Semi-supervised fault diagnosis of rolling bearing based on CL-ICAE

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (7) : 301-311.

QI Yongsheng1,2, GONG Yurui1,2, GAO Shengli3, LIU Liqiang1,2, LI Yongting1,2

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Abstract

At present, the fault diagnosis technology of rotating machinery based on deep learning has attracted wide attention because of its powerful layer-by-layer processing and built-in feature transformation function. However, the traditional depth network for fault diagnosis requires a lot of label data, and the diagnosis results depend on the number and accuracy of labels. For this reason, a semi-supervised fault diagnosis method based on center loss-improved convolutional autoencoder is proposed. Firstly, the fault signal is converted into time-frequency graph by continuous wavelet transform to refine the fault feature representation. Then an improved convolutional autoencoder network structure is constructed, and batch normalization(BN) and Dropout are introduced to prevent over-fitting in the feature extraction stage. Then in the classification stage, the center loss is introduced into the Softmax loss function to build a joint loss function to make the fault features achieve smaller intra-class distance and greater feature differences, and further improve the classification accuracy. Finally, the proposed method is verified by Case Western Reserve University bearing data set and bearing fault experimental platform. The results show that in the case of a small number of label samples, effective fault diagnosis can be achieved and the diagnosis accuracy can be improved.

Key words

rolling bearing / convolutional autoencoder / semi-supervised / batch normalization / center loss

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QI Yongsheng1,2, GONG Yurui1,2, GAO Shengli3, LIU Liqiang1,2, LI Yongting1,2. Semi-supervised fault diagnosis of rolling bearing based on CL-ICAE[J]. Journal of Vibration and Shock, 2023, 42(7): 301-311

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