Weighted Multiple Source-Free Domain Adaptation Ensemble Network in Intelligent Machinery Fault Diagnosis

In recent years, the emergence of domain-adaptation algorithms has addressed issues related to data distribution shifts between source and target domains in the field of fault diagnosis. Most of these methods assume that data samples from the source domain are accessible for model training. However, in practical machine monitoring scenarios, obtaining direct access to source domain samples is often unfeasible, posing significant challenges for traditional domain adaptation methods. Consequently, the introduction of Source-free Domain Adaptation has proposed a method for fault diagnosis scenarios, utilizing only the source domain model, rather than source domain data samples, to achieve data distribution alignment. Building upon this, we consider a more realistic scenario involving multiple source domain models simultaneously employed for training the target model. Thus, we propose a new method for machine fault diagnosis in the target domain, comprising a multi-source weighted integrating module and an ensemble model adaptation module. Our experiments on the CWRU and PADERBORN datasets demonstrate the exceptional performance of our proposed method, even in the absence of labeled source domain samples.