Bayesian Sensor Estimation For Machine Condition Monitoring

• US 20080086283A1
• Filed: 10/03/2007
• Published: 04/10/2008
• Est. Priority Date: 10/05/2006
• Status: Active Grant

• Current Assignee: Siemens Corp. (Siemens AG)
• Original Assignee: Siemens Corporate Research Incorporated (Siemens AG)
• Inventors: Neubauer, Claus, Yuan, Chao
• Granted Patent: US 7,565,262 B2
• Time in Patent Office: Days
• US Class Current: 702/181
• CPC Class Codes: G05B 23/024 Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
• 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 23/0283 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

Abstract

A method for monitoring a system includes receiving a set of training data. A Gaussian mixture model is defined to model a probability distribution for a particular sensor of the system from among a plurality of sensors of the system based on the received training data. The Gaussian mixture model includes a sum of k mixture components, where k is a positive integer. Sensor data is received from the plurality of sensors of the system. An expectation-maximization technique is performed to estimate an expected value for the particular sensor based on the defined Gaussian mixture model and the received sensor data from the plurality of sensors.

Claims

1. A method for monitoring a system, comprising:

receiving a set of training data;

defining a Gaussian mixture model to model a probability distribution for a particular sensor of the system from among a plurality of sensors of the system based on the received training data, the Gaussian mixture model comprising a sum of k mixture components, wherein k is a positive integer;

receiving sensor data from the plurality of sensors of the system; and

performing an expectation-maximization technique to estimate an expected value for the particular sensor based on the defined Gaussian mixture model and the received sensor data from the plurality of sensors.

2. The method of claim 1, additionally comprising:

receiving an actual sensor value from the particular sensor;

comparing the received actual sensor value to the estimated expected sensor value; and

detecting a potential fault based on the comparison.

3. The method of claim 1, wherein performing the expectation-maximization technique comprises:

ranking the k mixture components according to a degree of influence on the Gaussian mixture model;

selecting a set of mixture components that are most influential;

iteratively improving an estimation of the expected value for the particular sensor based on a diagonal covariance matrix that contributes to a relationship between the received sensor data from the plurality of sensors and the expected value for the particular sensor, using the selected set of mixture components;

iteratively improving an estimation of the diagonal covariance matrix using the selected set of mixture components; and

repeating the steps of iteratively improving the estimation of the expected value for the particular sensor and iteratively improving the estimation of the diagonal covariance matrix until the two estimates achieve convergence.

4. The method of claim 3, wherein convergence is achieved when repeating the steps of iteratively improving the estimation of the expected value for the particular sensor and iteratively improving the estimation of the diagonal covariance matrix results in a negligible improvement.

5. The method of claim 1, wherein performing the expectation-maximization technique comprises:

estimating the expected value for the particular sensor; and

estimating a diagonal covariance matrix that contributes to a relationship between the received sensor data from the plurality of sensors and the expected value for the particular sensor, wherein the two estimations are performed at substantially the same time.

6. The method of claim 1, wherein the training data comprises sensor data from the plurality of sensors and sensor data from the particular sensor taken during a period of fault-free operation of the system.

7. The method of claim 1, wherein each of the k mixture components is a Gaussian distribution defined by a mean and a variance that are determined during the performance of the expectation-maximization technique.

8. A system for monitoring a machine, comprising:

a plurality of sensors for monitoring the machine including a particular sensor;

a Gaussian mixture model defining unit for defining a Gaussian mixture model to for estimating an expected value for the particular sensor, the Gaussian mixture model comprising a sum of a plurality of mixture components; and

an estimation unit for estimating the expected value for the particular sensor based on the defined Gaussian mixture model.

9. The system of claim 8, additionally comprising training data for use by the Gaussian mixture model defining unit in defining the plurality of mixture components.

10. The system of claim 9, wherein the training data comprises sensor data from the plurality of sensors and sensor data from the particular sensor taken during a period of fault-free operation of the machine.

11. The system of claim 8, wherein an expectation-maximization technique is used to estimate an expected value for the particular sensor based on the defined Gaussian mixture model and the received sensor data from the plurality of sensors.

12. The system of claim 11, wherein performing the expectation-maximization technique comprises:

ranking the plurality of mixture components according to a degree of influence on the Gaussian mixture model;

selecting a set of mixture components that are most influential;

iteratively improving an estimation of the diagonal covariance matrix using the selected set of mixture components; and

repeating the steps of iteratively improving the estimation of the expected value for the particular sensor and iteratively improving the estimation of the diagonal covariance matrix until the two estimates achieve convergence.

13. The system of claim 12, wherein convergence is achieved when repeating the steps of iteratively improving the estimation of the expected value for the particular sensor and iteratively improving the estimation of the diagonal covariance matrix results in a negligible improvement.

14. The system of claim 11, wherein performing the expectation-maximization technique comprises:

estimating the expected value for the particular sensor; and

estimating a diagonal covariance matrix that contributes to a relationship between the received sensor data from the plurality of sensors and the expected value for the particular sensor, wherein the two estimations are performed at substantially the same time.

15. A computer system comprising:

a processor; and

a program storage device readable by the computer system, embodying a program of instructions executable by the processor to perform method steps for monitoring a system, the method comprising;

receiving a set of training data;

defining a Gaussian mixture model to model a probability distribution for a particular sensor of the system from among a plurality of sensors of the system based on the received training data, the Gaussian mixture model comprising a sum of k mixture components, wherein k is a positive integer;

receiving sensor data from the plurality of sensors of the system; and

performing an expectation-maximization technique to estimate an expected value for the particular sensor based on the defined Gaussian mixture model and the received sensor data from the plurality of sensors, wherein

each of the k mixture components is a Gaussian distribution defined by a mean and a variance that are determined during the performance of the expectation-maximization technique.

16. The computer system of claim 15, additionally comprising:

receiving an actual sensor value from the particular sensor;

comparing the received actual sensor value to the estimated expected sensor value; and

detecting a potential fault based on the comparison.

17. The computer system of claim 15, wherein performing the expectation-maximization technique comprises:

ranking the k mixture components according to a degree of influence on the Gaussian mixture model;

selecting a set of mixture components that are most influential;

iteratively improving an estimation of the expected value for the particular sensor based on a diagonal covariance matrix that contributes to a relationship between the received sensor data from the plurality of sensors and the expected value for the particular sensor, using the selected set of mixture components;

iteratively improving an estimation of the diagonal covariance matrix using the selected set of mixture components; and

repeating the steps of iteratively improving the estimation of the expected value for the particular sensor and iteratively improving the estimation of the diagonal covariance matrix until the two estimates achieve convergence.

18. The computer system of claim 17 wherein convergence is achieved when repeating the steps of iteratively improving the estimation of the expected value for the particular sensor and iteratively improving the estimation of the diagonal covariance matrix results in a negligible improvement.

19. The computer system of claim 15, wherein performing the expectation-maximization technique comprises:

estimating the expected value for the particular sensor; and

estimating a diagonal covariance matrix that contributes to a relationship between the received sensor data from the plurality of sensors and the expected value for the particular sensor, wherein the two estimations are performed at substantially the same time.

20. The computer system of claim 15, wherein the training data comprises sensor data from the plurality of sensors and sensor data from the particular sensor taken during a period of fault-free operation of the system.