Fuzzy Neural Classifier for Transformer Fault Diagnosis Based on EM Learning

  • Hongsheng Su
  • Qunzhan Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4114)


A novel fuzzy neural classifier and learning algorithm are proposed based on EM learning in this paper. The method firstly applies rough set of its information measurement ability to evaluate system parameters importance. Then, based on EM learning the unknown parameters of fuzzy member functions are estimated. Then a fuzzy neural classifier based on EM algorithm is generated. The research indicates that the proposed network possesses higher diagnosis precision and speed as well as excellent anti-interference abilities, and is an ideal pattern classifier. In the end, a practical application in transformer fault diagnosis shows the availability of the method.


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  1. 1.
    Yu, H.J.: Intelligent Diagnosis Based on Neural Networks, 1st edn. Metallurgy Industry Publication House, BJ (2000)Google Scholar
  2. 2.
    Su, H.S., Li, Q.Z.: Substation Fault Diagnosis Method Based on Rough Set Theory and Neural Network Model. Power System Technology 16, 66–70 (2005)Google Scholar
  3. 3.
    Zhang, Y.: An Artificial New Network Approach to Transformer Fault Diagnosis. IEEE Trans. on Power Delivery 11, 1836–1841 (1996)CrossRefGoogle Scholar
  4. 4.
    Sunan, H., Wei, R.: Use of Neural Fuzzy Networks with Mixed Genetic/Gradient Algorithm in Automated Vehicle Control. IEEE Trans. on Industrial Electronics 11, 1090–1102 (1999)CrossRefGoogle Scholar
  5. 5.
    Yang, J., Fen, Z.S., Huang, K.L. (eds.): Intelligent Fault Diagnosis Technology for Equipment, 1st edn. National Defence Industry Press, BJ (2004)Google Scholar
  6. 6.
    Hoskins, J.C., Kaliyur, K.M., Himmelblau, D.M.: Fault Diagnosis in Complex Chemical Plants Using Artificial Neural Networks. AIChE Journal 11, 137–141 (1991)CrossRefGoogle Scholar
  7. 7.
    Li, H.Q., Wan, B.W.: Application of Modular Wavelet Neural Networks in Industries Product Quality Control. Control and Decision 19, 195–298 (2004)MathSciNetGoogle Scholar
  8. 8.
    Narendra, K.S., Mukhopadhyay, S.: Adaptive Control of Nonlinear Multivariable Systems Using Neural Networks. Neural Networks 7, 737–742 (1994)zbMATHCrossRefGoogle Scholar
  9. 9.
    Hunt, K.J., Sbarbaro, D., Zbkowski, R., Gawthrop, P.J.: Neural Networks for Control Systems – ASurvey. Automatica 28, 1083–1112 (1992)zbMATHCrossRefGoogle Scholar
  10. 10.
    Zeng, H.: Intelligent Calculating, 1st edn. Chongqing University Press, Chongqing (2004)Google Scholar
  11. 11.
    Dempster, A.P., Larid, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society, Series B 39, 1–38 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hongsheng Su
    • 1
  • Qunzhan Li
    • 1
  1. 1.School of Electrical EngineeringSouthwest Jiaotong UniversityChengduChina

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