Electronic Science and Technology ›› 2019, Vol. 32 ›› Issue (4): 16-20.doi: 10.16180/j.cnki.issn1007-7820.2019.04.004

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Adaptive Acoustic Emission Noise Elimination and Bearing Fault Diagnosis

ZHU Wangchun,CHENG Hao,GAO Haiying   

  1. School of Electrical Engineering and Automation, Guilin University of Electronic Science and Technology, Guilin 541004, China
  • Received:2018-03-18 Online:2019-04-15 Published:2019-03-27
  • Supported by:
    Guilin University of Electronic Science and Technology Postgraduate Education Innovation Program Support Project(2017YJCX104)


When acoustic emission signals are analyzed for bearings, environmental noise causes signal energy to fluctuate, resulting in misdiagnosis of late failures. To solve the problem, a spectral subtraction method was introduced to pre-noise the acoustic emission signal to enhance the stability of signal. In view of the lack of processing performance of spectral subtraction for non-stationary signals, spectral subtraction coefficients were modified and genetic algorithm was used to optimize the spectral subtraction coefficients(m, λ) globally. Experiment showed that the adaptive spectral subtraction method could obtain better spectral subtraction coefficients, and the fluctuation of wavelet packet energy feature could be eliminated. After the SVM training for the energy features of various bearing faults, the accuracy rate could reach about 92%.

Key words: fault diagnosis, acoustic emission, spectral subtraction, genetic algorithm, wavelet packet transform, SVM

CLC Number: 

  • TP368.1