Curriculum learning-based domain generalization for cross-domain fault diagnosis with category shift

Intelligent fault diagnosis has witnessed significant advancements in the preceding years. Domain generalization-based methods can effectively alleviate the domain shift problem and be employ for fault diagnosis in unknown domains. Apart from the problem of domain shift, another challenge arises from the incomplete label space of each source domain due to the difficulty of data acquisition. Category shift can have a significant impact on the subsequent application of intelligent algorithms. To confront this more challenging and practical problem, we begin by formulating the setting of domain generalization with category shift. This paper proposes a Curriculum Learning-based Domain Generalization method (CLDG) to tackle with the intricate problem. The basic network consists of a feature extractor, a mixup-based reciprocal point learning classifier for tackling the category shift between the source and target domains, and a conditional domain discriminator for addressing the domain shift. In addition, we construct a curriculum learning strategy that uses the knowledge of categories with high observation degree to assist in extracting domain invariant features of lower ones, dealing with the category shift between the source domains and improving the generalization ability of the categorical information. Extensive experimental results on two datasets provide evidence for the effectiveness and superiority of the proposed algorithm in classifying known and missing classes in each source domain, as well as identifying unobserved failure modes in unknown target domains.