Non-linear system identification and fault detection method using RBF neural networks with set membership estimation Online publication date: Sat, 27-Sep-2014
by Wei Chai; Junfei Qiao
International Journal of Modelling, Identification and Control (IJMIC), Vol. 20, No. 2, 2013
Abstract: A modelling method is proposed and applied in fault detection for non-linear dynamic systems with bounded noises. Since the radial basis function (RBF) neural network is a universal approximator, it is used to model the non-linear system when the system runs without a fault. After some input and output data of the system are obtained, the centres of the hidden nodes are chosen using clustering technology. Assuming that the system noise and approximation error are unknown but bounded, the output weights of RBF neural network model of the system are determined by a linear-in-parameter set membership estimation algorithm. An interval containing the actual output of the system running without a fault can be easily predicted based on the result of the estimation. If the measured output is out of the predicted interval, it can be determined that a fault has occurred. Simulation results show the effectiveness of the proposed method.
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