Machine condition monitoring using discontinuity detection

• US 20080288213A1
• Filed: 03/18/2008
• Published: 11/20/2008
• Est. Priority Date: 04/12/2007
• Status: Active Grant

• Current Assignee: Siemens Corp. (Siemens AG)
• Original Assignee: Siemens Corp. (Siemens AG)
• Inventors: Neubauer, Claus, Yuan, Chao
• Granted Patent: US 7,949,497 B2
• Time in Patent Office: Days
• US Class Current: 702/185
• CPC Class Codes: G05B 23/0232 based on qualitative trend analysis, e.g. system evolution

Abstract

Condition signals of machines are observed and one or more discontinuities are detected in the condition signals. The discontinuities in the condition signals are compensated for (e.g., by applying a shifting factor to models of the signals) and trends of the compensated condition signals are determined. The trends are used to predict future fault conditions in machines. Kalman filters comprising observation models and evolution models are used to determine the trends. Discontinuity in observed signals is detected using hypothesis testing.

Claims

1. A method of machine condition monitoring comprising:

receiving a condition signal;

detecting a discontinuity in the condition signal;

compensating for the discontinuity in the condition signal; and

determining a trend of the compensated condition signal.

2. The method of claim 1 wherein the condition signal comprises an actual condition component and a noise component.

3. The method of claim 2 wherein detecting a discontinuity in the condition signal comprises:

(a) determining a probability of a discontinuity in the condition signal;

(b) determining a conditional probability of the actual condition signal given the received condition signal;

(c) determining a shifting factor;

(d) determining a conditional probability of the actual signal given the received condition signal and the shifting factor of (c);

(e) iteratively repeating (c) and (d);

(f) selecting a maximum of the conditional probabilities of the actual signal given the received condition signal and the shifting factors;

(g) determining a first discontinuity evaluation by multiplying the probability of no discontinuity in the condition signal with the conditional probability of the actual condition signal given the received condition signal;

(h) determining a second discontinuity evaluation by multiplying the probability of a discontinuity in the condition signal with the conditional probability of the actual condition signal given the received condition signal; and

(i) indicating a discontinuity in the condition signal only if the second discontinuity evaluation is greater than the first discontinuity evaluation.

4. The method of claim 2 further comprising:

determining the actual condition signal with a Kalman filter.

5. The method of claim 4 wherein the Kalman filter comprises an observation model of the condition signal and an evaluation model of the condition signal and compensating for the discontinuity in the condition signal comprises:

applying a shifting factor to the evolution model of the condition signal.

6. The method of claim 3 wherein compensating for the discontinuity in the condition signal comprises:

applying to an evolution model of the condition signal the shifting factor corresponding to the maximum of the conditional probabilities of the actual signal given the received condition signal and the shifting factors to an evolution model of the condition signal.

7. The method of claim 1 further comprising:

predicting a fault condition based at least in part on the determined trend of the compensated condition signal.

8. The method of claim 1 wherein detecting a discontinuity in the condition signal comprises:

determining a shifting factor;

conducting hypothesis testing based at least in part on the shifting factor to determine if a discontinuity in the condition signal exits.

9. A method of trend analysis comprising:

receiving an observed signal;

detecting a discontinuity in the observed signal; and

compensating for the discontinuity in the observed signal by shifting a portion of a trend analysis curve.

10. The method of claim 9 further comprising:

estimating an actual signal based on the observed signal using a Kalman filter.

11. The method of claim 9 wherein detecting a discontinuity in the condition signal comprises:

(a) determining a probability of a discontinuity in the observed signal;

(b) determining a conditional probability of an actual signal given the observed signal;

(c) determining a shifting factor;

(d) determining a conditional probability of the actual signal given the observed signal and the shifting factor of (c);

(e) iteratively repeating (c) and (d);

(f) selecting a maximum of the conditional probabilities of the actual signal given the observed signal and the shifting factors;

(g) determining a first discontinuity evaluation by multiplying the probability of no discontinuity in the actual signal with the conditional probability of the actual signal given the observed signal;

(h) determining a second discontinuity evaluation by multiplying the probability of a discontinuity in the actual signal with the conditional probability of the actual signal given the observed signal; and

(i) indicating a discontinuity in the condition signal only if the second discontinuity evaluation is greater than the first discontinuity evaluation.

12. The method of claim 9 wherein detecting a discontinuity in the observed signal comprises:

determining a shifting factor;

conducting hypothesis testing based at least in part on the shifting factor to determine if a discontinuity in the condition signal exits.

