Abstract
Methods for performance monitoring and diagnosis of multivariable closed loop systems have been proposed aiming at application to model predictive control systems for industrial processes. For performance monitoring, the well-established traditional statistical process control method is empolyed. To meet the underlying premise that the observed variable is univariate and statistically independent, a temporal and spatial decorrelation procedure for process variables has been suggested. For diagnosis of control performance deterioration, a method to estimate the model-error and disturbance signal has been devised. This method enables us to identify the cause of performance deterioration among the controller, process, and disturbance. The proposed methods were evaluated through numerical examples.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Box, G. and Luceno, A., “Statistical Control by Monitoring and Feedback,” John Wiley, NY (1997).
Harris, T. J., “Assessment of Control Loop Performance,”Can. J. Chem. Engng.,67(10), 856 (1989).
Harris, T. J., Boudreau, F. and MacGregor, J. F, “Performance Assessment of Multivariable Feedback Controllers,”Automatica,32, 1505 (1996).
Huang, B., Shah, S. L. and Kwok, E. K., “On-Line Control Performance Monitoring of MIMO Processes,” Proc. ACC, 1250–1254 (1995).
Huang, B., “Multivariable Statistical Methods for Control Loop Performance Assessment,” Ph.D thesis, Univ. of Alberta, Edmonton, Canada (1997).
Kesavan, P. and Lee, J. H., “Diagnostic Tools for Multivariable Model-Based Control Systems,”Ind. Eng. Chem.,36, 2725 (1997).
Kozub, D. J. and Garcia, C. E., “Monitoring and Diagnosis of Automated Controllers in the Chemical Process Industries,” 1993 AIChE Annual Meeting, Nov. (1993).
Ljung, L., “System Identification: Theory for the User,” Prentice-Hall, NJ (1999).
Mamzic, C. L. (ed.), “Statistical Process Control,” ISA, Research Triangle Park, NC, USA (1995).
Matrikon homepage, www.matrikon.com (2003).
Overschee, P. Van and DeMoore, B., “N4SID: Subspace Algorithms for the Identification of Combined Deterministic-Stochastic Systems,”Automatica,30, 75 (1994).
Ramirez, W. F, “Computational Methods for Process Simulation,” 2nd ed., Butterworth-Heinemann, Boulder, CO (1997).
Qin, S. J., “Control Performance Monitoring - a Review and Assessment,”Comp. Chem. Engng.,23, 173 (1997).
Qin, S. J. and Badgwell, T A., “A Survey of Industrial Model Predictive Control Technology,”Control Eng. Practice,11, 733 (2003).
Stanfelj, N, Marlin, T. E. and MacGregor, J. F, “Monitoring and Diagnosing Process Control Performance: the Single-loop Case,”Ind. Eng. Chem.,32, 301 (1993).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lee, S., Yeom, S. & Lee, K.S. Methods for performance monitoring and diagnosis of multivariable model-based control systems. Korean J. Chem. Eng. 21, 575–581 (2004). https://doi.org/10.1007/BF02705490
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF02705490