Diagnostic systems and methods for predictive condition monitoring

• US 20060036403A1
• Filed: 08/15/2005
• Published: 02/16/2006
• Est. Priority Date: 04/10/2001
• Status: Active Grant

• Current Assignee: SmartSignal Corporation (Emerson Electric Company)
• Original Assignee: SmartSignal Corporation (Emerson Electric Company)
• Inventors: Wegerich, Stephan W., Wolosewicz, Andre, Pipke, R. Matthew
• Granted Patent: US 7,308,385 B2
• Time in Patent Office: Days
• US Class Current: 702/183
• CPC Class Codes: G05B 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
• CPC Class Codes: G05B 23/0281 Quantitative, e.g. mathematical distance; Clustering; Neural networks; Statistical analysis
• CPC Class Codes: G06F 18/2433 Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
• CPC Class Codes: G06F 2218/12 Classification; Matching

Abstract

A system for empirically diagnosing a condition of a monitored system. Estimates of monitored parameters from a model of the system provide residual values that can be analyzed for failure mode signature recognition. Residual values can also be tested for alert (non-zero) conditions, and patterns of alerts thus generated are analyzed for failure mode signature patterns. The system employs a similarity operator for signature recognition and also for parameter estimation. Failure modes are empirically determined, and precursor data is automatically analyzed to determine differentiable signatures for failure modes.

Claims

1. A monitoring apparatus for diagnosing faults in a system, comprising:

a reference data store containing failure mode identification data and associated system data sampled from behavior of said system in the failure mode; and

a similarity engine responsive to monitored system data indicative of monitored behavior of said system, for generating at least one similarity value for a comparison of the monitored data to said failure mode associated system data, as a diagnostic indication of said failure mode.

2. An apparatus according to claim 1, wherein said system data is residual data.

3. An apparatus according to claim 2, further comprising:

a model for generating estimates of operational data in response to receiving operational data from said system; and

a signal generator for differencing the estimates and the received operational data to generate the residual data.

4. An apparatus according to claim 3, wherein said model for generating estimates is a non-parametric model.

5. An apparatus according to claim 1, further comprising a failure identification module responsive to similarity values from the similarity engine for determining an indicated failure mode.

6. An apparatus according to claim 5, wherein said failure identification module compares similarity values for a plurality of failure modes in said data store, and identifies at least the failure mode with the highest similarity as an indicated failure mode of said system.

7. An apparatus according to claim 5, wherein said failure identification module compares similarity values for a plurality of failure modes in said data store, and identifies at least the failure mode with the highest average similarity as an indicated failure mode of said system.

8. An apparatus according to claim 5, wherein said failure identification module compares similarity values for a plurality of failure modes in said data store, and identifies as an indicated failure mode of said system at least the failure mode with at least a selected number of highest similarities over a window of successive comparisons.

9. An apparatus according to claim 5, wherein said failure identification module compares similarity values for a plurality of failure modes in said data store, and identifies as an indicated failure mode of said system at least the failure mode with at least a selected number of highest average similarities over a window of successive comparisons.

10. A method for diagnosing faults in a monitored system, comprising the steps of:

acquiring monitored system data indicative of monitored behavior of said system;

sampling system data from a failure mode to define reference system data associated with said failure mode, and

comparing for similarity the monitored system data to said reference system data to generate a similarity value as a diagnostic indication of said failure mode.

11. A method according to claim 10, wherein said system data is residual data.

12. A method according to claim 11, further comprising the steps of:

generating estimates of operational data in response to acquiring operational data from said system; and

differencing the estimates and the received operational data to generate the residual data.

13. A method according to claim 10, further comprising the step of determining an indicated failure mode based on similarity values resulting from the similarity comparisons.

14. A method according to claim 13, wherein said determining step comprises comparing the similarity values for a plurality of failure modes, and identifying at least the failure mode with the highest similarity as an indicated failure mode of said system.

15. A method according to claim 13, wherein said determining step comprises comparing the similarity values for a plurality of failure modes, and identifying at least the failure mode with the highest average similarity as an indicated failure mode of said system.

16. A method according to claim 13, wherein said determining step comprises comparing the similarity values for a plurality of failure modes, and identifying as an indicated failure mode of said system at least the failure mode with at least a selected number of highest similarities over a window of successive comparisons.

17. A method according to claim 13, wherein said determining step comprises comparing the similarity values for a plurality of failure modes, and identifying as an indicated failure mode of said system at least the failure mode with at least a selected number of highest average similarities over a window of successive comparisons.

18. A monitoring apparatus for diagnosing faults in a system, comprising:

a similarity engine responsive to monitored parameter data from said system for generating estimates of the monitored parameter data;

means for differencing the estimates and the monitored data to generate residual data; and

means for recognizing an impending failure mode by comparing for similarity said residual data to reference residual data associated with the failure mode.

19. An apparatus according to claim 18, further comprising means for communicating remedial control commands to a control program for said system.

20. An apparatus according to claim 18, further comprising a control module for operating said system, responsive to recognition of an impending failure of said system from said recognizing means for remedially controlling said system.

21. An apparatus according to claim 18, further comprising a profile data store for storing and providing reference parameter data characteristic of known behavior of said system, to said similarity engine for generation of the estimates.

22. An apparatus according to claim 18, further comprising a failure mode data store for storing said reference residual data and associated failure mode identification data.

23. An apparatus according to claim 22 wherein said failure mode data store also stores information about remedial steps specific to a failure mode.