13. An apparatus for machine condition monitoring comprising:

means for receiving a condition signal;

means for detecting a discontinuity in the condition signal;

means for compensating for the discontinuity in the condition signal; and

means for determining a trend of the compensated condition signal.

14. The apparatus of claim 13 wherein the means for detecting a discontinuity in the condition signal comprises:

(a) means for determining a probability of a discontinuity in the condition signal;

(b) means for determining a conditional probability of an actual condition signal given the received condition signal;

(c) means for determining a shifting factor;

(d) means for determining a conditional probability of the actual signal given a received condition signal and a shifting factor;

(e) means for selecting a maximum conditional probability of the actual signal given a received condition signal and a shifting factor;

(f) means for determining a first discontinuity evaluation by multiplying the probability of no discontinuity in the condition signal with the conditional probability of the actual condition signal given the received condition signal;

(g) means for determining a second discontinuity evaluation by multiplying the probability of a discontinuity in the condition signal with the conditional probability of the actual condition signal given the received condition signal; and

(h) means for indicating a discontinuity in the condition signal only if the second discontinuity evaluation is greater than the first discontinuity evaluation.

15. The apparatus of claim 13 further comprising:

a Kalman filter configured to determine an actual condition signal.

16. The apparatus of claim 15 wherein the Kalman filter has an observation model of the condition signal and an evaluation model of the condition signal and the means for compensating for the discontinuity in the condition signal comprises:

means for applying a shifting factor to the evolution model of the condition signal.

17. The apparatus of claim 14 wherein the means for compensating for the discontinuity in the condition signal comprises:

means for applying to an evolution model of the condition signal the shifting factor corresponding to the maximum conditional probability of the actual signal given a received condition signal and a shifting factor to an evolution model of the condition signal.

18. The apparatus of claim 13 further comprising:

means for predicting a fault condition based at least in part on the determined trend of the compensated condition signal.

19. The apparatus of claim 13 wherein the means for detecting a discontinuity in the condition signal comprises:

means for determining a shifting factor;

means for conducting hypothesis testing based at least in part on the shifting factor to determine if a discontinuity in the condition signal exits.

20. A machine readable medium having program instructions stored thereon, the instructions capable of execution by a processor and defining the steps of:

receiving a condition signal;

detecting a discontinuity in the condition signal;

compensating for the discontinuity in the condition signal; and

determining a trend of the compensated condition signal.

21. The machine readable medium of claim 20 wherein the instructions for detecting a discontinuity in the condition signal further define the steps of:

(a) determining a probability of a discontinuity in the condition signal;

(b) determining a conditional probability of an actual condition signal given the received condition signal;

(c) determining a shifting factor;

(d) determining a conditional probability of the actual signal given the received condition signal and the shifting factor of (c);

(e) iteratively repeating (c) and (d);

(f) selecting a maximum of the conditional probabilities of the actual signal given the received condition signal and the shifting factors;

(g) determining a first discontinuity evaluation by multiplying the probability of no discontinuity in the condition signal with the conditional probability of the actual condition signal given the received condition signal;

(h) determining a second discontinuity evaluation by multiplying the probability of a discontinuity in the condition signal with the conditional probability of the actual condition signal given the received condition signal; and

(i) indicating a discontinuity in the condition signal only if the second discontinuity evaluation is greater than the first discontinuity evaluation.

22. The machine readable medium of claim 20 wherein the instructions further define the step of:

determining an actual condition signal with a Kalman filter.

23. The machine readable medium of claim 22 wherein the Kalman filter comprises an observation model of the condition signal and an evaluation model of the condition signal and the instructions for compensating for the discontinuity in the condition signal further define the step of:

applying a shifting factor to the evolution model of the condition signal.

24. The machine readable medium of claim 21 wherein the instructions for compensating for the discontinuity in the condition signal further define the step of:

applying to an evolution model of the condition signal the shifting factor corresponding to the maximum of the conditional probabilities of the actual signal given the received condition signal and the shifting factors to an evolution model of the condition signal.

25. The machine readable medium of claim 20 wherein the instructions further define the step of:

predicting a fault condition based at least in part on the determined trend of the compensated condition signal.

26. The machine readable medium of claim 20 wherein the instructions for detecting a discontinuity in the condition signal further define the steps of:

determining a shifting factor;

conducting hypothesis testing based at least in part on the shifting factor to determine if a discontinuity in the condition signal exits.