Domain adaptation with domain specific information and feature disentanglement for bearing fault diagnosis

Abstract

Abstract Collecting bearing fault signals from several rotating machines or under varied operating conditions often results in data distribution offset. Furthermore, the newly obtained data is typically unlabelled. When intricate confounding aspects of data distribution across several domains are present, achieving desired outcomes through straightforward transfer learning techniques becomes challenging. This research presents a new framework, the domain-specific invariant adversarial network, which combines the principles of domain-invariant representation learning and feature de-entanglement to solve the challenge at hand. This framework uses domain-specific information as an auxiliary training tool and employs the data generation process to transfer labelled source domain data to the target domain. The aim of this approach is to uncover potential information components and improve the model’s ability to acknowledge patterns. The study showcases the method’s strong diagnostic capability by conducting experimental analysis on four fault datasets.