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Journal of Intelligent Manufacturing

Erschienen in:

24.03.2022

Few-shot RUL estimation based on model-agnostic meta-learning

verfasst von: Yu Mo, Liang Li, Biqing Huang, Xiu Li

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 5/2023

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Abstract

Data-driven remaining useful life (RUL) estimation has been a research hotspot in the prognostic and health management (PHM) of industrial equipment and systems. It can achieve predictive maintenance of machinery and rarely require prior expertise in prognostics and signal processing. However, the data-driven methods require sufficient training data, which is difficult to acquire. In this paper, we employ the model-agnostic meta-learning (MAML) algorithm to seek suitable model parameter initialization that can rapidly adapt to the given test sample with few-shot training samples. We also propose to build pseudo-meta-RUL task sets for meta-learning by calculating time sequence similarities. To further improve the applicability of the model, we extend the proposed method from few-shot conditions to general conditions. We conduct experiments on the C-MAPSS dataset and the results show that the proposed algorithm can improve the prediction performance and enhance the generalization ability of the model in the context of few-shot conditions and general conditions.

Vorheriger Artikel Reconstruction of 3D surfaces from incomplete digitisations using statistical shape models for manufacturing processes

Nächster Artikel Convolutional neural networks for prediction of geometrical errors in incremental sheet metal forming

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Titel: Few-shot RUL estimation based on model-agnostic meta-learning
verfasst von: Yu Mo
Liang Li
Biqing Huang
Xiu Li
Publikationsdatum: 24.03.2022
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 5/2023
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-022-01929-w

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