A New Multisource Domain Bearing Fault Diagnosis Method With Adaptive Dual-Domain Obfuscation Weighting Strategy

Abstract

In the fault diagnosis of modern industrial equipment, domain adaptation is widely utilized to address the challenge of data distribution changes. However, traditional domain adaptation method only learns single domain knowledge and experiences difficulty in coping with scenarios wherein working conditions change drastically. Accordingly, learning knowledge from multiple different source domains can effectively improve the generalization ability of the model and the reliability of the diagnosis results. Simultaneously, aligning marginal and conditional distributions can weaken data distribution differences caused by drastic changes in operating conditions, enabling more accurate knowledge transfer between source and target domains, and improving the performance of model cross-domain fault diagnosis. Furthermore, when the data comes from multiple different domains, completely aligning all source domains is arduous and impractical, and thus, selectively synthesizing different source domain migration diagnostic results is necessary. To address the aforementioned issues, this study proposes a multibranch weight-adaptive adversarial fault diagnosis method. This method uses multiple branch networks to match each source–target domain pair independently, aligns them as much as possible with two domain confusion networks, and calculates the alignment degree to assign the source domain weight coefficient. The final fault diagnosis result is obtained by synthesizing the domain classifier results that correspond to the weighted coefficient domain. Experiments verify the reliability and stability of this method under various working conditions, such as high span speed and variable speed.