鲁棒原型网络用于少样本传感器故障诊断

中文摘要 AI 辅助

工业故障诊断通常仅有少量带标签的故障样本，这使得少样本学习对传感器监测具有吸引力。标准原型网络简单有效；然而，在极低样本情况下，其类别原型可能变得不稳定，因为每个决策依赖于一个小的支持集。我们提出了多情节原型网络（MEPN），该网络聚合来自多个不相交支持情节的原型，并使用其均值作为最终类别代表，在不改变编码器架构的情况下减少原型方差。我们在DeFACTO传感器数据集上评估了MEPN，使用五类故障分类，将合成偏置、漂移、尖峰和噪声故障注入真实工业测量数据中。在100次独立运行中，MEPN在每情节单样本设置（K=1样本，聚合N_agg=10个支持情节）下达到SensorOneShotGcpn%的准确率，显著高于单情节基线。在相同的10样本支持预算下，MEPN与K=10的ProtoNet在统计上无显著差异，证实了原型积累是机制而非优越的固定预算学习。

英文摘要

Industrial fault diagnosis often operates with only a handful of labeled fault examples, making few-shot learning attractive for sensor monitoring. Standard prototypical networks are simple and effective; however, their class prototypes may become unstable in the very-low-shot regime because each decision relies on a small support set. We propose \emph{Multi-Episode Prototypical Networks} (MEPN), which aggregate prototypes from multiple disjoint support episodes and use their mean as the final class representative, reducing prototype variance without changing the encoder architecture. We evaluate MEPN on the DeFACTO sensor dataset using five-way fault classification with synthetic bias, drift, spike, and noise faults injected into real industrial measurements. Over 100 independent runs, MEPN reaches \textbf{\SensorOneShotGcpn\%} in the per-episode one-shot setting ($K\!=\!1$ shot, aggregated over $N_{\text{agg}}\!=\!10$ support episodes), substantially above single-episode baselines. Under an equal 10-sample support budget, MEPN and ProtoNet at $K\!=\!10$ are statistically indistinguishable, confirming prototype accumulation as the mechanism rather than superior fixed-budget learning.

发表机构

• Simula UiB
• NTNU（挪威科技大学）
• SINTEF Energy Research（SINTEF能源研究所）

机构由 AI 辅助整理，请以论文原文为准。