Technologies 2026, 14(3), 167; https://doi.org/10.3390/technologies14030167 - 6 Mar 2026
Abstract
Predictive maintenance (PdM) and predictive quality monitoring (PQM) increasingly rely on data-driven condition monitoring using vibration and related signals. However, real-world deployment often faces domain drift across machines, operating regimes, and sensing conditions, while only a few labeled target samples are available. This
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Predictive maintenance (PdM) and predictive quality monitoring (PQM) increasingly rely on data-driven condition monitoring using vibration and related signals. However, real-world deployment often faces domain drift across machines, operating regimes, and sensing conditions, while only a few labeled target samples are available. This combination of distribution shift and label scarcity creates a substantial deployment gap for models trained in a single setting. This paper proposes a physics-informed few-shot learning (PI-FSL) domain adaptation framework that is among the first to combine episodic metric learning with soft physics-consistency regularization to improve cross-domain generalization. The framework integrates CWT-based time–frequency encoding, relation-based episodic classification, physics-consistency constraints at representation and signal levels, and PSD-guided episodic sampling within a unified adaptation pipeline. We evaluated PI-FSL under explicit few-shot transfer scenarios on tool-wear and bearing-condition-monitoring datasets. On the Bosch benchmark, PI-FSL achieved an F1 = 0.960 (balanced accuracy = 0.961) for cross-machine transfer and an F1 = 0.907 (balanced accuracy = 0.901) under a combined machine-operation shift. A cross-dataset evaluation across tool-wear and multiple bearing-fault benchmarks under a unified two-way five-shot protocol further demonstrated a competitive and transferable performance. PI-FSL achieved the best average macro-F1 and a balanced accuracy, with the largest margin on PU bearing transfer (macro-F1, 0.663 vs. 0.590; balanced accuracy, 0.710 vs. 0.634). The ablation results showed that few-shot fine-tuning is the main contributor, while physics regularization provides an additional stabilizing gain under transfer. These findings support PI-FSL as a practical episodic framework for robust cross-domain condition monitoring across heterogeneous industrial datasets under realistic drift and limited labels.
Full article
(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing—Third Edition)
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