Application of wavelet neural network in the fault diagnosis of turbine generator unit

  • Authors:

  • Chunmei Xu;Hao Zhang;Daogang Peng;Yuliang Qian

  • Affiliations:

  • School of Electronic and Information, Tongji University, Shanghai, China,School of Electric Power and Automation Engineering, Shanghai University of Electric Power, Shanghai, China;School of Electronic and Information, Tongji University, Shanghai, China,School of Electric Power and Automation Engineering, Shanghai University of Electric Power, Shanghai, China;School of Electric Power and Automation Engineering, Shanghai University of Electric Power, Shanghai, China;School of Electronic and Information, Tongji University, Shanghai, China

  • Venue:
  • AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
  • Year:
  • 2012

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Abstract

Wavelet neural network(WNN) is a type of feedforward network which is designed by using wavelet function as the activation functions in neural networks. Based on the technique of WNN, a diagnostic method is presented for turbine generator unit. The simulation results show that the proposed method can effectively diagnose the vibration fault of turbine generator, can overcome the random noise disturbance and has good application prospects.