Fault diagnosis of complicated machinery system based on genetic algorithm and fuzzy RBF neural network

Authors:
Guang Yang;Xiaoping Wu;Yexin Song;Yinchun Chen
Affiliations:
Department of Information Security, Naval University of Engineering, Wuhan, China;Department of Information Security, Naval University of Engineering, Wuhan, China;Department of Applied Mathematics, Naval University of Engineering, Wuhan, China;Department of Control Science & Technology, Huazhong University of Science & Technology, Wuhan, China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Year:
2006

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Abstract

Compared with traditional Back Propagation (BP) neural network, the advantages of fuzzy neural network in fault diagnosis are analyzed. A new diagnosis method based on genetic algorithm (GA) and fuzzy Radial Basis Function (RBF) neural network is presented for complicated machinery system. Fuzzy membership functions are obtained by using RBF neural network, and then genetic algorithm is applied to train fuzzy RBF neural network. The trained fuzzy RBF neural network is used for fault diagnosis of ship main power system. Diagnostic results indicate that the method is of good generalization performance and expansibility. It can significantly improve the diagnostic precision.