Although the fault diagnosis methods based on deep learning have attracted widespread attention in the academic field in recent years, such methods still face many challenges, including complex and variable working conditions, insufficient ability to extract key features, and large differences in sample data. To address these problems, a width multi-scale adversarial domain adaptation residual network with a convolutional block attention module (WMSRCIDANN) is proposed in this paper, which consists of a feature extraction network, a domain discriminant network, and a label classification network. In the feature extraction network, an improved width multi-scale residual network combined with a convolutional block attention module (WMSRC) is used as the feature extractor to achieve a weighted fusion of multi-depth features.In the domain discriminative network, the fully-connected network is replaced by a four-layer convolutional structure, which can further reduce the difference in feature distribution and improve the cross-domain invariance of deep features. In the label classification network, the classifier uses the extracted domain-invariant features to perform cross-domain fault identification. The experimental results show that WMSRCIDANN is effective in cross-domain bearing fault diagnosis.
In recent years, the deep learning-based fault diagnosis methods for rotating mechanical equipment have attracted great concern. However, because the data feature distributions present differences in applications with varying working conditions, the deep learning models cannot provide satisfactory performance of fault prediction in such scenarios. To address this problem, this paper proposes a domain adversarial-based rolling bearing fault transfer diagnosis model EMBRNDNMD. First of all, an EEMD-based time-frequency feature graph (EEMD-TFFG) construction method is proposed, and the time-frequency information of nonlinear nonstationary vibration signal is extracted; secondly, a multi-branch ResNet (MBRN) structure is designed, which is used to extract deep features representing the bearing state from EEMD-TFFG; finally, to solve the model domain adaptation transfer problem under varying working conditions, the adversarial network module and MK-MMD distribution difference evaluation method are introduced to optimize MBRN, so as to reduce the probability distribution difference between the deep features of source domain and target domain, and to improve the accuracy of EMBRNDNMD in state diagnosis of target domain. The results of experiments carried out on two bearing fault test platforms prove that EMBRNDNMD can maintain an average accuracy above 97% in fault transfer diagnosis tasks, and this method also has high stability and strong ability of scene adaptation.
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