Known classes aware and emerging unknown classes rejection based on adversarial training for open set fault diagnosis

Most domain adaptation diagnosis approaches presume that the label spaces of different domains are identical. However, novel fault states may emerge in real-world applications, and traditional closed-set approaches only rely on marginal distribution alignment, making them difficult to resolve the open-set domain adaptation issue. One typical open-set problem is that the label spaces of the source and target domains are partially overlapped. To tackle this issue, this paper proposes an approach called known classes aware and emerging unknown classes rejection (KAEUR) based on adversarial training. First, an adaptive weighted learning scheme based on the entropy is introduced to the maximum classifier discrepancy method, which aims to align the target known-type samples with each class of the source known-type samples and suppress the influence of unknown-type samples during feature alignment, thereby extracting domain-invariant features through interactive adversarial training. Second, two binary cross-entropy schemes and the entropy modules are constructed to enhance the divergence between the known and unknown types. Then, an integrated criterion is established to reject the target unknown classes. Finally, three machinery datasets are constructed to demonstrate the effectiveness and superior performance of the proposed approach.