A novel deep convolution multi-adversarial domain adaptation model for rolling bearing fault diagnosis. (15th March 2022) - Wan, Lanjun; Li, Yuanyuan; Chen, Keyu; Gong, Kun; Li, Changyun
Abstract: The traditional rolling bearing fault diagnosis methods are difficult to achieve effective cross-domain fault diagnosis. Therefore, a novel deep convolution multi-adversarial domain adaptation (DCMADA) model for rolling bearing fault diagnosis is proposed, which includes a feature extraction module, a domain adaptation module, and a fault identification module. In the feature extraction module, an improved deep residual network (ResNet) is used as the feature extractor to extract the transferable features from the raw vibration signals. In the domain adaptation module, the marginal and conditional distributions are adjusted using multi-kernel maximum mean discrepancy (MK-MMD) and multiple domain discriminators in the source and target domains, and an adaptive factor is designed to dynamically measure the relative importance of these two distributions. In the fault identification module, the classifier uses the extracted domain-invariant features to complete cross-domain fault identification. Experiments show that the model has superior transfer capability in cross-domain bearing fault diagnosis. Highlights: A novel deep convolution multi-adversarial domain adaptation model is proposed. An improved deep residual network is used to extract the transferable features. MK-MMD and multiple domain discriminators are used to adjust the joint distribution. An adaptive factor is designed to achieve better domain adaptation.