Aiming at the problem of inconsistent distribution of rolling bearing vibration signal data characteristics under strong noise and time-varying speed and the failure samples to be tested do not contain labels, a residual attention mechanism and sub-domain adaptive unsupervised transfer learning rolling bearing fault diagnosis method was proposed. Firstly, to give full play to the image classification capabilities of the convolutional neural network (CNN), the one-dimensional time-domain fault vibration signal under the time-varying speed was converted into a two-dimensional grayscale image using continuous wavelet transform (CWT), which was used as the input of the model in this article; Secondly, in order to better extract the common features of the source and target domains, the feature extractor used the residual channel attention weak sharing network model proposed in this paper, which used the cross-layer connection method of the residual network and the channel attention mechanism, and weakened the structural conditions of the traditional strong sharing network model; Thirdly, in order to match the conditional distribution difference between the source domain and the target domain, the network adaptation layer embedded the local maximum mean discrepancy (LMMD) measurement criterion; Finally, the time-varying speed rolling bearing public fault data set was used for experimental verification and analysis. The results show that the method proposed in this paper achieves an average recognition accuracy of more than 93% under strong noise and time-varying speed, which has better generalization and robustness than traditional convolutional neural network models.
Key words: Fault diagnosis；Unsupervised transfer learning； Weak sharing of residual attention； Time-varying speed； Strong noise
Key words
Fault diagnosis /
Unsupervised transfer learning /
Weak sharing of residual attention /
Time-varying speed /
Strong noise
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