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Fault Diagnosis of Complicated Machinery System Based on Genetic Algorithm and Fuzzy RBF Neural Network

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Advances in Natural Computation (ICNC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4222))

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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.

This work is partially supported by CNSF Grant #70471031.

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Author information

Authors and Affiliations

  1. Department of Information Security, Naval University of Engineering, 430033, Wuhan, China

    Guang Yang & Xiaoping Wu

  2. Department of Watercraft Engineering, Zhenjiang Watercraft College, 212003, Zhenjiang, China

    Guang Yang

  3. Department of Applied Mathematics, Naval University of Engineering, 430033, Wuhan, China

    Yexin Song

  4. Department of Control Science & Technology, Huazhong University of Science & Technology, 430074, Wuhan, China

    Yinchun Chen

Authors
  1. Guang Yang
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  2. Xiaoping Wu
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  3. Yexin Song
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  4. Yinchun Chen
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Editor information

Editors and Affiliations

  1. Life Science Research Center, School of Electronic Engineering, Xidian University, 710071, Xi’an, Shaanxi, China

    Licheng Jiao

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  3. School of Electronic Engineering, Xidian Univ., P.O. Box, 710071, Xi’an, P.R. China

    Xinbo Gao

  4. College of Mathematics and Information Science, Hebei Normal University, 050016, Shijiazhuang, Hebei, P.R. China

    Jing Liu

  5. Multi-Agent Systems Lab,Department of Computer Science, University of Science and Technology of China, 230026, Hefei, China

    Feng Wu

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© 2006 Springer-Verlag Berlin Heidelberg

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Yang, G., Wu, X., Song, Y., Chen, Y. (2006). Fault Diagnosis of Complicated Machinery System Based on Genetic Algorithm and Fuzzy RBF Neural Network. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_116

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  • DOI: https://doi.org/10.1007/11881223_116

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45907-1

  • Online ISBN: 978-3-540-45909-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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