Accurate remaining useful life prediction and uncertainty quantification for wind turbines using temporal convolutional variational deep Gaussian processes

Abstract It remains highly challenging to extract features from monitoring signals of wind turbine components under varying operating conditions and complex structures, as well as to quantify the uncertainty of Remaining Useful Life (RUL) predictions. To address these issues, this paper proposes a m...

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Autors principals:	Shuo Cui, Xiuli Liu, Guoxin Wu, Wan Qiao, Jianyu Liu, Xuanyu Chen
Format:	Artigo
Idioma:	Inglês
Publicat:	Nature Portfolio 2026-04-01
Col·lecció:	Scientific Reports
Matèries:	Remaining useful life prediction Interpretability Time convolutional variational deep Gaussian Process Deep Gaussian Process Wind turbine gearbox
Accés en línia:	https://doi.org/10.1038/s41598-026-47273-4
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