针对工业故障诊断数据不平衡问题，提出不确定性感知的元学习框架
UBMF: Uncertainty-Aware Bayesian Meta-Learning Framework for Fault Diagnosis with Imbalanced Industrial Data
- 引入不确定性感知机制，融合数据扰动与贝叶斯元知识
- 在10个数据集上实现42.22%的平均性能提升
- 适合小样本、跨任务、未知故障场景的工业诊断应用
机械设备故障诊断涉及数据采集、特征提取和模式识别，但工业数据常呈现不平衡特性，导致显著不确定性并降低诊断可靠性。为此，本文提出不确定性感知的贝叶斯元学习框架（UBMF），集成四个模块：用于增强特征鲁棒性的数据扰动注入、用于提升迁移能力的跨任务自监督特征提取、用于实现域外鲁棒泛化的基于不确定性的样本过滤，以及用于细粒度分类的贝叶斯元知识融合。在十个开源数据集上，于多种不平衡条件下（包括跨任务、小样本、未见样本场景）的实验表明，UBMF在十组任意方式1-5样本诊断任务中平均提升42.22%。该集成框架有效提升了诊断准确率、泛化能力与适应性，为复杂工业故障诊断提供了可靠解决方案。
原文摘要 · Abstract (English)
Fault diagnosis of mechanical equipment involves data collection, feature extraction, and pattern recognition but is often hindered by the imbalanced nature of industrial data, introducing significant uncertainty and reducing diagnostic reliability. To address these challenges, this study proposes the Uncertainty-Aware Bayesian Meta-Learning Framework (UBMF), which integrates four key modules: data perturbation injection for enhancing feature robustness, cross-task self-supervised feature extraction for improving transferability, uncertainty-based sample filtering for robust out-of-domain generalization, and Bayesian meta-knowledge integration for fine-grained classification. Experimental results on ten open-source datasets under various imbalanced conditions, including cross-task, small-sample, and unseen-sample scenarios, demonstrate the superiority of UBMF, achieving an average improvement of 42.22% across ten Any-way 1-5-shot diagnostic tasks. This integrated framework effectively enhances diagnostic accuracy, generalization, and adaptability, providing a reliable solution for complex industrial fault diagnosis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。