Few-shot condition diagnosis of rolling bearing using adversarial transfer network with class aggregation-guided

For the challenge of fault identification under limited labeled data in engineering applications, a novel adversarial transfer network with class aggregation-guided (ATN-CA) is proposed for few-shot condition diagnosis of bearings. The ATN-CA can focus on the discrepancy features of bearings by the proposed local discrepancy feature representation, which avoids that the features extracted by a single neural network may omit important fault information. Further, the proposed class aggregation-guided strategy uses the semantic information of signals to guide the dynamic adaptation of marginal and conditional distributions of source and target data, which shortens the distribution distance of the same category in different domains, thus completing the transfer diagnosis. By comparing with some existing methods on the artificial and real bearing fault datasets, results show the proposed method has the highest test precision and the smallest accuracy deviation in the transfer diagnosis of bearings.