Retraining Strategy-Based Domain Adaption Network for Intelligent Fault Diagnosis

Abstract

Industrial Internet of Things (IIoT) obtains big data from industrial facilities. Based on these data, health conditions for facilities can be predicted using machine learning methods, which in turn improves the trustworthiness of IIoT. Intelligent fault diagnosis is developing with this process. Since machine damages always happen under different circumstances, the designed intelligent fault diagnosis methods should have domain adaption ability. In this article, a data-driven fault diagnosis method called domain adaption network (DAN) is present, in which the difference between training and testing data can be minimized while the maximum training accuracy can be maintained. First, DAN uses a combined loss for consistent feature learning and optimum training classification performance. Then, a DAN retraining (DAN-R) strategy is employed based on weighted pseudo-labeled testing dataset. Finally, three experiments are performed on two fault diagnosis datasets. The results reveal that DAN-R is applicable and has outstanding domain adaption ability.