Mathematical Problems in Engineering
Volume 2007 (2007), Article ID 80321, 20 pages
Research Article

Fault Detection and Control of Process Systems

Vu Trieu Minh,1 Nitin Afzulpurkar,2 and W. M. Wan Muhamad3

1The Sirindhorn International Thai-German Graduate School of Engineering (TGGS), King Mongkut's Institute of Technology North Bangkok, 1518 Pibulsongkram, Bangsue, Bangkok 10800, Thailand
2Mechatronics, Industrial System Engineering Group (ISE), School of Engineering and Technology, Asian Institute of Technology, Klong Luang, Pathumthani 12120, Thailand
3Institute of Product Design and Manufacturing, Universiti Kuala Lumpur, Kuala Lumpur 50250, Malaysia

Received 8 September 2006; Revised 25 December 2006; Accepted 5 February 2007

Academic Editor: José Manoel Balthazar

Copyright © 2007 Vu Trieu Minh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper develops a stochastic hybrid model-based control system that can determine online the optimal control actions, detect faults quickly in the control process, and reconfigure the controller accordingly using interacting multiple-model (IMM) estimator and generalized predictive control (GPC) algorithm. A fault detection and control system consists of two main parts: the first is the fault detector and the second is the controller reconfiguration. This work deals with three main challenging issues: design of fault model set, estimation of stochastic hybrid multiple models, and stochastic model predictive control of hybrid multiple models. For the first issue, we propose a simple scheme for designing faults for discrete and continuous random variables. For the second issue, we consider and select a fast and reliable fault detection system applied to the stochastic hybrid system. Finally, we develop a stochastic GPC algorithm for hybrid multiple-models controller reconfiguration with soft switching signals based on weighted probabilities. Simulations for the proposed system are illustrated and analyzed.