Fuzzy Logic Based Modeling in the Complex System Fault Diagnosis

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.