Reconstruction Domain Adaptation Transfer Network for Partial Transfer Learning of Machinery Fault Diagnostics

In industrial applications of machinery fault diagnostics, transfer learning is often used to transfer the knowledge learned from the source domain including labeled data to the target domain containing unlabeled data. This method follows the assumption that the label space in the source and target domain is the same. However, the label space of the target domain may be unknown in reality. For ensuring the integrity of knowledge, labeled data with all common fault types are considered as the source domain. However, if the label space of the target domain is less than that of the source domain, the negative effect from outlier classes will influence the performance. For solving this problem, a reconstruction domain adaptation transfer network (RDATN) is proposed. RDATN mainly contains health condition recognition and domain adaptation. The former is constructed for health condition recognition, while another one contributes to extracting domain-invariant features. Additionally, for reducing negative effects from outlier classes, a class-level weight and a sample-level weight are applied in the loss function. To improve the performance, particle swarm optimization (PSO) is applied to search for initial parameters for RDATN. The superiority of the proposed model is verified through three bearing datasets in two experiments.