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Robust fault detection using set membership estimation and T-S fuzzy neural network

A robust fault detection method is proposed for nonlinear dynamical systems with unknown but bounded noises. The Takagi-Sugeno (T-S) fuzzy neural network is used to build a model of the nonlinear dynamical system when the system is fault-free, taking into account that it is a universal approximator. The input space is partitioned by means of a fuzzy clustering algorithm based on the input and output data of the system. Supposing that the system noise and approximation error are unknown but bounded, the consequence parameters of the T-S fuzzy neural network are determined using a linear-in-parameter set membership estimation algorithm. An interval guaranteed to contain the actual output of the fault-free system is obtained by propagating the effect of model uncertainty to the model output. An occurrence of the fault is signaled when the measured output crosses the computed interval. Simulation results show the effectiveness of the proposed method.