Review and Perspectives of Machine Learning Methods for Wind Turbine Fault Diagnosis

Wind turbines (WTs) generally comprise several complex and interconnected systems, such as hub, converter, gearbox, generator, yaw system, pitch system, hydraulic system control system,integration control system, and auxiliary system. Moreover, fault diagnosis plays an important role in ensuring WT...

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Autores principales:	Mingzhu Tang, Qi Zhao, Huawei Wu, Ziming Wang, Caihua Meng, Yifan Wang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	wind turbines fault diagnosis supervised learning unsupervised learning semi-supervised learning General Works A
Acceso en línea:	https://doaj.org/article/9f3284d42be74356b689ccb22fa575ce
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spelling	oai:doaj.org-article:9f3284d42be74356b689ccb22fa575ce2021-11-15T09:08:42ZReview and Perspectives of Machine Learning Methods for Wind Turbine Fault Diagnosis2296-598X10.3389/fenrg.2021.751066https://doaj.org/article/9f3284d42be74356b689ccb22fa575ce2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fenrg.2021.751066/fullhttps://doaj.org/toc/2296-598XWind turbines (WTs) generally comprise several complex and interconnected systems, such as hub, converter, gearbox, generator, yaw system, pitch system, hydraulic system control system,integration control system, and auxiliary system. Moreover, fault diagnosis plays an important role in ensuring WT safety. In the past decades, machine learning (ML) has showed a powerful capability in fault detection and diagnosis of WTs, thereby remarkably reducing equipment downtime and minimizing financial losses. This study provides a comprehensive review of recent studies on ML methods and techniques for WT fault diagnosis. These studies are classified as supervised, unsupervised, and semi-supervised learning methods. Existing state-of-the-art methods are analyzed and characteristics are discussed. Perspectives on challenges and further directions are also provided.Mingzhu TangQi ZhaoHuawei WuZiming WangCaihua MengYifan WangFrontiers Media S.A.articlewind turbinesfault diagnosissupervised learningunsupervised learningsemi-supervised learningGeneral WorksAENFrontiers in Energy Research, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	wind turbines fault diagnosis supervised learning unsupervised learning semi-supervised learning General Works A
spellingShingle	wind turbines fault diagnosis supervised learning unsupervised learning semi-supervised learning General Works A Mingzhu Tang Qi Zhao Huawei Wu Ziming Wang Caihua Meng Yifan Wang Review and Perspectives of Machine Learning Methods for Wind Turbine Fault Diagnosis
description	Wind turbines (WTs) generally comprise several complex and interconnected systems, such as hub, converter, gearbox, generator, yaw system, pitch system, hydraulic system control system,integration control system, and auxiliary system. Moreover, fault diagnosis plays an important role in ensuring WT safety. In the past decades, machine learning (ML) has showed a powerful capability in fault detection and diagnosis of WTs, thereby remarkably reducing equipment downtime and minimizing financial losses. This study provides a comprehensive review of recent studies on ML methods and techniques for WT fault diagnosis. These studies are classified as supervised, unsupervised, and semi-supervised learning methods. Existing state-of-the-art methods are analyzed and characteristics are discussed. Perspectives on challenges and further directions are also provided.
format	article
author	Mingzhu Tang Qi Zhao Huawei Wu Ziming Wang Caihua Meng Yifan Wang
author_facet	Mingzhu Tang Qi Zhao Huawei Wu Ziming Wang Caihua Meng Yifan Wang
author_sort	Mingzhu Tang
title	Review and Perspectives of Machine Learning Methods for Wind Turbine Fault Diagnosis
title_short	Review and Perspectives of Machine Learning Methods for Wind Turbine Fault Diagnosis
title_full	Review and Perspectives of Machine Learning Methods for Wind Turbine Fault Diagnosis
title_fullStr	Review and Perspectives of Machine Learning Methods for Wind Turbine Fault Diagnosis
title_full_unstemmed	Review and Perspectives of Machine Learning Methods for Wind Turbine Fault Diagnosis
title_sort	review and perspectives of machine learning methods for wind turbine fault diagnosis
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/9f3284d42be74356b689ccb22fa575ce
work_keys_str_mv	AT mingzhutang reviewandperspectivesofmachinelearningmethodsforwindturbinefaultdiagnosis AT qizhao reviewandperspectivesofmachinelearningmethodsforwindturbinefaultdiagnosis AT huaweiwu reviewandperspectivesofmachinelearningmethodsforwindturbinefaultdiagnosis AT zimingwang reviewandperspectivesofmachinelearningmethodsforwindturbinefaultdiagnosis AT caihuameng reviewandperspectivesofmachinelearningmethodsforwindturbinefaultdiagnosis AT yifanwang reviewandperspectivesofmachinelearningmethodsforwindturbinefaultdiagnosis
_version_	1718428512799424512

Review and Perspectives of Machine Learning Methods for Wind Turbine Fault Diagnosis

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