ROBUST DISTANCE MEASURES FOR ON-LINE MONITORING

• US 20080183425A1
• Filed: 12/14/2007
• Published: 07/31/2008
• Est. Priority Date: 12/15/2006
• Status: Active Grant

• Current Assignee: SmartSignal Corporation (Emerson Electric Company)
• Original Assignee: Smart Signal Corporation
• Inventors: Hines, J. Wesley
• Granted Patent: US 8,311,774 B2
• Time in Patent Office: Days
• US Class Current: 702/182
• CPC Class Codes: G05B 17/02 electric
• 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: G06F 11/30 Monitoring
• CPC Class Codes: G21C 17/00 Monitoring; Testing measuring in general G01; Maintaining
• CPC Class Codes: G21D 3/001 Computer implemented control
• CPC Class Codes: Y02E 30/00 Energy generation of nuclear origin
• CPC Class Codes: Y02E 30/30 Nuclear fission reactors

Abstract

An apparatus and associated method are utilized for monitoring an operation of a system characterized by operational parameters. A non-parametric empirical model generates estimates of parameter values in response to receiving a query vector of monitored parameters for a model characterizing the system. A distance estimation engine (a) determines robust distances between the query vector and each of a set of predetermined historical vectors for the non-parametric empirical model based on an implementation of an elemental kernel function; (b) determines weights for the monitored parameters based on the robust distances; and (c) combining the weights with the predetermined historical vectors to make predictions for the system.

Claims

1. A method for making predictions based on a non-parametric empirical model used in monitoring a system, the method comprising:

receiving a query vector of monitored parameters for the non-parametric empirical model characterizing the system;

determining robust distances between the query vector and each of a set predetermined historical vectors for the non-parametric empirical model based on an implementation of an elemental kernel function;

determining weights for the monitored parameters based on the robust distances;

combining the weights with the predetermined historical vectors to make predictions for the system.

2. The method of claim 1, wherein the query vector comprises readings from multiple sensors monitoring the system.

3. The method of claim 1, wherein the non-parametric empirical model is based on an autoassociative kernel regression model.

4. The method of claim 1, wherein the non-parametric empirical model is based on a similarity based model.

5. The method of claim 1, wherein the elemental kernel function is Euclidian distance.

6. The method of claim 1, wherein the elemental kernel function is a city block distance.

7. The method of claim 1, wherein the determining robust distances comprises removing a largest elemental contributor to the elemental kernel function.

8. The method of claim 1, wherein the determining robust distances comprises removing at least one of a set of largest elemental contributors to the elemental kernel function based on a threshold distance assigned for each element.

9. An apparatus for monitoring an operation of a system characterized by operational parameters, comprising:

a non-parametric empirical model for generating estimates of parameter values in response to receiving a query vector of monitored parameters for a model characterizing the system;

a distance estimation engine for

determining robust distances between the query vector and each of a set of predetermined historical vectors for the non-parametric empirical model based on an implementation of an elemental kernel function;

determining weights for the monitored parameters based on the robust distances; and

combining the weights with the predetermined historical vectors to make predictions for the system.

10. The apparatus of claim 9, further comprising multiple sensors for monitoring the system, wherein the query vector comprises readings from the multiple sensors.

11. The apparatus of claim 9, wherein the non-parametric empirical model is based on an autoassociative kernel regression model.

12. The apparatus of claim 9, wherein the non-parametric empirical model is based on a similarity based model.

13. The apparatus of claim 9, wherein the elemental kernel function is Euclidian distance.

14. The apparatus of claim 9, wherein the elemental kernel function is a city block distance.

15. The method of claim 9, wherein the distance estimation engine determines the robust distances at least partially by removing a largest elemental contributor to the elemental kernel function.

16. The apparatus of claim 9, wherein the distance estimation engine determines the robust distances at least partially by removing at least one of a set of largest elemental contributors to the elemental kernel function based on a threshold distance assigned for each element.