The accurate diagnosis of machine breakdowns is crucial for maintaining operational safety in smart manufacturing. Despite the promise shown by deep learning in automating fault identification, the scarcity of labeled training data, particularly for equipment failure instances, poses a significant challenge. This limitation hampers the development of robust classification models. Existing methods like model-agnostic meta-learning (MAML) do not adequately address variable working conditions, affecting knowledge transfer. To address these challenges, a Related Task Aware Curriculum Meta-learning (RT-ACM) enhanced fault diagnosis framework is proposed in this paper, 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 two real-world datasets demonstrate the superiority of RT-ACM framework.

翻译：在智能制造中，机器故障的准确诊断对于维持运行安全至关重要。尽管深度学习在自动化故障识别方面展现出潜力，但标注训练数据的稀缺性，尤其是设备故障实例的稀缺，构成了重大挑战。这一限制阻碍了稳健分类模型的开发。现有方法如模型无关元学习（MAML）未能充分应对多变的工作条件，影响了知识迁移。为应对这些挑战，本文受人类认知学习过程启发，提出了一种相关任务感知课程元学习（RT-ACM）增强故障诊断框架。RT-ACM通过考虑辅助传感器工作条件的相关性来改进训练，遵循“更关注更相关知识”的原则，并采用“先易后难”的课程采样策略。该方法有助于元学习器达到更优的收敛状态。在两个真实数据集上进行的大量实验证明了RT-ACM框架的优越性。