Gradient-based domain-augmented meta-learning single-domain generalization for fault diagnosis under variable operating conditions

Equipment operating conditions, referred to as domains, can induce domain drift in monitoring data, affecting data-driven fault diagnosis. Researchers have explored multi-domain generalization methods to tackle this issue. However, in actual industrial scenarios, the availability of fault data may be limited to a specific condition due to the cost or feasibility constraints associated with collecting extensive monitoring data. This limitation hampers the generalization ability of these methods, posing a major challenge for robust fault diagnosis under variable operating conditions. To address this challenge, we proposed a gradient-based domain-augmented meta-learning (GDM) single-domain generalization method. We analyze the restrictions of generating fake domains and construct a domain-augmented loss by evaluating diagnostic tasks minimization, semantic consistency, and distribution diversity for fake samples. Using a gradient-based technique, fake domains are generated iteratively, providing diverse fault knowledge for improved generalization. Instead of using time-consuming ensemble methods, we develop a novel meta-learning method to train a highly efficient and generalizable model, relaxing the requirement for auxiliary datasets in existing meta-learning methods. Two case studies consistently demonstrate the effectiveness and superiority of the proposed GDM method. Our findings suggest that this study offers a promising and competitive solution for single-domain generalization in fault diagnosis within real industrial scenarios.