Working condition decoupling adversarial network: A novel method for multi-target domain fault diagnosis

Abstract

In the practical application of rotating machinery, the change of working conditions can meet different manufacturing requirements. When fault diagnosis is performed on monitoring data with different working conditions, the change of data distribution will bring interference information which is highly related to working conditions and inconsistent matching problems in the process of multi-target domain transfer. In order to solve these problems, a working condition decoupling adversarial network (WCDAN) is proposed for multi-target domain fault diagnosis. Specifically, the prototype discrepancy alignment module is constructed following a weight-shared wavelet convolution feature extractor to ensure a clear prototype representation boundary. Then, the adaptive domain discriminator weight, along with the acquired multi-domain discrepancy, are utilized to decouple the working conditions. This process filters out interference information that highly associated with the source domain working conditions while preserving the inherent fault characteristics. Furthermore, the strategy of multi-domain hybrid alignment aims to minimize the disparity between different domains and solve the inconsistent matching issue. Based on two gearbox fault datasets under stable and unstable conditions, the comparative experimental results show that the WCDAN can be generalized from a single source domain to multiple target domains at the same time and achieve excellent fault diagnosis performance.