基于小波包分解和卷积神经网络的滚动轴承故障诊断

楼剑阳; 南国防; 宋传冲

doi:10.12187/2021.03.010

Volume 36 Issue 3

June 2021

Article Contents

Abstract

References

LOU Jianyang, NAN Guofang and SONG Chuanchong. Fault diagnosis of rolling bearing based on wavelet packet decomposition and convolutional neural network[J]. Journal of Light Industry, 2021, 36(3): 79-87. doi: 10.12187/2021.03.010

Citation: LOU Jianyang, NAN Guofang and SONG Chuanchong. Fault diagnosis of rolling bearing based on wavelet packet decomposition and convolutional neural network[J]. Journal of Light Industry, 2021, 36(3): 79-87. doi: 10.12187/2021.03.010 shu

Fault diagnosis of rolling bearing based on wavelet packet decomposition and convolutional neural network

School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Received Date: 2020-07-26
Accepted Date: 2021-02-05

Abstract

Aiming at the problems that the rolling bearing of rotating machinery is liable to be damaged in bad working environment,and the fault diagnosis at present relies too much on manual feature extraction,a rolling bearing fault diagnosis method based on wavelet packet decomposition and convolutional neural network (WPDEC-CNN) was proposed.This method used wavelet packet decomposition to process the vibration time-domain signal to obtain wavelet coefficients that characterized the signal similarly, and then preprocessed them and input them into CNN for classification and recognition.The experimental results showed that the loss rate of WPDEC-CNN was 0.108 9, which was lower than that of the BP neural network and the CNN.The fault classification accuracy of WPDEC-CNN reached 97.3%, higher than that of the BP neural network and the CNN, which verified the effectiveness of the proposed fault diagnosis method.
- wavelet packet decomposition,
- convolutional neural network,
- fault diagnosis,
- rolling bearing
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