Application of RBF and SOFM neural networks on vibration fault diagnosis for aero-engines

Authors:
Kai Li;Dongxiang Jiang;Kai Xiong;Yongshan Ding
Affiliations:
Department of Thermal Engineering, Tsinghua University, Beijing, China;Department of Thermal Engineering, Tsinghua University, Beijing, China;Department of Thermal Engineering, Tsinghua University, Beijing, China;Department of Thermal Engineering, Tsinghua University, Beijing, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

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Abstract

This paper applies two ANN methods–RBF and SOFM on fault diagnosis for two-shaft aero-engines. Two-shaft aero-engines are complex rotating machines which have many components and high rotating speed. First we presented both the principles and advantages of RBF and SOFM neural networks. Second we described the fundamentals of two-shaft aero-engines vibration fault diagnosis, and then obtained the standard fault samples (training samples) and simulation samples (testing samples). Third we applied the two ANN methods to perform diagnosing. The accurate diagnostic results have proved the effectiveness of the RBF and SOFM methods for vibration fault diagnosis of two-shaft aero-engines. Finally, the relative advantages and disadvantages of the two ANN methods are contrasted, and suggestions can be obtained on when one might use one of the two methods.