Collaborative fault diagnosis of rotating machinery via dual adversarial guided unsupervised multi-domain adaptation network

Most of the existing research on unsupervised cross-domain intelligent fault diagnosis is based on single-source domain adaptation, which fails to simultaneously utilize various source domains with enough and diverse diagnostic information in practical application scenarios. How to better extract common features from multiple domains and integrate multi-source domain knowledge for collaborative diagnosis is a main challenge. To address these problems, a dual adversarial guided unsupervised multi-domain adaptation network (DAG-MDAN) is proposed. Within the proposed framework, the edge adversarial module (EA-Module) in each set of sources-target domain adaptation sub-network is utilized to compute the source-target domain adversarial loss. And an inner adversarial module (IA-Module) is constructed to direct the extraction of common features between multi-source domains, which combined the EA-Module to form the dual adversarial training to enhance domain confusion. Besides, a multi-subnet collaborative decision module (MCD-Module) is designed to compute the confidence scores to assists the multi-subnet classifier to make better fusion decisions. The DAG-MDAN is verified by the several transfer tasks using faulty rotating machinery datasets under the different speed conditions.