A hybrid dynamic adversarial domain adaptation network with multi-channel attention mechanism for rotating machinery unsupervised fault diagnosis under varying operating conditions

Data-driven intelligent fault diagnosis methods have been extensively researched and applied in rotating machinery. In practical application scenarios, factors such as variable operating conditions and scarcity of labeled samples in rotating machinery hinder the engineering application and promotion of diagnostic models. To address these challenges, this paper proposes an unsupervised domain adaptation network called the Multi-scale Hybrid Domain Adaptation with Attention (MHDAA). Firstly, a multi-scale convolutional module was developed to extract fault features at different scales. Secondly, a multi-channel attention mechanism was proposed to enable the convolution layers of different convolution kernels fully extract feature information. Finally, a hybrid domain adaptation was constructed to dynamically extract invariant features from both the source and target domains. The method was evaluated in multiple transfer scenarios of planetary gearboxes and bearings. Experimental results demonstrate that the proposed method can effectively utilize fault features with high correlation from multiple source domains to complete fault diagnosis with unknown data labels in the target domain. Moreover, the proposed method exhibits superior diagnostic performance.