Synthetic image dataset of shaft junctions inside wind turbines in presence or absence of oil leaks

This paper presents a dataset of images generated via 3D graphics rendering. The dataset is composed by pictures of the junction between the high-speed shaft and the external bracket of the power generator inside a wind turbine cabin, in presence and absence of oil leaks. Oil leak occurrence is an a...

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Autores principales:	Matteo Cardoni, Danilo Pau, Laura Falaschetti, Claudio Turchetti, Marco Lattuada
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Oil leaks Wind turbines Anomaly detection Machine learning Image dataset Image classification Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/2159c0177d74418092b887d33c369a2f
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spelling	oai:doaj.org-article:2159c0177d74418092b887d33c369a2f2021-11-10T04:28:02ZSynthetic image dataset of shaft junctions inside wind turbines in presence or absence of oil leaks2352-340910.1016/j.dib.2021.107538https://doaj.org/article/2159c0177d74418092b887d33c369a2f2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352340921008143https://doaj.org/toc/2352-3409This paper presents a dataset of images generated via 3D graphics rendering. The dataset is composed by pictures of the junction between the high-speed shaft and the external bracket of the power generator inside a wind turbine cabin, in presence and absence of oil leaks. Oil leak occurrence is an anomaly that can verify in a zone of interest of the junction. Since the wind turbines industry is becoming more and more important, turbines maintenance is growing in importance accordingly. In this context a dataset, as we propose, can be used, for example, to design machine learning algorithms for predictive maintenance. The renderings have been produced, from various framings and various leaks shapes and colors, using the rendering engine Keyshot9. Subsequent preprocessing has been performed with Matlab, including images grayscale conversion and image binarization. Finally, data augmentation has been implemented in Python, and it can be easily extended/customized for realizing any further processing. The Matlab and Python source codes are also provided. To the authors’ knowledge, there are no other public available datasets on this topic.Matteo CardoniDanilo PauLaura FalaschettiClaudio TurchettiMarco LattuadaElsevierarticleOil leaksWind turbinesAnomaly detectionMachine learningImage datasetImage classificationComputer applications to medicine. Medical informaticsR858-859.7Science (General)Q1-390ENData in Brief, Vol 39, Iss , Pp 107538- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Oil leaks Wind turbines Anomaly detection Machine learning Image dataset Image classification Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390
spellingShingle	Oil leaks Wind turbines Anomaly detection Machine learning Image dataset Image classification Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390 Matteo Cardoni Danilo Pau Laura Falaschetti Claudio Turchetti Marco Lattuada Synthetic image dataset of shaft junctions inside wind turbines in presence or absence of oil leaks
description	This paper presents a dataset of images generated via 3D graphics rendering. The dataset is composed by pictures of the junction between the high-speed shaft and the external bracket of the power generator inside a wind turbine cabin, in presence and absence of oil leaks. Oil leak occurrence is an anomaly that can verify in a zone of interest of the junction. Since the wind turbines industry is becoming more and more important, turbines maintenance is growing in importance accordingly. In this context a dataset, as we propose, can be used, for example, to design machine learning algorithms for predictive maintenance. The renderings have been produced, from various framings and various leaks shapes and colors, using the rendering engine Keyshot9. Subsequent preprocessing has been performed with Matlab, including images grayscale conversion and image binarization. Finally, data augmentation has been implemented in Python, and it can be easily extended/customized for realizing any further processing. The Matlab and Python source codes are also provided. To the authors’ knowledge, there are no other public available datasets on this topic.
format	article
author	Matteo Cardoni Danilo Pau Laura Falaschetti Claudio Turchetti Marco Lattuada
author_facet	Matteo Cardoni Danilo Pau Laura Falaschetti Claudio Turchetti Marco Lattuada
author_sort	Matteo Cardoni
title	Synthetic image dataset of shaft junctions inside wind turbines in presence or absence of oil leaks
title_short	Synthetic image dataset of shaft junctions inside wind turbines in presence or absence of oil leaks
title_full	Synthetic image dataset of shaft junctions inside wind turbines in presence or absence of oil leaks
title_fullStr	Synthetic image dataset of shaft junctions inside wind turbines in presence or absence of oil leaks
title_full_unstemmed	Synthetic image dataset of shaft junctions inside wind turbines in presence or absence of oil leaks
title_sort	synthetic image dataset of shaft junctions inside wind turbines in presence or absence of oil leaks
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/2159c0177d74418092b887d33c369a2f
work_keys_str_mv	AT matteocardoni syntheticimagedatasetofshaftjunctionsinsidewindturbinesinpresenceorabsenceofoilleaks AT danilopau syntheticimagedatasetofshaftjunctionsinsidewindturbinesinpresenceorabsenceofoilleaks AT laurafalaschetti syntheticimagedatasetofshaftjunctionsinsidewindturbinesinpresenceorabsenceofoilleaks AT claudioturchetti syntheticimagedatasetofshaftjunctionsinsidewindturbinesinpresenceorabsenceofoilleaks AT marcolattuada syntheticimagedatasetofshaftjunctionsinsidewindturbinesinpresenceorabsenceofoilleaks
_version_	1718440629775630336

Synthetic image dataset of shaft junctions inside wind turbines in presence or absence of oil leaks

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