Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain. However, in engineering scenarios, achieving such high-quality label annotation is difficult and expensive. The incorrect label annotation produces two negative effects: 1) the complex decision boundary of diagnosis models lowers the generalization performance on the target domain, and 2) the distribution of target domain samples becomes misaligned with the false-labeled samples. To overcome these negative effects, this article proposes a solution called the label recovery and trajectory designable network (LRTDN). LRTDN consists of three parts. First, a residual network with dual classifiers is to learn features from cross-domain samples. Second, an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain. With the training of relabeled samples, the complexity of diagnosis model is reduced via semi-supervised learning. Third, the adaptation trajectories are designed for sample distributions across domains. This ensures that the target domain samples are only adapted with the pure-labeled samples. The LRTDN is verified by two case studies, in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines. The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.