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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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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 |
_version_ | 1785079058327928832 |
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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. |
format | Online Article Text |
id | pubmed-10375555 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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