Diagnosis of the health status of mooring systems for floating offshore wind turbines using autoencoders

Floating offshore wind turbines (FOWTs) show promise in terms of energy production, availability, and sustainability, but remain unprofitable due to high maintenance costs. This work proposes a deep learning algorithm to detect mooring line degradation and failure by monitoring the dynamic response...

Descripción completa

Detalles Bibliográficos
Autores: Gorostidi, N., Pardo, D., Nava, V.
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:TECNALIA Research & Innovation
Repositorio:TECNALIA Publications
Idioma:inglés
OAI Identifier:oai:dsp.tecnalia.com:11556/3234
Acceso en línea:https://hdl.handle.net/11556/3234
Access Level:acceso abierto
Palabra clave:Autoencoder
Deep learning
Floating offshore wind
Inverse problem
Operation and maintenance
Environmental Engineering
Ocean Engineering
SDG 7 - Affordable and Clean Energy
id ES_0e60cec71ca897c0710c72c5e91b5d64
oai_identifier_str oai:dsp.tecnalia.com:11556/3234
network_acronym_str ES
network_name_str España
repository_id_str
spelling Diagnosis of the health status of mooring systems for floating offshore wind turbines using autoencodersGorostidi, N.Pardo, D.Nava, V.AutoencoderDeep learningFloating offshore windInverse problemOperation and maintenanceEnvironmental EngineeringOcean EngineeringSDG 7 - Affordable and Clean EnergyFloating offshore wind turbines (FOWTs) show promise in terms of energy production, availability, and sustainability, but remain unprofitable due to high maintenance costs. This work proposes a deep learning algorithm to detect mooring line degradation and failure by monitoring the dynamic response of the publicly available DeepCWind OC4 semi-submersible platform. This study implements an autoencoder capable of predicting multiple forms of damage occurring at once, with various levels of severity. Given the scarcity of real data, simulations performed in OpenFAST, recreating both healthy and damaged mooring systems, are used to train and validate the algorithm. The novelty of the proposed approach consists of using a set of key statistical metrics describing the platform's displacements and rotations as input layer for the autoencoder. The statistics of the responses are calculated at 33-minute-long sea states under a broad spectrum of metocean and wind conditions. An autoencoder is trained using these parameters to discover that the proposed algorithm is capable of detecting mild anomalies caused by biofouling and anchor displacements, with correlation coefficients up to 98.51% and 99.16%, respectively. These results are encouraging for the continuous health monitoring of FOWT mooring systems using easily measurable quantities to plan preventive maintenance actions adequately.RENOVABLES OFFSHORE20232023-11-0120232023-11-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articlehttps://hdl.handle.net/11556/3234reponame:TECNALIA Publicationsinstname:TECNALIA Research & InnovationInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:dsp.tecnalia.com:11556/32342026-06-12T12:42:27Z
dc.title.none.fl_str_mv Diagnosis of the health status of mooring systems for floating offshore wind turbines using autoencoders
title Diagnosis of the health status of mooring systems for floating offshore wind turbines using autoencoders
spellingShingle Diagnosis of the health status of mooring systems for floating offshore wind turbines using autoencoders
Gorostidi, N.
Autoencoder
Deep learning
Floating offshore wind
Inverse problem
Operation and maintenance
Environmental Engineering
Ocean Engineering
SDG 7 - Affordable and Clean Energy
title_short Diagnosis of the health status of mooring systems for floating offshore wind turbines using autoencoders
title_full Diagnosis of the health status of mooring systems for floating offshore wind turbines using autoencoders
title_fullStr Diagnosis of the health status of mooring systems for floating offshore wind turbines using autoencoders
title_full_unstemmed Diagnosis of the health status of mooring systems for floating offshore wind turbines using autoencoders
title_sort Diagnosis of the health status of mooring systems for floating offshore wind turbines using autoencoders
dc.creator.none.fl_str_mv Gorostidi, N.
Pardo, D.
Nava, V.
author Gorostidi, N.
author_facet Gorostidi, N.
Pardo, D.
Nava, V.
author_role author
author2 Pardo, D.
Nava, V.
author2_role author
author
dc.contributor.none.fl_str_mv RENOVABLES OFFSHORE
dc.subject.none.fl_str_mv Autoencoder
Deep learning
Floating offshore wind
Inverse problem
Operation and maintenance
Environmental Engineering
Ocean Engineering
SDG 7 - Affordable and Clean Energy
topic Autoencoder
Deep learning
Floating offshore wind
Inverse problem
Operation and maintenance
Environmental Engineering
Ocean Engineering
SDG 7 - Affordable and Clean Energy
description Floating offshore wind turbines (FOWTs) show promise in terms of energy production, availability, and sustainability, but remain unprofitable due to high maintenance costs. This work proposes a deep learning algorithm to detect mooring line degradation and failure by monitoring the dynamic response of the publicly available DeepCWind OC4 semi-submersible platform. This study implements an autoencoder capable of predicting multiple forms of damage occurring at once, with various levels of severity. Given the scarcity of real data, simulations performed in OpenFAST, recreating both healthy and damaged mooring systems, are used to train and validate the algorithm. The novelty of the proposed approach consists of using a set of key statistical metrics describing the platform's displacements and rotations as input layer for the autoencoder. The statistics of the responses are calculated at 33-minute-long sea states under a broad spectrum of metocean and wind conditions. An autoencoder is trained using these parameters to discover that the proposed algorithm is capable of detecting mild anomalies caused by biofouling and anchor displacements, with correlation coefficients up to 98.51% and 99.16%, respectively. These results are encouraging for the continuous health monitoring of FOWT mooring systems using easily measurable quantities to plan preventive maintenance actions adequately.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-11-01
2023
2023-11-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/11556/3234
url https://hdl.handle.net/11556/3234
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:TECNALIA Publications
instname:TECNALIA Research & Innovation
instname_str TECNALIA Research & Innovation
reponame_str TECNALIA Publications
collection TECNALIA Publications
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869403393539178496
score 15,812429

Similares en LA Referencia