fault data, where each episode includes a sequence of the faults. The method further includes calculating a frequency of the episodes within the fault data, calculating a correlation confidence of the faults relative to the episodes as a function of the frequency of the episodes, and outputting a report of the faults with the correlation confidence.

Claims

1. A method for fault data correlation in a diagnostic system, comprising:

receiving the fault data including a plurality of faults collected over a period of time;

identifying a plurality of episodes within the fault data, wherein each episode includes a sequence of the faults;

calculating a frequency of the episodes within the fault data;

categorizing each of the episodes as an episode type, wherein the episode type is one of a well-known episode, an expected episode, and an unexpected episode;

calculating a correlation confidence of the faults relative to the episodes as a function of the frequency of the episodes;

outputting a report of the faults with the correlation confidence; and

filtering the report as a function of the episode type.

2. The method of claim 1 wherein the fault data includes a temporal data series comprising events with start times and end times, a set of allowed dwelling times, and a threshold frequency.

3. The method of claim 2 further comprising:

finding all frequent principal episodes of a particular length in the temporal data series having dwelling times, as determined by the start and end times, within the allowed dwelling times;

in successive passes through the temporal data series;

incrementing the particular length to generate an increased length;

combining frequent principal episodes to create combined episodes of the increased length;

creating a set of candidate episodes from the combined episodes by removing combined episodes which have non-frequent sub-episodes;

identifying one or more occurrences of a candidate episode in the temporal data series;

incrementing a count for each identified occurrence;

determining frequent principal episodes of the increased length; and

setting the particular length to the increased length; and

producing an output for frequent principal episodes;

wherein a frequent principal episode is a principal episode whose count of occurrences results in a frequency meeting or exceeding the threshold frequency.

4. The method of claim 1 further comprising:

identifying an initial fault in the sequence of faults in one of the episodes as a root cause; and

reporting the root cause.

5. The method of claim 1 further comprising:

calculating downtime as time duration between an initial fault and a final fault in the sequence of faults in one of the episodes; and

reporting the downtime.

6. The method of claim 1 wherein the fault data is limited to a selected period of time.

7. A system for fault data correlation in a diagnostic system, the system comprising:

a host system;

a data storage device in communication with the host system, the data storage device holding fault data; and

a temporal data mining (TDM) diagnostics tool executing on the host system, the TDM diagnostics tool including computer instructions for performing;

receiving the fault data including a plurality of faults collected over a period of time;

identifying a plurality of episodes within the fault data, wherein each episode includes a sequence of the faults;

calculating a frequency of the episodes within the fault data;

categorizing each of the episodes as an episode type, wherein the episode type is one of a well-known episode, an expected episode, and an unexpected episode;

calculating a correlation confidence of the faults relative to the episodes as a function of the frequency of the episodes;

outputting a report of the faults with the correlation confidence; and

filtering the report as a function of the episode type.

8. The system of claim 7 wherein the fault data includes a temporal data series comprising events with start times and end times, a set of allowed dwelling times, and a threshold frequency.

9. The system of claim 8 wherein the TDM diagnostics tool further performs:

finding all frequent principal episodes of a particular length in the temporal data series having dwelling times, as determined by the start and end times, within the allowed dwelling times;

in successive passes through the temporal data series;

incrementing the particular length to generate an increased length;

combining frequent principal episodes to create combined episodes of the increased length;

creating a set of candidate episodes from the combined episodes by removing combined episodes which have non-frequent sub-episodes;

identifying one or more occurrences of a candidate episode in the temporal data series;

incrementing a count for each identified occurrence;

determining frequent principal episodes of the increased length; and

setting the particular length to the increased length; and

producing an output for frequent principal episodes;

wherein a frequent principal episode is a principal episode whose count of occurrences results in a frequency meeting or exceeding the threshold frequency.

10. The system of claim 7 wherein the TDM diagnostics tool further performs:

identifying an initial fault in the sequence of faults in one of the episodes as a root cause; and

reporting the root cause.

11. The system of claim 7 wherein the TDM diagnostics tool further performs:

calculating downtime as time duration between an initial fault and a final fault in the sequence of faults in one of the episodes; and

reporting the downtime.

12. The system of claim 7 wherein the fault data is limited to a selected period of time.

13. A computer program product for fault data correlation in a diagnostic system, the computer program product comprising:

a storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for implementing a method, the method comprising;

receiving the fault data including a plurality of faults collected over a period of time;

identifying a plurality of episodes within the fault data, wherein each episode includes a sequence of the faults;

calculating a frequency of the episodes within the fault data;

categorizing each of the episodes as an episode type, wherein the episode type is one of a well-known episode, an expected episode, and an unexpected episode;

calculating a correlation confidence of the faults relative to the episodes as a function of the frequency of the episodes;

outputting a report of the faults with the correlation confidence; and

filtering the report as a function of the episode type.

14. The computer program product of claim 13 wherein the fault data includes a temporal data series comprising events with start times and end times, a set of allowed dwelling times, and a threshold frequency.

15. The computer program product of claim 14 further comprising:

finding all frequent principal episodes of a particular length in the temporal data series having dwelling times, as determined by the start and end times, within the allowed dwelling times;

in successive passes through the temporal data series;

incrementing the particular length to generate an increased length;

combining frequent principal episodes to create combined episodes of the increased length;

creating a set of candidate episodes from the combined episodes by removing combined episodes which have non-frequent sub-episodes;

identifying one or more occurrences of a candidate episode in the temporal data series;

incrementing a count for each identified occurrence;

determining frequent principal episodes of the increased length; and

setting the particular length to the increased length; and

producing an output for frequent principal episodes;

wherein a frequent principal episode is a principal episode whose count of occurrences results in a frequency meeting or exceeding the threshold frequency.

16. The computer program product of claim 13 further comprising:

identifying an initial fault in the sequence of faults in one of the episodes as a root cause; and

reporting the root cause.

17. The computer program product of claim 13 further comprising:

calculating downtime as time duration between an initial fault and a final fault in the sequence of faults in one of the episodes; and

reporting the downtime.