Acoustic inspection system with unmanned aerial vehicles for offshore wind turbines: A real case study

Wind energy has become fundamental in the global transition towards renewable energies, with the deployment of larger and more complex wind turbines. CMS play a crucial role in early fault detection, enhancing productivity while decreasing downtimes and maintenance costs to ensure the optimal perfor...

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Detalles Bibliográficos
Autores: Segovia Ramírez, Isaac, García Márquez, Fausto Pedro, Bernalte Sánchez, Pedro José, Peinado Gonzalo, Alfredo
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/47320
Acceso en línea:https://hdl.handle.net/10578/47320
Access Level:acceso abierto
Palabra clave:Offshore wind turbines
Acoustic analysis
Maintenance management
Unmanned aerial vehicle
Structural heal monitoring
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spelling Acoustic inspection system with unmanned aerial vehicles for offshore wind turbines: A real case studySegovia Ramírez, IsaacGarcía Márquez, Fausto PedroBernalte Sánchez, Pedro JoséPeinado Gonzalo, AlfredoOffshore wind turbinesAcoustic analysisMaintenance managementUnmanned aerial vehicleStructural heal monitoringWind energy has become fundamental in the global transition towards renewable energies, with the deployment of larger and more complex wind turbines. CMS play a crucial role in early fault detection, enhancing productivity while decreasing downtimes and maintenance costs to ensure the optimal performance and viability of the wind energy industry. This paper presents a novel non-destructive testing system embedded in an unmanned aerial vehicle designed to acquire acoustic data from rotating wind turbine components. This approach develops pre-processing and filtering methodologies based on wavelet transform, Fast Fourier or energy transformation to avoid undesired noise sources, e.g., the rotor of the drones or the environment, and to obtain patterns associated with the real state of the wind turbine. The implementation of acoustic monitoring in wind turbines is a novelty in the current state of the art, and this methodology is tested in an operating offshore wind turbine. The experiments incorporate an external condition monitoring system and introduce noise records from simulated mechanical faults. The results demonstrate that all the noise sources and faulty and healthy scenarios can be differentiated, proving the reliability of the methodology and the robustness of the fault detection approach.Elsevier202620262025info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10578/47320reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésPID2021-125278OB-I00info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/oai:ruidera.uclm.es:10578/473202026-05-27T07:36:41Z
dc.title.none.fl_str_mv Acoustic inspection system with unmanned aerial vehicles for offshore wind turbines: A real case study
title Acoustic inspection system with unmanned aerial vehicles for offshore wind turbines: A real case study
spellingShingle Acoustic inspection system with unmanned aerial vehicles for offshore wind turbines: A real case study
Segovia Ramírez, Isaac
Offshore wind turbines
Acoustic analysis
Maintenance management
Unmanned aerial vehicle
Structural heal monitoring
title_short Acoustic inspection system with unmanned aerial vehicles for offshore wind turbines: A real case study
title_full Acoustic inspection system with unmanned aerial vehicles for offshore wind turbines: A real case study
title_fullStr Acoustic inspection system with unmanned aerial vehicles for offshore wind turbines: A real case study
title_full_unstemmed Acoustic inspection system with unmanned aerial vehicles for offshore wind turbines: A real case study
title_sort Acoustic inspection system with unmanned aerial vehicles for offshore wind turbines: A real case study
dc.creator.none.fl_str_mv Segovia Ramírez, Isaac
García Márquez, Fausto Pedro
Bernalte Sánchez, Pedro José
Peinado Gonzalo, Alfredo
author Segovia Ramírez, Isaac
author_facet Segovia Ramírez, Isaac
García Márquez, Fausto Pedro
Bernalte Sánchez, Pedro José
Peinado Gonzalo, Alfredo
author_role author
author2 García Márquez, Fausto Pedro
Bernalte Sánchez, Pedro José
Peinado Gonzalo, Alfredo
author2_role author
author
author
dc.subject.none.fl_str_mv Offshore wind turbines
Acoustic analysis
Maintenance management
Unmanned aerial vehicle
Structural heal monitoring
topic Offshore wind turbines
Acoustic analysis
Maintenance management
Unmanned aerial vehicle
Structural heal monitoring
description Wind energy has become fundamental in the global transition towards renewable energies, with the deployment of larger and more complex wind turbines. CMS play a crucial role in early fault detection, enhancing productivity while decreasing downtimes and maintenance costs to ensure the optimal performance and viability of the wind energy industry. This paper presents a novel non-destructive testing system embedded in an unmanned aerial vehicle designed to acquire acoustic data from rotating wind turbine components. This approach develops pre-processing and filtering methodologies based on wavelet transform, Fast Fourier or energy transformation to avoid undesired noise sources, e.g., the rotor of the drones or the environment, and to obtain patterns associated with the real state of the wind turbine. The implementation of acoustic monitoring in wind turbines is a novelty in the current state of the art, and this methodology is tested in an operating offshore wind turbine. The experiments incorporate an external condition monitoring system and introduce noise records from simulated mechanical faults. The results demonstrate that all the noise sources and faulty and healthy scenarios can be differentiated, proving the reliability of the methodology and the robustness of the fault detection approach.
publishDate 2025
dc.date.none.fl_str_mv 2025
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10578/47320
url https://hdl.handle.net/10578/47320
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv PID2021-125278OB-I00
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812455

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