Feature decoupling integrated domain generalization network for bearing fault diagnosis under unknown operating conditions.

In real engineering scenarios, the complex and variable operating conditions of mechanical equipment lead to distributional differences between the collected fault data and the training data. This distribution difference can lead to the failure of deep learning-based diagnostic models. Extracting generalized diagnostic knowledge from the source domain in scenarios where the target domain is not visible is the key to solving this problem. To this end, in this paper, we propose a domain generalization network for diagnosing bearing faults under unknown operating conditions, i.e., Feature Decoupled Integrated Domain Generalization Network (FDIDG). First, we propose a "feature decoupling" algorithm to uncover generalized representations of fault features from multiple source domains. The algorithm aims to explore the generalized representations of fault features by shrinking the distribution of data from multiple source domains and further generalize the fault features to unknown domains to reduce the coupling between fault features and operating conditions. Second, the diagnostic accuracy of the model under unknown operating conditions is further improved by adopting a multi-expert integration strategy in the decision-making stage and utilizing domain-private features to reduce the negative impact of edge samples on diagnosis. We conducted several sets of cross-domain experiments on both public and private datasets, and the results show that FDIDG has excellent generalization capabilities.