Vibration-based multi-class fault severity classification of wind turbine gearboxes using explainable machine learning

This study presents a comprehensive fault severity classification framework for wind turbine (WT) gearboxes using vibration data from the National Renewable Energy Laboratory. The objective is to identify the extent of damage, categorized as early, moderate, or severe, based on analysis of vibration...

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Auteurs principaux:	All Mumtahina Arika, A.S. Nazmul Huda, Shameem Ahmad, Amirul Islam
Format:	Artigo
Langue:	Inglês
Publié:	Elsevier 2026-05-01
Collection:	Energy Conversion and Management: X
Sujets:	Wind Turbine Gearbox Damage Severity Classification eXplanable AI (XAI) SHapley Additive exPlanations (SHAP)
Accès en ligne:	http://www.sciencedirect.com/science/article/pii/S2590174526001042
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