Author(s)

Chenxue Li, Xiaoqi Yin^*, Jiaxue Chen, Hang Yang, Li Hong

Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, China.

Rolling bearings are the most prone to failures in mechanical equipment, and the vibration signal is unstable. Therefore, according to this characteristic of the bearing, the time-frequency characteristics of the bearing vibration signal are adaptively extracted. This paper proposes a wavelet transform-based method. And the bearing fault diagnosis method of convolutional neural network realizes intelligent diagnosis. Firstly, the fault signal of the rolling bearing is converted into a wavelet time-frequency map by using wavelet transform, and it is divided into training set samples and test set samples. Secondly, the experimental samples are input into the constructed convolutional neural network model for training, and the training continues updating the network parameters; finally, the training results and accuracy are obtained by testing. The experimental results show that 100% accuracy can be obtained by inputting the wavelet time-frequency map of vibration signal into the convolution neural network model for classification. The results prove the feasibility, stability and convenience of this method in fault diagnosis.

Roller Bearing, Fault Diagnosis, Continuous Wavelet Transform, Convolution Neural Network

Li, C.X., Yin, X.Q., Chen, J.X., Yang, H. and Hong, L. (2022) Bearing Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network. Open Access Library Journal, 9, 1-14. doi: 10.4236/oalib.1108845.