×

Improved principal component monitoring using the local approach. (English) Zbl 1126.62122

Summary: This paper shows that current multivariate statistical monitoring technology may not detect incipient changes in the variable covariance structure nor changes in the geometry of the underlying variable decomposition. To overcome these deficiencies, the local approach is incorporated into the multivariate statistical monitoring framework to define two new univariate statistics for fault detection. Fault isolation is achieved by constructing a fault diagnosis chart which reveals changes in the covariance structure resulting from the presence of a fault. A theoretical analysis is presented and the proposed monitoring approach is exemplified using application studies involving recorded data from two complex industrial processes.

MSC:

62P30 Applications of statistics in engineering and industry; control charts
62H25 Factor analysis and principal components; correspondence analysis
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Anderson, T. W., An introduction to multivariate statistical analysis (1958), Wiley: Wiley New York · Zbl 0083.14601
[2] Basseville, M., On-board component fault detection and isolation using the statistical local approach, Automatica, 34, 11, 1391-1415 (1998) · Zbl 0945.93608
[3] Chen, J.; Patton, R., Robust model based fault diagnosis for dynamic systems (1999), Kluwer Academic Publishers: Kluwer Academic Publishers Dordrecht · Zbl 0920.93001
[4] Jackson, J. E., A users guide to principal components. Wiley series in probability and mathematical statistics (1991), Wiley: Wiley New York
[5] Jackson, J. E.; Mudholkar, G. S., Control procedures for residuals associated with principal component analysis, Technometrics, 21, 341-349 (1979) · Zbl 0439.62039
[6] Kano, M.; Hasebe, S.; Hashimoto, I.; Ohno, H., A new multivariate statistical process monitoring method using principal component analysis, Computers & Chemical Engineering, 25, 7-8, 1103-1113 (2001)
[7] Kourti, T., Application of latent variable methods to process control and multivariate statistical process control in industry, International Journal of Adaptive Control and Signal Processing, 19, 4, 213-246 (2005) · Zbl 1113.62147
[8] Kruger, U.; Chen, Q.; Sandoz, D. J.; McFarlane, R. C., Extended PLS approach for enhanced condition monitoring of industrial processes, AIChE Journal, 47, 9, 2076-2091 (2001)
[9] Ku, W.; Storer, R. H.; Georgakis, C., Disturbance rejection and isolation by dynamic principal component analysis, Chemometrics & Intelligent Laboratory Systems, 30, 179-196 (1995)
[10] Lieftucht, D.; Kruger, U.; Irwin, G. W.; Treasure, R. J., Fault reconstruction in linear dynamic systems using multivariate statistics, IEE Proceedings on Control Theory and Applications, 153, 4, 437-446 (2006)
[11] Li, P., Treasure, R., & Kruger, U. (2005). Dynamic principal component analysis using subspace model identification (pp. 727-736), Lecture notes in computer science. Berlin: Springer.; Li, P., Treasure, R., & Kruger, U. (2005). Dynamic principal component analysis using subspace model identification (pp. 727-736), Lecture notes in computer science. Berlin: Springer.
[12] MacGregor, J. F.; Kourti, T., Statistical process control of multivariate processes, Control Engineering Practice, 3, 3, 403-414 (1995)
[13] Negiz, A.; Çinar, A., Statistical monitoring of multivariable continuous processes with state-space models, AIChE Journal, 43, 8, 2002-2020 (1997)
[14] Nomikos, P.; MacGregor, J. F., Multivariate SPC charts for monitoring batch processes, Technometrics, 37, 1, 41-59 (1995) · Zbl 0825.62740
[15] Patton, R.; Frank, P.; Clark, R., Fault diagnosis in dynamic systems—theory and applications (1989), Prentice Hall: Prentice Hall Englewood Cliffs, NJ
[16] Russel, E. L.; Chiang, L. H.; Braatz, R. D., Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis, Chemometrics & Intelligent Laboratory Systems, 51, 1, 81-93 (2000)
[17] Simoglou, A.; Martin, E. B.; Morris, A. J., Statistical performance monitoring of dynamic multivariate processes using state space modeling, Computers & Chemical Engineering, 26, 6, 909-920 (2002)
[18] Treasure, R. J.; Kruger, U.; Cooper, J. E., Dynamic multivariate statistical process control using subspace identification, Journal of Process Control, 14, 279-292 (2004)
[19] van Overschee, P.; de Moor, B., Subspace identification for linear systems (1996), Kluwer Academic Publishers: Kluwer Academic Publishers Dordrecht · Zbl 0888.93001
[20] Wang, X.; Kruger, U.; Irwin, G. W., Process monitoring approach using fast moving window PCA, Industrial & Engineering Chemistry Research, 44, 15, 5691-5702 (2005)
[21] Wise, B. M.; Gallagher, N. B., The process chemometrics approach to process monitoring and fault detection, Journal of Process Control, 6, 6, 329-348 (1996)
[22] Zhang, Q.; Basseville, M.; Benveniste, A., Early warning of slight changes in systems and plants with application to condition based maintenance, Automatica, 30, 1, 95-114 (1994) · Zbl 0800.93239
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.