Leveraging Auxiliary Task Relevance for Enhanced Bearing Fault Diagnosis Through Curriculum Meta-Learning

The accurate diagnosis of machine breakdowns is crucial for maintaining operational safety in smart manufacturing. Despite the promise shown by deep learning (DL) in intelligent fault diagnosis, the scarcity of labeled sensors data, particularly for equipment failure instances, poses a significant challenge. This limitation hampers the development of robust classification models. Existing methods such as model-agnostic meta-learning (MAML) do not adequately address variable working conditions, thus limiting effective knowledge transfer. To address these challenges, a related task-aware curriculum meta(RT-ACM)-learning-enhanced fault diagnosis framework is proposed in this article, inspired by human cognitive learning processes. RT-ACM improves training by considering the relevance of auxiliary sensor working conditions, adhering to the principle of “paying more attention to more relevant knowledge,” and focusing on “easier first, harder later” curriculum sampling. This approach aids the meta-learner in achieving a superior convergence state. Extensive experiments on three real-world datasets demonstrate the superiority of RT-ACM framework.