Complementary-Label Adversarial Domain Adaptation Fault Diagnosis Network under Time-Varying Rotational Speed and Weakly-Supervised Conditions

Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems, such as relatively ideal speed conditions and sample conditions. In engineering practice, the rotational speed of the machine is often transient and time-varying, which makes the sample annotation increasingly expensive. Meanwhile, the number of samples collected from different health states is often unbalanced. To deal with the above challenges, a complementary-label (CL) adversarial domain adaptation fault diagnosis network (CLADAN) is proposed under time-varying rotational speed and weakly-supervised conditions. In the weakly supervised learning condition, machine prior information is used for sample annotation via cost-friendly complementary label learning. A diagnostic model learning strategy with discretized category probabilities is designed to avoid multi-peak distribution of prediction results. In adversarial training process, we developed virtual adversarial regularization (VAR) strategy, which further enhances the robustness of the model by adding adversarial perturbations in the target domain. Comparative experiments on two case studies validated the superior performance of the proposed method.