Dynamic joint distribution alignment network for bearing fault diagnosis under variable working conditions

Abstract

An inconsistent distribution between training and testing data caused by complicated and changeable machine working conditions hinders wide applications of traditional deep learning for machine fault diagnosis. In a target domain, in which labeled samples are not available (testing data), transfer learning can adopt a relevant source domain (training data) to identify the similarity between the two domains and subsequently mitigate the negative effects of a domain shift. Previous studies on transfer learning mainly focused on decreasing the marginal distribution distance of two different domains or narrowing the conditional distribution distance even though marginal and conditional distribut..