Detection, identification, and reconstruction of faulty sensors with maximized sensitivity
A new method proposed here detects, reconstructs, and identifies faulty sensors using a normal process model, which can be built from first principles or statistical methods such as partial least squares or principal component analysis. The model residual is used to detect sensor faults that demonst...
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| Published in | AIChE journal Vol. 45; no. 9; pp. 1963 - 1976 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken
Wiley Subscription Services, Inc., A Wiley Company
01.09.1999
Wiley Subscription Services American Institute of Chemical Engineers |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0001-1541 1547-5905 |
| DOI | 10.1002/aic.690450913 |
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| Abstract | A new method proposed here detects, reconstructs, and identifies faulty sensors using a normal process model, which can be built from first principles or statistical methods such as partial least squares or principal component analysis. The model residual is used to detect sensor faults that demonstrate a deviation from the normal process model. To identify which sensor is faulty, a structured residual approach with maximized sensitivity is proposed to make one residual insensitive to one subset of faults but most sensitive to other faults. The structured residuals are subject to exponentially weighted moving average filtering to reduce the effect of noise and dynamic transients. The confidence limits for these filtered structured residuals are determined using statistical inferential techniques. In addition, other indices including generalized likelihood ratio test, cumulative sum, and cumulative variance of the structured residuals are compared to identify faulty sensors. The fault magnitude is then estimated based on the model and faulty data. Four types of sensor faults, including bias, precision degradation, drifting and complete failure, are simulated to test this method. Data from an industrial boiler process are used to test its effectiveness. Both single faults and simultaneous double faults are detected and uniquely identified with the method. |
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| AbstractList | A new method proposed here detects, reconstructs, and identifies faulty sensors using a normal process model, which can be built from first principles or statistical methods such as partial least squares or principal component analysis. The model residual is used to detect sensor faults that demonstrate a deviation from the normal process model. To identify which sensor is faulty, a structured residual approach with maximized sensitivity is proposed to make one residual insensitive to one subset of faults but most sensitive to other faults. The structured residuals are subject to exponentially weighted moving average filtering to reduce the effect of noise and dynamic transients. The confidence limits for these filtered structured residuals are determined using statistical inferential techniques. In addition, other indices including generalized likelihood ratio test, cumulative sum, and cumulative variance of the structured residuals are compared to identify faulty sensors. The fault magnitude is then estimated based on the model and faulty data. Four types of sensor faults, including bias, precision degradation, drifting and complete failure, are simulated to test this method. Data from an industrial boiler process are used to test its effectiveness. Both single faults and simultaneous double faults are detected and uniquely identified with the method. |
| Author | Qin, S. Joe Li, Weihua |
| Author_xml | – sequence: 1 givenname: S. Joe surname: Qin fullname: Qin, S. Joe organization: Dept. of Chemical Engineering, University of Texas, Austin, TX 78712 – sequence: 2 givenname: Weihua surname: Li fullname: Li, Weihua organization: Dept. of Chemical Engineering, University of Texas, Austin, TX 78712 |
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| Keywords | Boiler Failure detection Fault diagnostic Measurement sensor |
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| References_xml | – reference: Basseville, M., and I. V. Nikiforov, Detection of Abrupt Changes-Theory and Applications, Prentice-Hall, Englewood Cliffs, NJ (1993). – reference: Gertler, J., W. Li, Y. Huang, and T. McAvoy, "Isolation-Enhanced Principal Component Analysis," AIChE J., 45, 323 (1999). – reference: Deckert, J. C., M. N. Desai, J. J. Deyst, and A. S. Willsky, "F-8 DFBW Sensor Failure Identification Using Analytical Redundancy," IEEE Trans. Auto. Cont., 22, 796 (1977). – reference: Qin, S. J., H. Yue, and R. Dunia, "Self-Validating Inferential Sensors with Application to Air Emission Monitoring," Ind. Eng. Chem. Res., 36, 1675 (1997). – reference: Hald, A., Statistical Theory with Engineering Applications, Wiley (1952). – reference: Narasimhan, S., and R. S. H. Mah, "Generalized Likelihood Ratios for Gross Error Identification in Dynamic Processes," AIChE J., 34, 1321 (1988). – reference: Liebman, M. J., T. F. Edgar, and L. S. 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| Snippet | A new method proposed here detects, reconstructs, and identifies faulty sensors using a normal process model, which can be built from first principles or... |
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| Title | Detection, identification, and reconstruction of faulty sensors with maximized sensitivity |
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