Expert system for testing industrial processes and determining sensor status

• US 5,761,090 A
• Filed: 10/10/1995
• Issued: 06/02/1998
• Est. Priority Date: 10/10/1995
• Status: Expired due to Term

• Current Assignee: ARCH Development Corporation (University of Chicago)
• Original Assignee: University of Chicago
• Inventors: Gross, Kenneth C., Singer, Ralph M.
• Primary Examiner(s): Voeltz, Emanuel T.
• Assistant Examiner(s): PEESO, THOMAS R
• Application Number:
  US08/541,602
• Time in Patent Office: 966 Days
• Field of Search: 364/551.01, 364/185, 364/492, 340/635, 340/825.06, 340/517, 73/618, 73/628, 73/649, 74/DIG. 7, 395/904, 395/50, 395/67, 395/77
• US Class Current: 714/26
• CPC Class Codes: G05B 19/4184 characterised by fault tolerance, reliability of production system
• CPC Class Codes: G05B 2219/34048 Fourier transformation, analysis, fft
• CPC Class Codes: G05B 2219/37325 Multisensor integration, fusion, redundant
• CPC Class Codes: G05B 2219/37326 Automatic configuration of multisensor, adaptive, active sensing
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
• CPC Class Codes: Y10S 706/906 Process plant

Abstract

A method and system for monitoring both an industrial process and a sensor. The method and system include determining a minimum number of sensor pairs needed to test the industrial process as well as the sensor for evaluating the state of operation of both. The technique further includes generating a first and second signal characteristic of an industrial process variable. After obtaining two signals associated with one physical variable, a difference function is obtained by determining the arithmetic difference between the pair of signals over time. A frequency domain transformation is made of the difference function to obtain Fourier modes describing a composite function. A residual function is obtained by subtracting the composite function from the difference function and the residual function (free of nonwhite noise) is analyzed by a statistical probability ratio test.

Claims

1. A method of testing both an industrial process and a sensor for determining fault conditions therein, comprising the steps of:

determining automatically, using computer means, a configuration of a minimum number of sensor pairs needed to test the industrial process and the sensor for state of operation;

operating at least a first and second sensor to form at least one sensor pair to redundantly detect at least one physical variable of the industrial process to provide a first signal from said first sensor and a second signal from said second sensor, each said signal being characteristic of the one physical variable;

obtaining a difference function characteristic of the arithmetic difference pairwise between said first signal and said second signal at each of a plurality of different times of sensing the one physical variable;

obtaining a frequency domain transformation of said first difference function to procure Fourier coefficients corresponding to Fourier frequencies;

generating a composite function over time domain using the Fourier coefficients;

obtaining a residual function over time by determining the arithmetic difference between the difference function and the composite function, the residual function being substantially free of serially correlated noise;

operating on the residual function using the computer means for performing a statistical analysis technique to determine whether an alarm condition is present in at least one of the industrial process and the sensor, the residual function including white noise characteristics of an uncorrelated function of reduced skewness relative to the difference function and being input to the statistical analysis technique; and

said at least one sensor pair providing alarm information to an operator of the industrial process allowing modification of at least one of the industrial process and said at least first and second sensor when an alarm condition is detected.

2. The method described is claim 1 wherein said computer means comprises an artificial intelligence system.

3. The method as defined in claim 1 wherein the residual function comprises reduced Markov dependent noise.

4. The method as defined in claim 1 wherein the industrial process comprises at least one of a chemical process, a mechanical process and an electrical operational process.

5. The method as defined in claim 1 wherein the step of obtaining Fourier coefficients comprise iteratively determining the minimum number of Fourier harmonics able to generate the composite function.

6. The method as defined in claim 1 further including at least one of the steps of modifying the industrial process or changing the sensor responsive to the alarm condition.

7. A method of testing both an industrial process and a sensor for determining fault conditions therein, comprising the steps of:

determining, using computer means, a configuration of a minimum number of sensor pair signals needed to characterize the industrial process and the sensor state of operation;

operating at least a first sensor to detect at least one physical variable of the industrial process to provide a real signal from said first sensor;

generating an artificial signal characteristic of the one physical variable;

forming a sensor pair signal from said real signal and said artificial signal;

obtaining a difference function characteristic of the difference pairwise between said real signal and said artificial signal at each of a plurality of different times of sensing the one physical variable;

obtaining a frequency domain transformation of said difference function;

generating a composite function over a time domain;

obtaining a residual function over time by determining the difference between the frequency transformed difference function and the composite function;

operating on the residual function using the computer means for performing a statistical analysis technique to determine whether an alarm condition is present in at least one of the industrial process and the first sensor, the residual function including white noise characteristics of an uncorrelated signal of reduced skewness relative to the difference function and being input to the statistical analysis technique; and

said first sensor providing alarm information to an operator of the industrial process allowing modification of at least one of the industrial process and the first sensor when an alarm condition is detected.

8. The method as defined in claim 7 wherein the step of obtaining a frequency domain transformation comprises performing a Fourier transformation.

9. The method as defined in claim 7 wherein the steps of obtaining a composite function over time comprises performing an auto regressive moving average analysis.

10. The method as defined in claim 7 further including the step of determining a difference function for both the artificial signal and the real signal, as well as a separate pair of real signals.

11. The method as defined in claim 7 wherein the residual function comprises reduced Markov dependent noise.

12. The method as defined in claim 8 wherein the step of obtaining a frequency domain transformation comprises obtaining Fourier coefficients iteratively to determine the minimum number of Fourier harmonics able to generate the composite function.

13. A system for automatically configuring sensors for testing both an industrial process and a sensor for determining a fault condition therein, comprising:

computer means for automatically configuring a minimum number of sensor pair signals needed to characterize the industrial process and the sensor state of operation;

at least a first sensor to detect at least one physical variable of the industrial process to provide a first signal from said first sensor;

first means for generating a second sensor signal for comparison with said first signal from said first sensor;

second means for determining a difference function characteristic of the arithmetic difference pairwise between said first signal and said second signal at each of a plurality of different times of sensing the one physical variable;

third means for obtaining a residual function over time by means for determining the arithmetic difference between the difference function and the composite function, the residual function including white noise characteristics of an uncorrelated signal of reduced skewness;

fourth means for operating on the residual function, said fourth means including the computer means for executing a computer program for performing a statistical analysis technique and for determining whether an alarm condition is present in at least one of the industrial process and the sensor and with said second means, said third means, and said fourth means cooperatively providing a function comprised of said white noise characteristics of uncorrelated signal of reduced skewness relative to the difference function as an input to the statistical analysis technique; and

means for providing information allowing modification of at least one of the industrial process and the sensor when an alarm condition is detected.

14. The system as defined in claim 13 further including means for obtaining a frequency domain transformation of said difference function.

15. The system as defined in claim 13 wherein said computer means comprises an artificial intelligence system.

16. The system as defined in claim 13 wherein said means for generating a second signal comprises the computer means for executing a computer program.

17. The system as defined in claim 16 wherein the computer program includes an autoregressive moving average procedure.

18. The system as defined in claim 13 wherein the system includes at least one pair of sensors for detecting each of the physical variables.

19. The system as defined in claim 13 wherein said computer means executes a computer program including a statistical probability ratio test on the residual function.

20. The system as defined in claim 13 further including means for changing at least one of the industrial process and substituting another sensor for a defective sensor.