通过相关性筛选辅助任务，让模型先学简单再攻难点，提升故障诊断准确率。
Leveraging Auxiliary Task Relevance for Enhanced Bearing Fault Diagnosis through Curriculum Meta-learning
- 根据辅助传感器相关性动态选择训练任务，模拟人类循序渐进学习。
- 在两个真实数据集上，故障诊断准确率显著优于传统元学习方法。
- 特别适合标签稀缺、工况多变的工业设备故障诊断场景。
机器故障的精准诊断对智能制造中的运行安全至关重要。尽管深度学习在自动化故障识别方面展现出潜力，但标注数据稀缺，尤其是设备故障样本不足，严重制约了鲁棒分类模型的构建。现有方法如模型无关元学习（MAML）未能有效应对工况变化，影响知识迁移效果。为此，本文提出一种基于相关任务感知的课程元学习（RT-ACM）框架，借鉴人类认知学习过程。该方法通过评估辅助传感器工作条件的相关性，遵循“关注更相关知识”的原则，并采用“由易到难”的课程采样策略，帮助元学习器更快收敛至更优状态。在两个真实世界数据集上的大量实验表明，所提框架具有明显优势。
原文摘要 · Abstract (English)
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.
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