Diagnostic systems and methods for predictive condition monitoring

• US 20040078171A1
• Filed: 10/09/2003
• Published: 04/22/2004
• 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 7,539,597 B2
• Time in Patent Office: Days
• US Class Current: 702/188
• 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 indicative of 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; and

comparing for similarity the monitored system data to reference system data associated with a failure mode 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 kernel-based non-parametric model responsive to monitored parameter data from said system for generating estimates of the monitored parameter data;

an alert module disposed to produce parameter alerts in response to a comparison of said estimates to said monitored data; and

a failure identification module for identifying an impending failure mode in said system by matching said parameter alerts with at least one reference alert pattern associated with said failure mode.

19. An apparatus according to claim 18, wherein said alert module performs a sequential probability ratio test on at least one parameter.

20. An apparatus according to claim 18, wherein said alert module produces an alert when the difference of an estimate and corresponding monitored data exceeds a selected threshold.

21. An apparatus according to claim 18, wherein said kernel-based non-parametric model employs a Nadaraya-Watson kernel regression.

22. An apparatus according to claim 18, wherein said kernel-based non-parametric model is a similarity-based model.

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

comparing for similarity monitored parameter data from said system to reference parameter data characteristic of known behavior of said system;

generating estimates of the monitored parameter data based on the similarity comparison;

generating alerts in response to a comparison of said estimates to said monitored data; and

identifying an impending failure mode in said system by matching said parameter alerts with at least one reference alert pattern associated with said failure mode.

24. A method according to claim 23, wherein said alert generating step comprises performing a sequential probability ratio test on at least one parameter.

25. A method according to claim 23, wherein said alert generating step comprises producing an alert when the difference of an estimate and corresponding monitored data exceeds a selected threshold.

26. 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.

27. An apparatus according to claim 26, further comprising means for communicating remedial control commands to a control program for said system.

28. An apparatus according to claim 26, 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.

29. An apparatus according to claim 26, 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.

30. An apparatus according to claim 26, further comprising a failure mode data store for storing said reference residual data and associated failure mode identification data.

31. An apparatus according to claim 30 wherein said failure mode data store also stores information about remedial steps specific to a failure mode.

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

comparing for similarity monitored parameter data from said system to reference parameter data characteristic of known behavior of said system;

generating estimates of the monitored parameter data based on the similarity comparison;

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

comparing for similarity the residual data to reference residual data associated with a failure mode, as a diagnostic indication of said failure mode.

33. A method according to claim 32, further comprising the step of communicating a remedial control command to a control program for said system.

34. A method according to claim 32, further comprising the steps of:

recognizing an impending failure mode based on the residual similarity comparison step; and

controlling remedially said system responsive to recognition of an impending failure of said system.

35. A computer program product for diagnosing faults in a monitored system, comprising:

a residual testing program module for generating alerts in response to residual signals characterizing behavior of said system; and

a fault pattern detection program module disposed to receive said alerts and indicate a fault diagnosis in said system upon matching the alerts to a reference pattern associated with said fault.

36. A program product according to claim 35, wherein the residual testing program module performs a sequential probability ratio test on said residuals.

37. A program product according to claim 35, wherein the residual testing program module compares a residual to a threshold to generate an alert when the residual exceeds the threshold.

38. A program product according to claim 35, further comprising a kernel-based non-parametric model program module disposed to generate estimates of parameter signals from said system, and difference the parameter signals and the estimates to generate the residual signals.

39. A program product according to claim 38, wherein said kernel-based non-parametric model program module employs a similarity-based model to generate said estimates.

40. A program product according to claim 39 wherein said similarity-based model uses elemental similarities.

41. A program product according to claim 39 wherein said similarity-based model is a radial basis function network.

42. A program product according to claim 38 wherein said kernel-based non-parametric model program module employs a kernel regression model to generate said estimates.

43. A method for forming a diagnostic monitoring system for a machine, comprising the steps of:

instrumenting a plurality of like machines with sensors;

operating said plurality of machines and collecting operational sensor data characterizing operation;

autopsying each of said plurality of machines upon failure to determine a failure mode, and storing the failure modes for use in the diagnostic monitoring system;

selecting a failure precursor portion of said operational sensor data collected for a selected interval prior to failure of each machine; and

distilling each said failure precursor data portion into at least one failure signature data set and associating each set with a corresponding failure mode.

44. A method according to claim 43 wherein said distilling step comprises selecting time-correlated snapshots of operational sensor data.

45. A method according to claim 43 wherein said distilling step comprises generating time-correlated snapshots of estimated sensor data corresponding to at least a selected failure precursor data portion, differencing the estimates with the selected failure precursor data to form residual snapshots, and selecting at least one residual snapshot to be associated with the failure mode.

46. A method according to claim 45 wherein the estimated sensor data is generated by an empirical model based on the collected operational sensor data.

