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dc.contributor.authorZayas-Gato, Francisco
dc.contributor.authorMichelena, Álvaro
dc.contributor.authorJove, Esteban
dc.contributor.authorCasteleiro-Roca, José-Luis
dc.contributor.authorQuintián, Héctor
dc.contributor.authorNovais, Paulo
dc.contributor.authorMéndez Pérez, Juan Albino
dc.contributor.authorCalvo-Rolle, José Luis
dc.date.accessioned2024-07-01T11:45:52Z
dc.date.available2024-07-01T11:45:52Z
dc.date.issued2022
dc.identifier.citationZayas-Gato, F., Michelena, Á., Jove, E. et al. A distributed topology for identifying anomalies in an industrial environment. Neural Comput & Applic 34, 20463–20476 (2022). https://doi.org/10.1007/s00521-022-07106-7es_ES
dc.identifier.issn0941-0643
dc.identifier.urihttp://hdl.handle.net/2183/37585
dc.descriptionOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.es_ES
dc.description.abstract[Abstract] The devastating consequences of climate change have resulted in the promotion of clean energies, being the wind energy the one with greater potential. This technology has been developed in recent years following different strategic plans, playing special attention to wind generation. In this sense, the use of bicomponent materials in wind generator blades and housings is a widely spread procedure. However, the great complexity of the process followed to obtain this kind of materials hinders the problem of detecting anomalous situations in the plant, due to sensors or actuators malfunctions. This has a direct impact on the features of the final product, with the corresponding influence in the durability and wind generator performance. In this context, the present work proposes the use of a distributed anomaly detection system to identify the source of the wrong operation. With this aim, five different one-class techniques are considered to detect deviations in three plant components located in a bicomponent mixing machine installation: the flow meter, the pressure sensor and the pump speed.es_ES
dc.description.sponsorshipCITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF) and the Secretaría Xeral de Universidades (Ref. ED431G 2019/01).es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01es_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.relation.urihttps://doi.org/10.1007/s00521-022-07106-7es_ES
dc.rightsCreative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/es_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectAnomaly detectiones_ES
dc.subjectOne-classes_ES
dc.subjectControl systemes_ES
dc.subjectkNNes_ES
dc.subjectMSTes_ES
dc.subjectNCBoPes_ES
dc.subjectPCAes_ES
dc.subjectSVDDes_ES
dc.titleA distributed topology for identifying anomalies in an industrial environmentes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleNeural Computing and Applicationses_ES
UDC.volume34es_ES
UDC.startPage20463es_ES
UDC.endPage20476es_ES
dc.identifier.doihttps://doi.org/10.1007/s00521-022-07106-7


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