A partial domain adaptation scheme based on weighted adversarial nets with improved CBAM for fault diagnosis of wind turbine gearbox

Most domain adaptation methods for fault diagnosis depend heavily on the precondition that the source and target domain have an identical label space, which is hard to be satisfied in industrial sites. Recently, many approaches have been developed to implement partial domain adaptation. However, most existing methods adopt classic convolutional neural network as the feature extractor, which limits the ability to learn discriminative representations from non-stationary vibration signals of wind turbine (WT) gearboxes. Moreover, the design of multiple subdomain adaptation will cause complex network structure with many source classes. To address these problems, this paper proposes a partial domain adaptation scheme based on weighted adversarial nets with improved convolutional block attention module (CBAM) for WT gearbox unsupervised fault diagnosis. In detail, a residual convolutional network combining the improved CBAM is designed to extract finer domain discriminative features for knowledge transfer. Meanwhile, a weighting mechanism based on the two-stage domain discriminator is designed to evaluate the contribution of each source sample, through which a simplified transfer network structure is constructed and the source samples unrelated to the target domain can be filtered. Furthermore, an adversarial transfer strategy is introduced to decrease the distribution discrepancy between domains, then the helpful diagnosis knowledge can be transferred. Experiments on two cases demonstrate the superiority and effectiveness of the proposed method compared with existing domain adaptation methods.