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Certainty Improvement in Diagnosis of Multiple Faults by Using Versatile Membership Functions for Fuzzy Neural Networks

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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Abstract

Because the relationship between frequency symptoms and fault causes are different, this study uses fuzzy neural network (FNN) with versatile membership functions to diagnose multiple faults in rotary machinery. According to the frequency symptom values for each fault causes, three kinds of membership functions are used. Besides, the structure of the FNN is large which spend much training time. Thus, when the matrix between frequency symptoms and fault causes can decoupled, the relational matrix decomposed into several sub-matrixes and the structure of the FNN can also divided into several sub-networks. In this study, two above-mention approaches are combined to diagnose multiple faults and compared with neural network (NN), FNN with single/versatile membership functions in two actual cases.

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Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Chung Yuan Christian University, Chung Li, 320, Taiwan

    Yuan Kang, Chun-Chieh Wang & Yeon-Pun Chang

  2. Power System Dept., Industrial Technology Research Institute, Hsinchu, 310, Taiwan

    Chien-Ching Hsueh

  3. Magnate Aeronautical Industry Co.,Ltd, Kaohsiung, 811, Taiwan

    Ming-Chang Chang

Authors
  1. Yuan Kang
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  2. Chun-Chieh Wang
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  3. Yeon-Pun Chang
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  4. Chien-Ching Hsueh
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  5. Ming-Chang Chang
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Kang, Y., Wang, CC., Chang, YP., Hsueh, CC., Chang, MC. (2006). Certainty Improvement in Diagnosis of Multiple Faults by Using Versatile Membership Functions for Fuzzy Neural Networks. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_55

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  • DOI: https://doi.org/10.1007/11760191_55

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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