Intelligent condition monitoring and fault diagnostic system for preventative maintenance

• US 10,120,374 B2
• Filed: 08/10/2015
• Issued: 11/06/2018
• Est. Priority Date: 07/11/2005
• Status: Active Grant

• Current Assignee: Brooks Automation Ltd. (United Kingdom) (Azenta, Inc.)
• Original Assignee: Brooks Automation Incorporated (Azenta, Inc.)
• Inventors: Hosek, Martin, Krishnasamy, Jay, Prochazka, Jan
• Primary Examiner(s): Ko, Chae M
• Application Number:
  US14/822,310
• Publication Number:
  US 20150346717A1
• Time in Patent Office: 1,184 Days
• Field of Search: 714 26
• CPC Class Codes: B25J 9/1674 characterised by safety, monitoring, diagnostic
• CPC Class Codes: G05B 19/41875 characterised by quality surveillance of production
• CPC Class Codes: G05B 23/0221 Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
• CPC Class Codes: G05B 23/0283 Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06
• CPC Class Codes: G06F 11/2257 using expert systems

Abstract

A system for condition monitoring and fault diagnosis includes a data collection function that acquires time histories of selected variables for one or more of the components, a pre-processing function that calculates specified characteristics of the time histories, an analysis function for evaluating the characteristics to produce one or more hypotheses of a condition of the one or more components, and a reasoning function for determining the condition of the one or more components from the one or more hypotheses.

Claims

1. A continuous health monitoring system comprising:

a controller including

a non-transitory data collection function that acquires unparametrized time histories of one or more component energy dissipation values during component operations;

a non-transitory pre-processing function that characterizes each unparametrized time history of the one more component energy dissipation values independently and computes metrics from characteristics of each independently characterized unparametrized time history of the one or more component energy dissipation values using an operational energy dissipation from the time histories and a baseline energy dissipation;

a non-transitory analysis function for evaluating whether the computed metrics exceed predetermined threshold values to produce one or more hypotheses of a condition of one or more components corresponding to the one or more component energy dissipation values; and

a non-transitory reasoning function for determining the condition of the one or more components from the one or more hypotheses,

wherein the data collection, pre-processing, and analysis functions operate in parallel with the component operations.

2. The system of claim 1, wherein the data collection function acquires unparametrized time histories of mechanical energy dissipation values.

3. The system of claim 1, wherein the data collection function acquires unparametrized time histories of electrical energy dissipation values.

4. The system of claim 1, wherein the data collection function acquires unparametrized time histories of energy dissipation values of a robotic joint.

5. The system of claim 1, wherein the data collection function acquires unparametrized time histories for a predefined sequence of component moves.

6. The continuous health monitoring system of claim 1, wherein the pre-processing function computes a difference between the operational energy dissipation and the baseline energy dissipation as a first metric and an exponentially weighted moving average of the difference as a second metric.

7. The system of claim 1, wherein the pre-processing function computes the predetermined threshold values using a confidence coefficient for predicting a change in the metrics.

8. The system of claim 1, wherein the baseline energy dissipation used by the pre-processing function is acquired from data obtained from a selected move sequence.

9. The system of claim 1, wherein the baseline energy dissipation used by the pre-processing function is acquired from a component model.

10. A method of continuously monitoring system health comprising:

acquiring unparametrized time histories of one or more component energy dissipation values during component operations;

characterizing each unparametrized time history of the one or more component energy dissipation values independently and computing metrics, during the component operations, from characteristics of each independently characterized unparametrized time history of the one or more component energy dissipation values using an operational energy dissipation from the time histories and a baseline energy dissipation;

in parallel with the component operations, evaluating whether the computed metrics exceed predetermined threshold values to produce one or more hypotheses of a condition of one or more components corresponding to the one or more component energy dissipation values; and

determining the condition of the one or more components from the one or more hypotheses.

11. The method of claim 10, further comprising acquiring unparametrized time histories of mechanical energy dissipation values.

12. The method of claim 10, further comprising acquiring unparametrized time histories of electrical energy dissipation values.

13. The method of claim 10, further comprising acquiring unparametrized time histories of energy dissipation values of a robotic joint.

14. The method of claim 10, further comprising acquiring unparametrized time histories for a predefined sequence of component moves.

15. The method of claim 10, further comprising computing a difference between the operational energy dissipation and the baseline energy dissipation as a first metric and an exponentially weighted moving average of the difference as a second metric.

16. The method of claim 10, further comprising computing the predetermined threshold values using a confidence coefficient for predicting a change in the metrics.

17. The method of claim 10, further comprising acquiring the baseline energy dissipation from data obtained from a selected move sequence.

18. The method of claim 10, further comprising acquiring the baseline energy dissipation from data obtained from a component model.

