Few-Shot Learning for Industrial Time Series: A Comparative Analysis Using the Example of Screw-Fastening Process Monitoring

TL;DR

This paper systematically evaluates few-shot learning methods for industrial time series, demonstrating that lightweight CNNs with metric learning outperform larger models in defect detection tasks with limited data.

Contribution

Introduces a label-aware episodic sampler and compares metric-based and gradient-based FSL paradigms on industrial time series, highlighting the effectiveness of lightweight CNNs with metric learning.

Findings

InceptionTime + Prototypical Network achieves 0.944 F1 score in 10-shot, 3-way classification.

Metric learning outperforms MAML across all backbones.

Lightweight CNNs with metric learning outperform larger models in scarce data scenarios.

Abstract

Few-shot learning (FSL) has shown promise in vision but remains largely unexplored for \emph{industrial} time-series data, where annotating every new defect is prohibitively expensive. We present a systematic FSL study on screw-fastening process monitoring, using a 2\,300-sample multivariate torque dataset that covers 16 uni- and multi-factorial defect types. Beyond benchmarking, we introduce a \textbf{label-aware episodic sampler} that collapses multi-label sequences into multiple single-label tasks, keeping the output dimensionality fixed while preserving combinatorial label information. Two FSL paradigms are investigated: the metric-based \emph{Prototypical Network} and the gradient-based \emph{Model-Agnostic Meta-Learning} (MAML), each paired with three backbones: 1D CNN, InceptionTime and the 341 M-parameter transformer \emph{Moment}. On 10-shot, 3-way evaluation, the InceptionTime + Prototypical Network combination achieves a \textbf{0.944 weighted F1} in the multi-class regime and \textbf{0.935} in the multi-label regime, outperforming finetuned Moment by up to 5.3\% while requiring two orders of magnitude fewer parameters and training time. Across all backbones, metric learning consistently surpasses MAML, and our label-aware sampling yields an additional 1.7\% F1 over traditional class-based sampling. These findings challenge the assumption that large foundation models are always superior: when data are scarce, lightweight CNN architectures augmented with simple metric learning not only converge faster but also generalize better. We release code, data splits and pre-trained weights to foster reproducible research and to catalyze the adoption of FSL in high-value manufacturing inspection.