Industrial process surveillance system

• US 5,764,509 A
• Filed: 06/19/1996
• Issued: 06/09/1998
• Est. Priority Date: 06/19/1996
• Status: Expired due to Term

• Current Assignee: ARCH Development Corporation (University of Chicago)
• Original Assignee: University of Chicago
• Inventors: Wegerich, Stephan W., Gross, Kenneth C., Mott, Jack E., Singer, Ralph M.
• Primary Examiner(s): Elmore, Reba I.
• Assistant Examiner(s): PATEL, RAMESH B
• Application Number:
  US08/666,938
• Time in Patent Office: 720 Days
• Field of Search: 364/148, 364/149, 364/164, 364/492, 364/576, 364/554, 364/600, 364/198, 395/22, 376/216, 376/217, 340/506, 340/651
• US Class Current: 700/29
• 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 23/0262 Confirmation of fault detection, e.g. extra checks to confirm that a failure has indeed occurred

Abstract

A system and method for monitoring an industrial process and/or industrial data source. The system includes generating time varying data from industrial data sources, processing the data to obtain time correlation of the data, determining the range of data, determining learned states of normal operation and using these states to generate expected values, comparing the expected values to current actual values to identify a current state of the process closest to a learned, normal state; generating a set of modeled data, and processing the modeled data to identify a data pattern and generating an alarm upon detecting a deviation from normalcy.

Claims

1. A method for monitoring at least one of an industrial process and industrial sensors, comprising the steps of:

generating time varying data from a plurality of industrial sensors;

processing the time varying data to effectuate optimum time correlation of the data accumulated fi-om the plurality of industrial sensors;

searching the time correlated data to identify maximum and minimum values for the data, thereby determining a full range of values for the data from the industrial process;

determining learned states of a normal operational condition of the industrial process and using the learned states to generate expected values of the operating industrial process;

comparing the expected values to current actual values of the industrial process to identify a current state of the industrial process closest to one of the learned states and generating a set of modeled data;

processing the modeled data to identify a pattern for the data and upon detecting a deviation from a pattern characteristic of normal operation, an alarm is generated.

2. The method as defined in claim 1 wherein the industrial process comprises a physical process.

3. The method as defined in claim 1 wherein the industrial process comprises a financial process.

4. The method as defined in claim 1 wherein the step of determining optimum time correlation comprises comparing pairs of sensor signals, each characteristic of a separate sensor and calculating a cross correlation vector over time, applying a low pass filter to remove noise from the cross correlation vector and determining phase shift between the sensor signals.

5. The method as defined in claim 4 wherein the step of determining phase shift comprises differentiating the cross correlation vector with respect to lag time between each pair of signals and performing an interpolation to compute the root of the differential of the cross correlation vector.

6. The method as defined in claim 1 wherein the step of identifying a current state of the industrial process closest to the learned state includes forming a combination of the learned states to identify a true state of the industrial process.

7. The method as defined in claim 6 further including the step of substituting an expected value for incomplete observations of the industrial process.

8. The method as defined in claim 1 wherein the step of detecting a deviation from normal operation comprises applying a sequential probability ratio test to the modeled data.

9. The method as defined in claim 1 wherein the step of detecting a deviation from normal operation comprises performing a pattern recognition analysis using computer means.

10. A method for monitoring at least one of an industrial process and an industrial data source, comprising the steps of:

generating time varying data from a plurality of industrial data sources;

determining learned states of a normal operational condition of the industrial process to use the learned states to generate expected values of the operating industrial process;

comparing the expected values to current values of the industrial process to identify a current state of the industrial process closest to one of the learned states and generating a set of modeled data;

processing the modeled data to identify a pattern for the data and upon detecting a deviation from a pattern characteristic of normal operation, an alarm is generated.

11. The method as defined in claim 10 wherein the step of identifying a current state of the industrial process closest to the learned state includes forming a combination of the learned states to identify a true state of the industrial process.

12. The method as defined in claim 10 further including the step of substituting an expected value for incomplete observations.

13. The method as defined in claim 10 wherein the industrial data source is selected from the group consisting of an industrial manufacturing process, a utility operation, a business operation, an investment process, weather forecasting and a transportation system.

14. The method as defined in claim 10 wherein the plurality of industrial data sources comprises a plurality of sensor pairs.

15. The method as defined in claim 10 wherein the step of processing the modeled data comprises applying a SPRT process.

16. The method as defined in claim 10 further including a step of determining time phase shift between the plurality of the time varying data being output.

17. A method for monitoring at least one of an industrial process and an individual date source, comprising the steps of:

sensing time varying data from at least one industrial data source of an industrial process;

determining learned states of a desired operational condition of the industrial process to use the learned states to generate expected values of the industrial process;

comparing the expected values to current sensed values of the industrial process to identify a current state of the industrial process closest to one of the learned states and generating data characteristic of the current state; and

processing the data that is characteristic of the current state to identify a pattern for the data and upon detecting a deviation from a pattern characteristic of the desired operational condition, a signal is generated indicating at least one of the industrial process and the industrial data source is not of the desired operational condition.

18. The method as defined in claim 17 further including the step of searching the time varying data, before comparing the current actual values to the expected values, to identify minimum and maximum values for the data, thereby establishing a full range of values for the data.

19. The method as defined in claim 18 wherein each said industrial data source is characterized by two data values associated with the minimum and maximum values.

20. The method as defined in claim 17 wherein said step of processing the data characteristic of the current state to identify a pattern comprises applying a sequential probability ratio test.

21. The method as defined in claim 20 wherein data that is characteristic of the current state is processed to generate a set of modeled data which is further processed to identify the pattern for the data.

22. The method as defined in claim 17 wherein the industrial process is selected from the group consisting of a manufacturing process, a physical process, a chemical process, a biological process, an electronic process and a financial process.

23. The method as defined in claim 17 further including the step of substituting an estimated signal for said industrial data source upon detecting the deviation from a pattern characteristic of the desired operational condition, thereby replacing a faulted data source enabling continued operation and monitoring of the industrial process.

24. The method as defined in claim 17 further including the step of processing the time varying data to effectuate optimum time correlation of the data.

25. The method as defined in claim 24 wherein the step of determining optimum time correlation comprise comparing pairs of sensor signals, each characteristic of a separate sensor and calculating a cross correlation vector over time, applying a low pass filter to remove noise from the cross correlation vector and determining phase shift between the sensor signals.

26. A method for monitoring at least one of an industrial process and an industrial sensor, comprising the steps of:

sensing time varying data from at least one industrial data source of an industrial process;

searching signals from the at least one industrial data source to identify maximum and minimum values for the time varying data;

determining learned states of a desired operational condition of the industrial process to use the learned states to generate expected values of the industrial process; and

processing the expected values by identifying a pattern for the time varying data and upon detecting a deviation from the desired operational condition, a signal is generated indicating at least one of the industrial process and the industrial data source is not of the desired operational condition.

27. The method as defined in claim 26 further including the step of substituting an estimated signal for said industrial data source upon detecting the deviation from a pattern and characteristic of the desired operational condition, thereby replacing a faulted data source enabling continued operation and monitoring of the industrial process.

28. The method as defined in claim 26 further including the step of processing the time varying data to effectuate optimum time correlation.

29. The method as defined in claim 26 wherein the step of identifying a pattern for the time varying data comprises applying a sequential probability ratio test.

30. The method as defined in claim 26 wherein the industrial process is selected from the group consisting of a manufacturing process, a physical process, a chemical process, a financial process, an electronic process and a biological process.