LAUSR

As for some machineries in real industrial applications, it is difficult to obtain massive labeled data. Therefore, transfer learning is introduced to apply the knowledge learned from labeled datasets on the unlabeled data for the fault diagnosis of machineries. However, there are the following three challenges in real applications. First, the label space of unlabeled datasets may be unknown. Second, the labeled machinery datasets often only include a part of fault types. Third, it is difficult to apply some ideal datasets to real data. For solving such problems, a new transfer learning model namely multisource partial transfer network is proposed. First, this model is constructed by four modules, including a common module and three domain-specific modules. The common module extracts common features while the domain-specific modules are responsible for fault diagnosis and domain adaptation through capturing domain-specific features. During training, the specific modules are trained through their corresponding source domain, respectively. Then, for reducing negative effects from outlier classes, two kinds of class-level weight mechanisms are designed in optimization objectives. The distance between target domain and source domain is measured as a standard to assist the target domain to learn knowledge selectively. Afterwards, particle swarm optimization is applied to search for initial parameters for the proposed model. Finally, its performance is verified through six datasets.