19. A continuous heath monitoring system comprising:

a controller including

a non-transitory data collection function that acquires unparametrized time histories of one or more values related to power consumption by a component during operation;

a non-transitory pre-processing function that characterizes each unparametrized time history of the one or more values related to power consumption independently and computes metrics from characteristics of each independently characterized unparametrized time history of the one or more values related to power consumption using an operational power consumption computed from the time histories and a power consumption baseline;

a non-transitory analysis function for evaluating whether the computed metrics exceed predetermined threshold values to produce one or more hypotheses of a condition of the component; and

a non-transitory reasoning function for determining the condition of the component from the one or more hypotheses,

wherein the data collection, pre-processing, and analysis functions operate in parallel with the component operations.

20. The system of claim 19, wherein the one or more values related to power consumption by a component include component current consumption.

21. The system of claim 19, wherein the one or more values related to power consumption by a component include or more of component position, velocity, or acceleration.

22. The system of claim 19, wherein the power consumption baseline used by the pre-processing function is acquired from a component model.

23. The system of claim 19, wherein the pre-processing function computes a difference between the operational power consumption and the baseline power consumption as a first metric and an exponentially weighted moving average of the difference as a second metric.

24. The system of claim 23, wherein the data collection function acquires the unparametrized time histories for a predefined set of component locations and the pre-processing function computes difference between the operational power consumption and he baseline power consumption those predetermined locations as the first metric.

25. The system of claim 23, wherein the data collection function acquires the unparametrized time histories for a predefined sequence of component moves and the pre-processing function computes an integral of the absolute value of the difference between the operational power consumption and the baseline power consumption over the predefined sequence of component moves as the first metric.

26. The system of claim 19, wherein the pre-processing function computes a fast Fourier transform on portions of the unparametrized time histories and on portions of the baseline power consumption, and wherein the analysis function monitors peaks emerging or shifting in a frequency spectrum from the transform.

27. A method of continuously monitoring system health comprising:

acquiring unparametrized time histories of one or more power consumption related values of a component during operation;

characterizing each unparametrized time history of the one or more power consumption related values independently and computing metrics from characteristics of each independently characterized unparametrized time history of the one or more power consumption related values during the component operations using an operational power consumption computed from the unparametrized time histories and a power consumption baseline;

in parallel with the component operations, evaluating whether the computed metrics exceed predetermined threshold values to produce one or more hypotheses of a condition of the component; and

determining the condition of the component from the one or more hypotheses.

28. The method of claim 27, wherein the one or more power consumption related values include component current consumption.

29. The method of claim 27, wherein the one or more power consumption related values include one or more of component position, velocity, or acceleration.

30. The method of claim 27, further comprising determining the baseline energy dissipation from a component model.

31. The method of claim 27, further comprising computing a difference between the operational power consumption and the baseline power consumption as a first metric and an exponentially weighted moving average of the difference as a second metric.

32. The method of claim 31, further comprising acquiring time histories for a predefined set of component locations and computing a difference between the operational power consumption and the baseline power consumption at those predetermined locations as the first metric.

33. The method of claim 31, further comprising acquiring unparametrized time histories a predefined sequence of component moves and computing an integral of the absolute value of the difference between the operational power consumption and the baseline power consumption over the predefined sequence of component moves as the first metric.

34. The method of claim 27, further comprising computing a fast Fourier transform on portions of the unparametrized time histories and on portions of the baseline power consumption, and monitoring peaks emerging or shifting in a frequency spectrum from the transform.

35. system for automatic fault diagnosis comprising:

a controller including

a non-transitory data collection function that acquires unparametrized time histories of selected power consumption related values for one or more components of a device in response to deterioration in the operation of the device;

a non-transitory pre-processing function that characterizes each unparametrized time history of the selected power consumption related values independently by calculating specified characteristics of the unparametrized time stories including a range and a minimum variance and requests additional unparametrized time history acquisition if certain thresholds are not met for the range and minimum variance;

a non-transitory analysis function for evaluating the characteristics to produce one or more hypotheses of a condition of the one or more components; and

a non-transitory reasoning function for determining faults of the one or more components or of the device from the one or more hypotheses.

36. The system of claim 35, wherein the selected power consumption related values for the one or more components include component current consumption.

37. The system of claim 35, wherein the selected power consumption related values for the one or more components include one or more of component position, velocity, or acceleration.

38. The system of claim 35, wherein the power consumption baseline used by the pre-processing function is acquired from a component model.

39. A method for automatic fault diagnosis comprising:

acquiring time unparametrized histories of selected power consumption related values for one or more components of a device in response to deterioration in the operation of the device;

characterizing each unparametrized time history of the selected power consumption related values independently by calculating specified characteristics of the unparametrized time histories including a range and a minimum variance and requests additional unparametrized time history acquisition if certain thresholds are not met for t e range and minimum variance;

evaluating the characteristics to produce one or more hypotheses of a condition of the one or more components; and

determining faults of the one or more components or of the device from the one or more hypotheses.

40. The method of claim 39, wherein the selected power consumption related values for the one or more components include component current consumption.

41. The method of claim 39, wherein the selected power consumption related values for the one or more components include one or more of component position, velocity, or acceleration.

42. The method of claim 39, wherein the power consumption baseline used by the pre processing function is acquired from a component model.