Interactive dual adversarial neural network framework: An open-set domain adaptation intelligent fault diagnosis method of rotating machinery

Abstract

The domain-adaptation technique has been proven to be able to resolve the fault diagnosis under various working conditions. Most research presumes that the health states in the source domain are consistent with the target domain. However, open-set domain adaptation problem that contains the unknown states in testing process remains unexplored. Here we propose an interactive dual adversarial neural network (IDANN) for this problem. First, a closed-set domain adversarial network is trained to obtain the weight of each target instance. Then, an open-set domain adversarial network is trained by importing the weighted unknown classification items and entropy minimization techniques. Through a series of interactive training, the IDANN can not only distinguish the unknown instances but also assign known instances to corresponding classes. Two experiment cases validate the effectiveness of the proposed IDANN method. The comparison results suggest that the proposed method can achieve superior performance in open-set domain adaptation problems.