Fault detection model-based controller for process systems

This paper develops a model‐based control system for fault detection and controller reconfiguration using stochastic model predictive control (MPC). The system can determine online the optimal control actions, detect faults quickly, and reconfigure the controller accordingly. Such a system can perfo...

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Published inAsian journal of control Vol. 13; no. 3; pp. 382 - 397
Main Authors Minh, Vu Trieu, Afzulpurkar, Nitin, Muhamad, W. M. Wan
Format Journal Article
LanguageEnglish
Published BP, Asia (Melbourne) John Wiley and Sons Asia Pte Ltd 01.05.2011
Wiley Subscription Services, Inc
Subjects
controller reconfigura-tion
Fault detection model based controller
fault model set design
generalized predictive control
interacting multiple models estimation
Online AccessGet full text
ISSN1561-8625
1934-6093
DOI10.1002/asjc.346

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Abstract This paper develops a model‐based control system for fault detection and controller reconfiguration using stochastic model predictive control (MPC). The system can determine online the optimal control actions, detect faults quickly, and reconfigure the controller accordingly. Such a system can perform its function correctly in the presence of internal faults. A fault detection model based (FDMB) controller consists of two main parts: the first is fault detection and diagnosis (FDD) and the second is controller reconfiguration (CR). Systems subject to such abrupt failures are modeled as stochastic hybrid systems with variable‐structure. This paper deals with three challenging issues: design of the fault‐model set; estimation of hybrid multiple models; and stochastic MPC of hybrid multiple models. For the first issue, we propose a simple scheme for designing a fault model set based on random variables. For the second issue, we consider and select a fast and reliable FDD system applied to the above model set. Finally, we develop a stochastic MPC scheme for multiple model CR with soft switching signals based on the weighted probabilities of the outputs of different models. Simulations for the proposed FDMB controller are illustrated and analyzed. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society
AbstractList This paper develops a model‐based control system for fault detection and controller reconfiguration using stochastic model predictive control (MPC). The system can determine online the optimal control actions, detect faults quickly, and reconfigure the controller accordingly. Such a system can perform its function correctly in the presence of internal faults. A fault detection model based (FDMB) controller consists of two main parts: the first is fault detection and diagnosis (FDD) and the second is controller reconfiguration (CR). Systems subject to such abrupt failures are modeled as stochastic hybrid systems with variable‐structure. This paper deals with three challenging issues: design of the fault‐model set; estimation of hybrid multiple models; and stochastic MPC of hybrid multiple models. For the first issue, we propose a simple scheme for designing a fault model set based on random variables. For the second issue, we consider and select a fast and reliable FDD system applied to the above model set. Finally, we develop a stochastic MPC scheme for multiple model CR with soft switching signals based on the weighted probabilities of the outputs of different models. Simulations for the proposed FDMB controller are illustrated and analyzed. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society
This paper develops a model-based control system for fault detection and controller reconfiguration using stochastic model predictive control (MPC). The system can determine online the optimal control actions, detect faults quickly, and reconfigure the controller accordingly. Such a system can perform its function correctly in the presence of internal faults. A fault detection model based (FDMB) controller consists of two main parts: the first is fault detection and diagnosis (FDD) and the second is controller reconfiguration (CR). Systems subject to such abrupt failures are modeled as stochastic hybrid systems with variable-structure. This paper deals with three challenging issues: design of the fault-model set; estimation of hybrid multiple models; and stochastic MPC of hybrid multiple models. For the first issue, we propose a simple scheme for designing a fault model set based on random variables. For the second issue, we consider and select a fast and reliable FDD system applied to the above model set. Finally, we develop a stochastic MPC scheme for multiple model CR with soft switching signals based on the weighted probabilities of the outputs of different models. Simulations for the proposed FDMB controller are illustrated and analyzed. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [PUBLICATION ABSTRACT]
Author Muhamad, W. M. Wan
Minh, Vu Trieu
Afzulpurkar, Nitin
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References_xml – reference: Gertler, J., "Survey of model-based failure detection and isolation in complex plants," IEEE Control Syst. Mag., Vol. 8, No. 6, pp. 3-11 (1988).
– reference: Bahgwat, A., R. Srinivasan, and P. Krishnaswamy, "Fault detection during process transitions: a model-based approach," Chem. Eng. Sci., Vol. 58, No. 2, pp. 309-325 (2003).
– reference: Kinnaert, M., "Adaptive generalized predictive control for MIMO systems," Int. J. Control., Vol. 50, No. 1, pp. 161-172 (1989).
– reference: Jaffer, A. and S. Gupta, "On estimation of discrete process under multiplicative and additive noise conditions," Inf. Sci., Vol. 3, pp. 267-276 (1971).
– reference: Brown, R. G., Introduction to Random Signals and Kalman Filtering, Wiley, New York, NY (1983).
– reference: Clarke, D., C. Mohtadi, and P. Tuffs, "Generalized predictive control: I. the basic algorithm," Automatica, Vol. 23, No. 2, pp. 137-147 (1987).
– reference: Nael, H., G. Adiwinata, and D. Panagiotis, "Fault-tolerant control of process systems using communication networks," AIChE J., Vol. 51, pp. 1665-1682 (2005).
– reference: Isermann, R., "Process fault detection based on modeling and estimation method-a survey," Automatica, Vol. 20, No. 4, pp. 387-404 (1984).
– reference: Bemporad, A., W. Heemels, and B. Schutter, "On hybrid systems and closed-loop MPC systems," IEEE Trans. Autom. Control, Vol. 47, No. 5, pp. 863-869 (2002).
– reference: Li, X. R. and Y. B. Shalom, "Multile-model estimation with variable structure," IEEE Trans. Autom. Control, Vol. 41, No. 4, pp. 478-493 (1996).
– reference: Bassevill, M., "Detecting changes in signals and systems-a survey," Automatica, Vol. 24, No. 3, pp. 309-326 (1988).
– reference: Ackerson, G. and K. Fu, "On state estimation in switching environments," IEEE Trans. Autom. Control, Vol. 15, No. 1, pp. 10-17 (1970).
– reference: Cristian, F., "Understanding fault tolerant distri-buted system," Commun. ACM, Vol. 34, pp. 56-78 (1991).
– reference: Zhang, Y. and R. Li, "Detection and diagnosis of sensor and actuator failures using IMM estimator," IEEE Trans. Aerosp. Electron. Syst., Vol. 34, No. 4, pp. 321-325 (1998).
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Snippet This paper develops a model‐based control system for fault detection and controller reconfiguration using stochastic model predictive control (MPC). The system...
This paper develops a model-based control system for fault detection and controller reconfiguration using stochastic model predictive control (MPC). The system...
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SubjectTerms controller reconfigura-tion
Fault detection model based controller
fault model set design
generalized predictive control
interacting multiple models estimation
Title Fault detection model-based controller for process systems
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Volume 13
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