Fuzzy Logic Based Modeling in the Complex System Fault Diagnosis

Fuzzy Logic Based Modeling in the Complex System Fault Diagnosis

Miroslav Pokorný, Pavel Fojtík
DOI: 10.4018/978-1-61692-811-7.ch006
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This chapter deals with the model-based fault diagnosis approaches that exploit the fuzzy modeling approximation abilities to obtain the appropriate model of the monitored system. This technique makes use of the Takagi-Sugeno fuzzy model to describe the non-linear dynamic system by its decomposition onto number of linear submodels, so that it is possible to overcome difficulties in conventional methods for dealing with nonlinearity. A linear residual generator formed by Kalman filters which are designed for the each of the linear subsystem is then proposed to generate diagnostic signals - residuals. Since the task is formulated on a statistical basis, the generalized likelihood ratio test is chosen as a decision-making algorithm. Finally, two practical examples are presented to demonstrate the applicability of the proposed approach.
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Previously, a brief definition of the fault term has been introduced. There was posed that the fault is an undesired change in the plant or its instrumentation that leads to the inability of the system to perform the requested operations (Chen & Patton, 1999). However, there has been no mention of the types of the faults under consideration so far.

According to (Gertler, 1998) the each fault can be assigned to the one of following four categories:

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