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

To realize fault identification of unlabeled data and improve model generalization capability, domain adaptation technology has been increasingly applied to intelligent fault diagnosis of machinery. Nevertheless, traditional domain adaptation diagnosis models generally restrict different domains to have the same label space, which does not always hold in complex industrial scenarios. Consequently, a more practical scenario, i.e., partial-set transfer diagnosis, is explored in this article, where the target label space is a subspace of the source domain. A multiweight domain adversarial network (MWDAN) is proposed to solve this issue, in which the weighting mechanism of class-level and instance-level is jointly designed to distinguish the label space and quantify the transferability of data samples. 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 the target domain. Extensive experiments constructed on two mechanical systems demonstrate the outstanding performance of MWDAN.