Fault diagnosis of rolling bearings via CEPADA: a partial adversarial domain adaptation method based on contrastive error perception
In cross-condition bearing fault diagnosis, the target domain often contains only a subset of the fault classes available in the source domain. This asymmetric label-space setting makes conventional domain alignment prone to negative transfer, because source-private and unreliable samples may still participate in adaptation. To address this problem, this paper proposes a contrastive error-perception-based partial adversarial domain adaptation method, named CEPADA. Instead of relying only on class-level target predictions, CEPADA introduces an error-perception mechanism to jointly evaluate class-level transfer reliability and sample-level uncertainty. Samples with high error risk are assigned lower influence during adversarial alignment, while more reliable shared-class samples are emphasized. Meanwhile, contrastive learning is embedded into the adaptation process to make features of the same fault class more compact and features of different classes more separable. A pseudo-label refinement strategy is also used to provide additional target-domain guidance under the unlabeled setting. Experiments on the case western reserve university and JNU bearing datasets show that CEPADA achieves average accuracies of 93.17% and 86.02%, respectively, with improvements of 1.47 and 1.52 percentage points over the best-performing compared unsupervised baseline in our experiments.