Robust Prototypical Networks for Few-Shot Sensor Fault Diagnosis

arXiv cs.AISeptember 14, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2609.12287v1 Announce Type: new 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-wa

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