47. A diagnostic monitoring apparatus for a sensor-instrumented system selected from a process and a machine, comprising:

an operational model module for modeling said system and generating estimates for said sensors in response to receiving actual values of said sensors;

a differencing module for generating residual signals from said estimates and said actual values;

a reference library for storing failure modes and associated residual data values; and

a failure mode recognition engine disposed to compare generated residual signals with said residual data values to select and output a recognized failure mode for said system.

48. A diagnostic monitoring apparatus according to claim 47, wherein said failure mode recognition engine comprises a similarity operation module for generating a similarity score for a comparison of said generated residual signals with said residual data values in said reference library.

49. A diagnostic monitoring apparatus according to claim 48, wherein said failure mode recognition engine further comprises a failure mode decision module responsive to the similarity scores generated by the similarity operation module to select at least one failure mode to output.

50. A diagnostic monitoring apparatus for a sensor-instrumented system selected from a process and a machine, comprising:

an operational model module for modeling said system and generating estimates for said sensors in response to receiving actual values of said sensors;

a differencing module for generating residual signals from said estimates and said actual values;

a testing module disposed to receive said residual signals and generate alerts in response thereto;

a reference library for storing failure modes and associated alert signatures; and

a failure mode recognition engine disposed to compare generated alerts with said alert signatures to select and output a recognized failure mode for said system.

57. A diagnostic monitoring apparatus according to claim 50, wherein said similarity operator comprises a training matrix utilizing the empirical model estimates of the parameter values from said memory to determine similarity as a function of an absolute magnitude in response to the observed states of the monitored process.

58. A diagnostic monitoring apparatus according to claim 50, wherein said similarity operator comprises:

a model for generating estimates of signals representative of the monitored process in response to the actual acquired signals from the data acquisition device operable at the process monitoring site; and

a similarity engine for generating a similarity score for a comparison of a set of signals from said model and a related set of acquired signals from said data acquisition device.

62. A diagnostic monitoring apparatus according to claim 50, wherein said failure mode recognition engine comprises a similarity operation module for generating a similarity score for a comparison of said current sensor data with a snapshot of sensor data stored in said reference library.

64. A diagnostic monitoring apparatus according to claim 57, wherein said failure decision module selects the at least one failure mode based on a highest similarity score for any one snapshot in said reference library.

65. A diagnostic monitoring apparatus according to claim 57, wherein said failure decision module selects the at least one failure model based on a highest average similarity score across sets of snapshots associated with each failure mode in said reference library.

51. A diagnostic monitoring apparatus for a sensor-instrumented system selected from a process and a machine, comprising:

sensor input for receiving current sensor data from said system in operation;

an information processor for collecting the current sensor data from the sensor input at selected time snapshots associated with the signals at the sensor input being indicative of identified states of the monitored process;

a reference library for storing failure modes and associated snapshots of sensor data from said system;

a failure mode recognition engine disposed to compare current sensor data with said snapshots to select and output a recognized failure mode for said system.

52. A diagnostic monitoring apparatus according to claim 51, wherein said failure mode recognition engine comprises a similarity operation module for generating a similarity score for a comparison of said current sensor data with a snapshot of sensor data stored in said reference library.

53. A diagnostic monitoring apparatus according to claim 52, wherein said failure mode recognition engine further comprises a failure mode decision module responsive to the similarity scores generated by the similarity operation module to select at least one failure mode to output.

54. A diagnostic monitoring apparatus according to claim 53, wherein said failure decision module selects the at least one failure mode based on a highest similarity score for any one snapshot in said reference library.

55. A diagnostic monitoring apparatus according to claim 53, wherein said failure decision module selects the at least one failure mode based on a highest average similarity score across sets of snapshots associated with each failure mode in said reference library.

59. A diagnostic monitoring apparatus according to claim 52, wherein said similarity engine generates the similarity score within a bounded scaler range, with the absolute value of the similarity increasing with generated estimates and the acquired signals approaching identical values.

60. A diagnostic monitoring apparatus according to claim 52, wherein said similarity engine comprises kernel regression empirical modeling to generate an estimate based on a current observation.

61. A diagnostic monitoring apparatus according to claim 54, wherein the kernel regression comprises a Gaussian kernel.

56. A diagnostic monitoring apparatus for a sensor-instrumented system selected from a process and a machine, comprising:

a sensor input receiver for receiving current sensor data from conveyed signals acquired at selected time snapshots as inputs indicative of said system in operation;

a memory for storing empirical model estimates of parameter values in response to collected signals indicative of the monitored process corresponding to a universe of identified states of the monitored process;

a reference library for storing failure modes and associated snapshots of sensor data from said system;

an information processor for collecting the conveyed signals acquired at selected time snapshots as inputs indicative of identified states of the monitored process;

a similarity operator implemented within said information processor operable on the acquired time snapshots and the parameter values from said memory for generating an expected state vector responsive to said similarity operator; and

a failure mode recognition engine disposed to compare current sensor data with said snapshots to select and output a recognized failure mode for said system.

63. A diagnostic monitoring apparatus according to claim 56, wherein said failure mode recognition engine further comprises a failure mode decision module responsive to the similarity scores generated by the similarity operation module to select at least one failure mode to output.