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

  • Yuan Kang
  • Chun-Chieh Wang
  • Yeon-Pun Chang
  • Chien-Ching Hsueh
  • Ming-Chang Chang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

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.

Keywords

Membership Function Induction Motor Fuzzy Neural Network Frequency Symptom Multiple Fault 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yuan Kang
    • 1
  • Chun-Chieh Wang
    • 1
  • Yeon-Pun Chang
    • 1
  • Chien-Ching Hsueh
    • 2
  • Ming-Chang Chang
    • 3
  1. 1.Department of Mechanical EngineeringChung Yuan Christian UniversityChung LiTaiwan
  2. 2.Power System Dept.Industrial Technology Research InstituteHsinchuTaiwan
  3. 3.Magnate Aeronautical Industry Co.,LtdKaohsiungTaiwan

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