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

  • Guang Yang
  • Xiaoping Wu
  • Yexin Song
  • Yinchun Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)


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.


Fault Diagnosis Back Propagation Radial Basis Function Neural Network Radial Basis Function Network Fuzzy Neural Network 
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

  • Guang Yang
    • 1
    • 2
  • Xiaoping Wu
    • 1
  • Yexin Song
    • 3
  • Yinchun Chen
    • 4
  1. 1.Department of Information SecurityNaval University of EngineeringWuhanChina
  2. 2.Department of Watercraft EngineeringZhenjiang Watercraft CollegeZhenjiangChina
  3. 3.Department of Applied MathematicsNaval University of EngineeringWuhanChina
  4. 4.Department of Control Science & TechnologyHuazhong University of Science & TechnologyWuhanChina

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