Surveillance system and method having an adaptive sequential probability fault detection test

• US 7,415,382 B1
• Filed: 07/24/2006
• Issued: 08/19/2008
• Est. Priority Date: 03/08/2002
• Status: Expired due to Term

• Current Assignee: Intellectual Assets LLC
• Original Assignee: Intellectual Assets LLC
• Inventors: Herzog, James P., Bickford, Randall L.
• Primary Examiner(s): Barlow, Jr.; John E.
• Assistant Examiner(s): Taylor; Victor J
• Application Number:
  US11/492,329
• Time in Patent Office: 757 Days
• Field of Search: 702 1- 6, 702178-185
• US Class Current: 702/178
• 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 18/24155 Bayesian classification

Abstract

System and method providing surveillance of an asset such as a process and/or apparatus by providing training and surveillance procedures that numerically fit a probability density function to an observed residual error signal distribution that is correlative to normal asset operation and then utilizes the fitted probability density function in a dynamic statistical hypothesis test for providing improved asset surveillance.

Claims

1. A method for performing surveillance an asset, the steps including:

acquiring residual data correlative to expected asset operation;

fitting a mathematical model to the acquired residual data and storing the mathematical model in a memory means;

collecting a current set of observed signal data values from the asset;

using the mathematical model in a sequential hypotheses test performed as a function of at least one of the observed signal data values for detecting a fault condition; and

determining if a detected fault condition is indicative of an actual presence of a fault for providing asset surveillance.

2. The method of claim 1 wherein the step of acquiring residual data includes a step of acquiring residual data correlative to at least one mode of asset operation.

3. The method of claim 2 wherein the step of acquiring residual data correlative to at least one mode of asset operation includes a step of acquiring past observations of signal data values from the asset and obtaining deviations between the past observations of signal data values and expected signal values obtained by using a model of expected asset operation.

4. The method of claim 1 wherein the step of fitting the mathematical model to the acquired residual data and storing the mathematical model in the memory means includes a step of fitting at least one mathematical function correlative to a frequency distribution to the acquired residual data.

5. The method of claim 4 wherein the step of fitting at least one mathematical function correlative to a frequency distribution to the acquired residual data includes a step of fitting at least one function defining a frequency curve to the acquired residual data.

6. The method of claim 4 wherein the step of fitting at least one mathematical function correlative to a frequency distribution to the acquired residual data includes a step of fitting at least one function defining a frequency histogram to the acquired residual data.

7. The method of claim 4 wherein the step of fitting at least one mathematical function correlative to a frequency distribution to the acquired residual data includes a step of fitting at least one function defining a frequency polygon to the acquired residual data.

8. The method of claim 1 wherein the step of using the mathematical model in a sequential hypotheses test performed as a function of at least one of the observed signal data values for detecting a fault condition includes the step of performing at least one of a group of sequential hypothesis tests comprised of a positive mean test, a negative mean test, a nominal variance test, and a inverse variance test.

9. The method of claim 1 further including a step of performing a control action when an actual presence of a fault is determined.

10. The method of claim 1 further including a step of sounding an alarm when an actual presence of a fault is determined.

11. A method for performing surveillance of an asset, the steps including:

acquiring residual data correlative to expected asset operation;

fitting a mathematical model to the acquired residual data and storing the mathematical model in a memory means;

collecting a current set of observed signal data values from the asset;

using the mathematical model in a sequential hypotheses test performed as a function of at least one of the observed signal data values for determining indices correlative to asset status;

evaluating the indices for detecting a fault condition; and

determining if a detected fault condition is indicative of an actual presence of a fault for providing asset surveillance.

12. The method of claim 11 wherein the step of acquiring residual data includes a step of acquiring residual data correlative to at least one mode of asset operation.

13. The method of claim 11 wherein the step of fitting the mathematical model to the acquired residual data and storing the mathematical model in the memory means includes a step of fitting at least one mathematical function correlative to a frequency distribution to the acquired residual data.

14. The method of claim 13 wherein the step of fitting at least one mathematical function correlative to a frequency distribution to the acquired residual data includes a step of fitting at least one function defining a frequency curve to the acquired residual data.

15. The method of claim 13 wherein the step of fitting at least one mathematical function correlative to a frequency distribution to the acquired residual data includes a step of fitting at least one function defining a frequency histogram to the acquired residual data.

16. The method of claim 13 wherein the step of fitting at least one mathematical function correlative to a frequency distribution to the acquired residual data includes a step of fitting at least one function defining a frequency polygon to the acquired residual data.

17. The method of claim 11 wherein the step of using the mathematical model in a sequential hypotheses test performed as a function of at least one of the observed signal data values for detecting a fault condition includes the step of performing at least one of a group of sequential hypothesis tests comprised of a positive mean test, a negative mean test, a nominal variance test, and a inverse variance test.

18. The method of claim 11 further including a step of performing a control action when an actual presence of a fault is determined.

19. The method of claim 11 further including a step of sounding an alarm when an actual presence of a fault is determined.

20. A method for performing surveillance of an asset, the steps including:

acquiring residual data correlative to expected asset operation;

fitting a mathematical model to the acquired residual data and storing the mathematical model in a memory means;

collecting a current set of observed signal data values from the asset;

using the mathematical model in a sequential hypotheses test performed as a function of at least one of the observed signal data values for determining a time series of indices correlative to asset status;

evaluating the time series of indices for detecting a fault condition; and

determining if a detected fault condition is indicative of an actual presence of a fault for providing asset surveillance.