Robust Prototypical Networks for Few-Shot Sensor Fault Diagnosis

[Submitted on 10 Sep 2026]

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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.

Submission history

From: Mohammed Ayalew Belay [view email]
[v1] Thu, 10 Sep 2026 23:36:05 UTC (461 KB)