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Integrating fault detection and isolation with model predictive control
Author(s) -
Lennox Barry
Publication year - 2005
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.858
Subject(s) - fault detection and isolation , model predictive control , isolation (microbiology) , partial least squares regression , process (computing) , computer science , fault (geology) , model selection , piecewise , controller (irrigation) , data mining , engineering , control engineering , control (management) , artificial intelligence , machine learning , mathematics , seismology , microbiology and biotechnology , actuator , biology , geology , operating system , mathematical analysis , agronomy
This paper illustrates how the application of partial least squares (PLS) can be extended to provide an integrated solution to fault detection and isolation, inferential estimation and model predictive control. It is shown that if PLS is used to identify a dynamic model of a plant then the latent variables of the model can identify the suitability of using this model under current conditions. This functionality enables automated model switching in piecewise linear systems. A further advantage of the proposed technique is that the inner structure of the model can be used to provide fault detection and isolation capabilities. By extending the approach to control systems and integrating a dynamic model, identified using the PLS algorithm, within a model predictive controller, similar benefits, such as automatic model selection can be achieved for the control system. The proposed approach is illustrated through its application to the Tennessee Eastman challenge process. Copyright © 2004 John Wiley & Sons, Ltd.