An Auxiliary Branch Semisupervised Domain Generalization Network for Unseen Working Conditions Bearing Fault Diagnosis

Abstract

Deep learning-based methods have made remarkable achievements in rolling bearing fault diagnosis in recent years. Nevertheless, due to the diversity and complexity of rolling bearing working conditions, how to generalize deep learning models trained under limited conditions to unseen working conditions has become a popular issue in current research. The existing methods based on domain generalization usually need to train the model using a significant quantity of labeled data under multiple known working conditions to enhance its generalization capabilities under unseen working conditions. However, the acquisition of adequately labeled samples is a time-consuming and laborious task. Therefore, this article proposes an auxiliary branching semi-supervised domain generalization network (ABSDGN). ABSDGN employs a joint learning strategy of the auxiliary and main branches. The auxiliary branch generates high-confidence pseudolabels for the unlabeled source-domain data using the labeled source-domain data. The main branch leverages both real and pseudolabels to learn domain-invariant knowledge. Meanwhile, a quadratic neuron-based convolution (QConv) is introduced to take advantage of its powerful nonlinear and higher order feature representation capabilities in enhancing the performance and generalization of the model in complicated situations. In addition, a weight decomposition method based on domain labels is proposed to decompose the main branch classifier into a generic feature and feature-specific classifier to better explore the universal knowledge among different domains. The experimental results show that the proposed model has better diagnostic accuracy and stability than the most advanced semisupervised domain generalization method on two-bearing datasets.