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Wind turbine blades fault diagnosis based on vibration dataset analysis

Globally, wind turbines play a significant role in generating sustainable and clean energy. Ensuring optimal performance and reliability is crucial to minimize failures and reduce operating and maintenance costs. However, due to their conventional design, identifying faults in wind turbines is chall...

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Detalles Bibliográficos
Autores principales: Ogaili, Ahmed Ali Farhan, Abdulhady Jaber, Alaa, Hamzah, Mohsin Noori
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10375555/
https://www.ncbi.nlm.nih.gov/pubmed/37520651
http://dx.doi.org/10.1016/j.dib.2023.109414
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author Ogaili, Ahmed Ali Farhan
Abdulhady Jaber, Alaa
Hamzah, Mohsin Noori
author_facet Ogaili, Ahmed Ali Farhan
Abdulhady Jaber, Alaa
Hamzah, Mohsin Noori
author_sort Ogaili, Ahmed Ali Farhan
collection PubMed
description Globally, wind turbines play a significant role in generating sustainable and clean energy. Ensuring optimal performance and reliability is crucial to minimize failures and reduce operating and maintenance costs. However, due to their conventional design, identifying faults in wind turbines is challenging. This dataset provides vibration data for faulty wind turbine blades, which covers common vibration excitation mechanisms associated with various faults and operating conditions, including wind speed. The introduced faults in the wind turbine blades include surface erosion, cracked blade, mass imbalance, and twist blade fault. This data article serves as a valuable resource for validating condition monitoring methods in industrial wind turbine applications and facilitates a better understanding of vibration signal characteristics associated with different faults.
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spelling pubmed-103755552023-07-29 Wind turbine blades fault diagnosis based on vibration dataset analysis Ogaili, Ahmed Ali Farhan Abdulhady Jaber, Alaa Hamzah, Mohsin Noori Data Brief Update Article Globally, wind turbines play a significant role in generating sustainable and clean energy. Ensuring optimal performance and reliability is crucial to minimize failures and reduce operating and maintenance costs. However, due to their conventional design, identifying faults in wind turbines is challenging. This dataset provides vibration data for faulty wind turbine blades, which covers common vibration excitation mechanisms associated with various faults and operating conditions, including wind speed. The introduced faults in the wind turbine blades include surface erosion, cracked blade, mass imbalance, and twist blade fault. This data article serves as a valuable resource for validating condition monitoring methods in industrial wind turbine applications and facilitates a better understanding of vibration signal characteristics associated with different faults. Elsevier 2023-07-16 /pmc/articles/PMC10375555/ /pubmed/37520651 http://dx.doi.org/10.1016/j.dib.2023.109414 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Update Article
Ogaili, Ahmed Ali Farhan
Abdulhady Jaber, Alaa
Hamzah, Mohsin Noori
Wind turbine blades fault diagnosis based on vibration dataset analysis
title Wind turbine blades fault diagnosis based on vibration dataset analysis
title_full Wind turbine blades fault diagnosis based on vibration dataset analysis
title_fullStr Wind turbine blades fault diagnosis based on vibration dataset analysis
title_full_unstemmed Wind turbine blades fault diagnosis based on vibration dataset analysis
title_short Wind turbine blades fault diagnosis based on vibration dataset analysis
title_sort wind turbine blades fault diagnosis based on vibration dataset analysis
topic Update Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10375555/
https://www.ncbi.nlm.nih.gov/pubmed/37520651
http://dx.doi.org/10.1016/j.dib.2023.109414
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