[en] In this article, the monitoring of continuous processes using linear dynamic models is presented. It is outlined that dynamic extensions to conventional multivariate statistical process control (MSPC) models may lead to the inclusion of large numbers of variables in the condition monitor. To prevent this, a new dynamic monitoring scheme, based on subspace identification, is introduced, which can (i) determine a set of state variable for describing process dynamics and (ii) produce a reduced set of variables to monitor process performance. This is demonstrated by an application study to a realistic simulation of a chemical process.
Disciplines :
Chemical engineering Mechanical engineering
Author, co-author :
Kruger, Uwe; Queen's University Belfast > Intelligent Systems and Control Research Group
Treasure, Richard; University of Western Australia > Control Systems Research Group
Dimitriadis, Grigorios ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Intéractions fluide structure et aérodynamique expérimentale
Language :
English
Title :
Subspace Monitoring of Multivariate Dynamic Systems
Publication date :
July 2004
Event name :
7th Biennial ASME Conference on Engineering Systems Design and Analysis
Event organizer :
American Society of Mechanical Engineers
Event place :
Manchester, United Kingdom
Event date :
du 19 juillet 2004 au 22 juillet 2004
Audience :
International
Main work title :
Proceedings of 7th Biennial ASME Conference on Engineering Systems Design and Analysis
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Bibliography
[1 MacGregor, J. F., and Kourti, T., 1995. "Statistical process control of multivariate processes". Control Engineering Practice, 3 (3), pp. 403-414.
Wise, B. M., and Gallagher, N. B., 1996. "The process chemometrics approach to process monitoring and fault detection". Journal or Process Control, 6 (6), pp. 329-348.
Martin, E. B., and Morris, A. J., 1996. "An overview of multivariate statistical process control in continuous and batch process performance monitoring". Transactions of the Institute of Measurement and Control, 18 (1), pp. 51-60.
Kruger, U., Chen, Q., Sandoz, D. J., and McFarlane, R. C., 2001. "Extended pls approach for enhanced condition monitoring of industrial processes". AIChE Journal, 47 (9), pp. 2076-2091.
MacGregor, J. F., Marlin, T. E., Kresta, J. V., and Skagerberg, B., 1991. "Multivariate statistical methods in process analysis and control". In AIChE Symposium proceedings of the 4th International Conference on Chemical Process Control, AIChE Publication, No. P-67, pp. 79-99.
Ku, W., Storer, R. H., and Georgakis, C., 1995. "Disturbance rejection and isolation by dynamic principal component analysis". Chemometrics and Intelligent Laboratory Systems, 30, pp. 179-196.
van Overschee, P., and de Moor, B., 1996. Subspace Identification for Linear Systems. Kluwer Academic Publishers.
Wang, J., and Qin, S. J., 2002. "A new subspace identification approach based on principal component analysis". Journal of Process Control, 12 (8), pp. 841-855.
Norvalis, A., Negez, A., DeCicco, J., and Çinar, A., 2000. "Intelligent process monitoring by interfacing knowledge-based systems and multivariate statistical monitoring". Journal of Process Control, 10 (4), pp. 341-350.
Russel, E. L., Chiang, L. H., and Braatz, R. D., 2000. Data-Driven Techniques for Fault Detection and Diagnosis in Chemical Processes. Advances in Industrial Control. Springer, London.
Negiz, A., and Çinar, A., 1997. "Statistical monitoring of mulrivariable continuous processes with state-space models". AIChE Journal, 43 (8), pp. 2002-2020.
Shi, R., and MacGregor, J. F., 2000. "Modelling of dynamic systems using latent variable and subspace methods". Journal of Chemometrics, 14 (5-6), pp. 423-439.
Jackson, J. E., 1980. "Principal components and factor analysis: Part i: Principal components". Journal of Quality Control, 12 (4), pp. 201-213.
Jackson, J. E., and Mudholkar, G. S., 1979. "Control procedures for residuals associated wht principal component analysis". Technometrics, 21, pp. 341-349.
Söderström, T., and Stoica, P., 1988. System Identification. Prentice Hall.
Chou, C. T., and Verhaegen, M., 1997. "Subspace algorithms for the identification of multi-variable dynamic error-in-variable models". Automatica, 33 (10), pp. 1857-1869.
Li, W., and Qin, S. J., 2001. "Consistent dynamic pca based on errors-in-variable subspace identification". Journal of Process Control, 11 (6), pp. 661-678.
van Overschee, P., and de Moor, B., 1994. "System identification for the identification of combined deterministic-stochastic systems". Automatica, 30 (1), pp. 75-93.
Yoon, S., and MacGregor, J. F., 2000. "Statistical causal model-based approaches to fault detection and isolation". AIChE Journal, 46 (9), pp. 1813-1824.
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