A new fault detection method of induction motor

  • Authors:

  • Chuanbo Wen;Yun Liang

  • Affiliations:

  • Electric Engineering School, Shanghai Dianji University, Shanghai, China;Electronic and information school, Shanghai Dianji University, Shanghai, China

  • Venue:
  • AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
  • Year:
  • 2010

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Abstract

According to the shortcoming that the Extended Kalman filter (EKF) method can only estimate the speed and rotor position of induction motors in time domain when it is used to diagnose the fault existed in induction motor. A new multi-scale default diagnosing method is developed by combining EKF and wavelet transform. By monitoring the voltages and currents of the stator, it is possible to estimate the speed and position on-line. The new filter combines the merit of EKF and wavelet, and it not only possesses the multiscale analysis capability both in time domain and frequency domain, but also has better estimation accuracy than traditional EKF. Computer simulation shows the effect of the new algorithm.