Gear fault detection analysis method based on fractional wavelet transform and back propagation neural network

  • Yanqiang Sun Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Beijing University of Technology
  • Hongfang Chen Beijing University of Technology
  • Liang Tang Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Beijing University of Technology
  • Shuang Zhang Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Beijing University of Technology
Keywords: Gear fault detection preparation, Fractional wavelet transform, Back propagation neural network.

Abstract

A gear fault detection analysis method based on Fractional Wavelet Transform (FRWT) and Back Propagation Neural Network (BPNN) is proposed. Taking the changing order as the variable, the optimal order of gear vibration signals is determined by discrete fractional Fourier transform. Under the optimal order, the fractional wavelet transform is applied to eliminate noise from gear vibration signals. In this way, useful components of vibration signals can be successfully separated from background noise. Then, a set of feature vectors obtained by calculating the characteristic parameters for the de-noised signals are used to characterize the gear vibration features. Finally, the feature vectors are divided into two groups, including training samples and test samples, which are input into the BPNN for learning and classification. Experimental results showed that this gear fault detection analysis method could well maintain the useful signal components related to gear faults and effectively extract the weak fault feature. The accuracy rate reached 96.67% in the identification of the type of gear fault.

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Published
2019-12-26
Section
Articles