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

• URL: http://arxiv.org/abs/2506.13909v1
• Date: Mon, 16 Jun 2025 18:38:34 GMT
• Title: Few-Shot Learning for Industrial Time Series: A Comparative Analysis Using the Example of Screw-Fastening Process Monitoring
• Authors: Xinyuan Tu, Haocheng Zhang, Tao Chengxu, Zuyi Chen,
• Abstract summary: Few-shot learning has shown promise in vision but remains largely unexplored for emphindustrial time-series data.<n>We present a systematic FSL study on screw-fastening process monitoring, using a 2,300-sample multivariate torque dataset.<n>We introduce a textbflabel-aware episodic sampler that collapses multi-label sequences into multiple single-label tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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