Cross-Domain Bearing Fault Diagnosis Method Using Hierarchical Pseudo Labels

Data-driven bearing fault diagnosis methods have become increasingly crucial for the health management of rotating machinery equipment. However, in actual industrial scenarios, the scarcity of labeled data presents a challenge. To alleviate this problem, many transfer learning methods have been proposed. Some domain adaptation methods use models trained on source domain to generate pseudo labels for target domain data, which are further employed to refine models. Domain shift issues may cause noise in the pseudo labels, thereby compromising the stability of the model. To address this issue, we propose a Hierarchical Pseudo Label Domain Adversarial Network. In this method, we divide pseudo labels into three levels and use different training approach for diverse levels of samples. Compared with the traditional threshold filtering methods that focus on high-confidence samples, our method can effectively exploit the positive information of a great quantity of medium-confidence samples and mitigate the negative impact of mislabeling. Our proposed method achieves higher prediction accuracy compared with the-state-of-the-art domain adaptation methods in harsh environments.