A Novel Multidomain Contrastive-Coding-Based Open-Set Domain Generalization Framework for Machinery Fault Diagnosis

Domain generalization detection of fault categories in industrial equipment diagnosis is a challenging problem, as it demands a model with high generalization performance. Previous methods have primarily focused on a closed set, implying that the label spaces of the training and testing sets are identical. However, this approach is insufficient to reason about the intricate industrial dynamics. In this article, we fuse domain generalization and open-set recognition to introduce a new domain generalization fault diagnosis scenario, called open-set domain generalization. It learns from different source domains to achieve high performance on unknown target domains, where the distribution and label set can be different for each source and target domain. The problem can be more applicable to real-world industrial applications. In addition, we propose a multidomain contrastive coding (MDCC) framework to learn open-set domain generalizable representations. We conduct multidomain contrastive coding by designing a new contrastive coding task and loss to preserve domain-unique knowledge and generalize knowledge across domains simultaneously. Experimental results on two multidomain datasets demonstrate that the proposed MDCC framework outperforms prior methods in open-set domain generalization.