Machine condition monitoring using discontinuity detection

• US 7,949,497 B2
• Filed: 03/18/2008
• Issued: 05/24/2011
• Est. Priority Date: 04/12/2007
• Status: Expired due to Fees

• Current Assignee: Siemens Corp. (Siemens AG)
• Original Assignee: Siemens Corp. (Siemens AG)
• Inventors: Neubauer, Claus, Yuan, Chao
• Primary Examiner(s): Barbee; Manuel L
• Application Number:
  US12/077,255
• Publication Number:
  US 20080288213A1
• Time in Patent Office: 1,162 Days
• Field of Search: 702182-185, 702/188, 700/108, 700/110
• 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, at a computer, a condition signal, wherein the condition signal comprises an actual condition signal and a noise component;

detecting, by the computer, a discontinuity in the condition signal;

compensating, by the computer, for the discontinuity in the condition signal; and

determining, by the computer, a trend of the compensated condition signal,

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 condition 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 condition 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.

2. The method of claim 1 further comprising:

determining the actual condition signal with a Kalman filter.

3. The method of claim 2 wherein the Kalman filter comprises an observation model of the condition signal and an evolution 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.

4. The method of claim 1 wherein compensating for the discontinuity in the condition signal comprises:

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

5. 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.

6. A method of trend analysis comprising:

receiving, at a computer, an observed signal;

detecting, by the computer, a discontinuity in the observed signal; and

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

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.

7. The method of claim 6 further comprising:

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

8. 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,

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 condition signal given a received condition signal and a shifting factor;

(e) means for selecting a maximum conditional probability of the actual condition 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.

9. The apparatus of claim 8 further comprising:

a Kalman filter configured to determine the actual condition signal.

10. The apparatus of claim 9 wherein the Kalman filter has an observation model of the condition signal and an evolution 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.

11. The apparatus of claim 8 wherein the means for compensating for the discontinuity in the condition signal comprises:

means for applying 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.

12. The apparatus of claim 8 further comprising:

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

13. A non-transitory computer 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,

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 condition 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 condition 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.

14. The non-transitory computer readable medium of claim 13 wherein the instructions further define the step of:

determining the actual condition signal with a Kalman filter.

15. The non-transitory computer readable medium of claim 14 wherein the Kalman filter comprises an observation model of the condition signal and an evolution 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.

16. The non-transitory computer readable medium of claim 13 wherein the instructions for compensating for the discontinuity in the condition signal further define the step of:

applying 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.

17. The non-transitory computer readable medium of claim 13 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.