Wavelet neural network based fault diagnosis of asynchronous motor

  • Authors:

  • Bo Hu;Wen-Hua Tao;Bo Cui;Yi-Tong Bai;Xu Yin

  • Affiliations:

  • School of Information & Control Engineering, Liaoning Shihua University, Fushun, Liaoning Province, China;School of Information & Control Engineering, Liaoning Shihua University, Fushun, Liaoning Province, China;School of Information & Control Engineering, Liaoning Shihua University, Fushun, Liaoning Province, China;School of Information & Control Engineering, Liaoning Shihua University, Fushun, Liaoning Province, China;School of Information & Control Engineering, Liaoning Shihua University, Fushun, Liaoning Province, China

  • Venue:
  • CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
  • Year:
  • 2009

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Abstract

According to asynchronous motor's complex fault characteristics, and the combination of wavelet tranform technique, an improved wavelet neural network for fault diagnosis of asynchronous motor is proposed in this paper. Taking Wavelet transform technique as wavelet neural network(WNN) the input vector of picking up asynchronous motor's the characteristic signal, and wavelet neural network algorithm is ptimized, The self-adaptive wavelet neural network algorithm about adjusting momentum vector alter-learning rate is proposed and given the momentum coefficient and alter-learning rate adjustment method. Through the actual testified results show that the method is effective and feasible, and has a better diagnostic accuracy, fast and generalized performances.