Adversarial Mutual Information-Guided Single Domain Generalization Network for Intelligent Fault Diagnosis

Domain generalization-based fault diagnosis has recently emerged to address domain shift problems. Most existing methods learn domain-invariant representations from multiple source domains. However, valuable fault samples from polytropic working conditions are difficult to be collected, and it is quite common that available data are from a single working condition. Therefore, this article proposes an adversarial mutual information-guided single domain generalization network for machinery fault diagnosis. To enhance the model generalization ability, a domain generation module is designed to generate fake target domains that have significant distribution discrepancies with the source domain. Then, an iterative minmax game of mutual information between the domain generation module and task diagnosis module is implemented to learn generalized features for resisting the unknown domain shift. Extensive diagnosis experiments conducted on two mechanical rigs validated the effectiveness of the proposed method.