Fault detection and identification for control systems in floating offshore wind farms: A supervised Deep Learning methodology

Fernandez Navamuel, Ana; Peña Sánchez, Yerai; Nava, Vincenzo

dc.rights.license	Attribution-NonCommercial-NoDerivatives 4.0 International
dc.contributor.author	Fernandez Navamuel, Ana
dc.contributor.author	Peña Sánchez, Yerai
dc.contributor.author	Nava, Vincenzo
dc.date.accessioned	2026-04-20T07:20:51Z
dc.date.available	2026-04-20T07:20:51Z
dc.date.issued	2024
dc.identifier.issn	1873-5258
dc.identifier.other	https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=178697
dc.identifier.uri	https://hdl.handle.net/20.500.11984/14172
dc.format.extent	p.
dc.language.iso	eng
dc.publisher	Elsevier
dc.rights	© 2024
dc.rights.uri	https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.title	Fault detection and identification for control systems in floating offshore wind farms: A supervised Deep Learning methodology
dcterms.accessRights	http://purl.org/coar/access_right/c_abf2
dcterms.source	Ocean Engineering
local.contributor.group	Mecánica de fluidos
local.description.peerreviewed	true
local.identifier.doi	https://doi.org/10.1016/j.oceaneng.2024.118678
local.contributor.otherinstitution	https://ror.org/02fv8hj62
local.contributor.otherinstitution	https://ror.org/03b21sh32
local.contributor.otherinstitution	https://ror.org/00wvqgd19
local.source.details	Vol. 310. Part 1. N. art. 118678. October, 2024
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