Robust Prototypical Networks for Few-Shot Sensor Fault Diagnosis

2026-09-10

AI Summary

为解决工业故障诊断中标注样本少的问题，提出多集原型网络（MEPN），通过聚合多个支持集的原型来减少原型方差，提高分类性能。

Abstract

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.

Problem

• few-shot learning
• sensor fault diagnosis
• prototypical networks
• low-shot regime

Innovation

• Multi-Episode Prototypical Networks
• few-shot learning
• sensor fault diagnosis
• prototype variance reduction