Fault Detection for Large Scale Systems Using Dynamic Principal Components Analysis with Adaptation
Keywords:
Fault Detection, Statistical Analysis, Dynamic Principal Component Analysis, Time Series Analysis, Non-Stationary SignalsAbstract
The Dynamic Principal Component Analysis is an adequate tool for the monitoring of large scale systems based on the model of multivariate historical data under the assumption of stationarity, however, false alarms occur for non-stationary new observations during the monitoring phase. In order to reduce the false alarms rate, this paper extends the DPCA based monitoring for non-stationary data of linear dynamic systems, including an on-line means estimator to standardize new observations according to the estimated means. The effectiveness of the proposed methodology is evaluated for fault detection in a interconnected tanks system.References
R. J. Patton, P. M. Frank, R. N. Clark, Issues of Fault Diagnosis for Dynamic Systems, Springer- Verlag, 1989, London.
L. H. Chiang, E. L. Russell, R. D. Braatz, Fault Detection and Diagnosis in Industrial Systems, Advanced Textbooks in Control and Signal Processing, Springer-Verlag, 2001, London. http://dx.doi.org/10.1007/978-1-4471-0347-9
J. E. Jackson, A Users Guide to Principal Components, John Wiley, 1991, New York. http://dx.doi.org/10.1002/0471725331
J. V. Kresta, J. F. MacGregor, T. E. Marlin, Multivariate Statistical Monitoring of Process Operating Performance, The Canadian Journal of Chemical Engineering, Vol. 69, pp. 35-47, February, 1991. http://dx.doi.org/10.1002/cjce.5450690105
A. Raich, A. Çinar, Statistical Process Monitoring and Disturbance Diagnosis in Multivariable Continuous Processes, AIChE Journal, Vol. 42, No. 4, pp. 995-1009, April, 1996. http://dx.doi.org/10.1002/aic.690420412
N. D. Tracy, J. C. Young, R. L. Mason, Multivariate Control Charts for Individual Observations, Journal of Quality Technology, Vol. 24, No. 2, pp. 88-95, April, 1992.
A. Norvilas, A. Negiz, J. DeCicco, A. Çinar, Intelligent Process Monitoring by Interfacing Knowledge-based Systems and Multivariate Statistical Monitoring, Journal of Process Control, Vol. 10, No. 4, pp. 341-350, August, 2000. http://dx.doi.org/10.1016/S0959-1524(99)00057-8
D. C. Montgomery, Introduction to Statistical Quality Control, John Wiley, 2001, New York.
W. Ku, R. H. Storer, Ch. Georgakis, Disturbance Detection and Isolation by Dynamic Principal Component Analysis, Chemometrics and Intelligent Laboratory Systems, Vol. 30, No. 1, pp. 179- 196, November, 1995. http://dx.doi.org/10.1016/0169-7439(95)00076-3
N. B. Gallagher, B. M.Wise, S.W. Butler, D. D. White, G. G. Barna, Development and Benchmarking of Multivariate Statistical Process Control Tools for a Semiconductor Etch Process: Improving Robustness Through Model Updating, ADCHEM'97, Banff, Canada, pp. 78-83, 9-11 June, 1997.
W. Li, H. H. Yue, S. Valle-Cervantes, S. J. Qin, Recursive PCA for Adaptive Process Monitoring, Journal of Process Control, Vol. 10, No. 5, pp. 471-486, October, 2000. http://dx.doi.org/10.1016/S0959-1524(00)00022-6
T. Kailath, A. H. Sayed, B. Hassibi, Linear Estimation, Prentice Hall, 2000, New Jersey.
G. E. P. Box, G. M. Jenkins, G. C. Reinsei, Time Series Analysis: Forecasting and Control, Prentice Hall, 1994, New Jersey.
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