A Domain Generalization Method for Fault Diagnosis: Integrating Causal Learning and Distributionally Robust Optimization

Domain adaptation has been widely used in variable condition fault diagnosis of mechanical equipment, due to its ability to effectively address the degradation of model generalization performance caused by differences in data distribution. However, the success of domain adaptation methods typically depends on sufficient access to target domain data, which significantly limits their practical application scenarios. To tackle this problem, this article proposes a novel domain generalization method called integrating causal learning and distributionally robust optimization (ICLDRO). In this method, a causal learning-based encoding-decoding system is designed to generate augmented data that maintains consistent semantic information and constructs uncertainty sets by the augmented data. Distributionally robust optimization (DRO) is then executed on the uncertainty set to enhance the robust domain generalization performance of the model on unknown target domains. The effectiveness of ICLDRO is validated through experiments on one public dataset and two private datasets. The results demonstrate that ICLDRO outperforms several state-of-the-art methods across most generalization tasks.