Multi-Weight Domain Adversarial Network for Partial-Set Transfer Diagnosis

To realize fault identification of unlabeled data and improve model generalization capability, domain adaptation has been increasingly applied to intelligent fault diagnosis of machinery. Nevertheless, traditional domain adaptation diagnosis models generally constrain different domains to have the same label space, which is not always hold in complex industrial scenarios. Consequently, a more practical scenario, i.e. partial-set transfer diagnosis (PSTD), is explored in this work, where the target label space is a subspace of source domain. A multi-weight domain adversarial network (MWDAN) is proposed to solve this issue, in which class-level and instance-level weighted mechanisms are jointly designed to quantify the transferability and importance of data example. Based on the proposed strategy, the positive transfer between shared classes is promoted while the negative effect caused by outlier classes is circumvented. As a result, MWDAN can learn discriminative representations for accurate fault diagnosis in target domain. Extensive experiments constructed on two mechanical systems demonstrate the outstanding performance of MWDAN.