A distributed topology for identifying anomalies in an industrial environment

Detalhes bibliográficos
Autor(a) principal: Zayas-Gato, Francisco
Data de Publicação: 2022
Outros Autores: Michelena, Alvaro, Jove, Esteban, Casteleiro-Roca, Jose-Luis, Quintian, Hector, Novais, Paulo, Albino Mendez-Perez, Juan, Luis Calvo-Rolle, Jose
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/1822/86271
Resumo: 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.
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spelling A distributed topology for identifying anomalies in an industrial environmentAnomaly detectionOne-classControl systemkNNMSTNCBoPPCASVDDCiências Naturais::Ciências da Computação e da InformaçãoScience & TechnologyThe 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.CITIC, as a Research Center of the university System of Galicia, is funded by Conselleria de Education, Universidade e Formacion Profesional of the Xunta de Galicia through the European regional Development Fund (ERDF) and the Secretaria Xeral de Universidades (Ref. ED431G 2019/01).SpringerUniversidade do MinhoZayas-Gato, FranciscoMichelena, AlvaroJove, EstebanCasteleiro-Roca, Jose-LuisQuintian, HectorNovais, PauloAlbino Mendez-Perez, JuanLuis Calvo-Rolle, Jose20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/86271engZayas-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/s00521-022-07106-70941-064310.1007/s00521-022-07106-7https://link.springer.com/article/10.1007/s00521-022-07106-7info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-09-09T01:16:47Zoai:repositorium.sdum.uminho.pt:1822/86271Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:28:41.873684Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A distributed topology for identifying anomalies in an industrial environment
title A distributed topology for identifying anomalies in an industrial environment
spellingShingle A distributed topology for identifying anomalies in an industrial environment
Zayas-Gato, Francisco
Anomaly detection
One-class
Control system
kNN
MST
NCBoP
PCA
SVDD
Ciências Naturais::Ciências da Computação e da Informação
Science & Technology
title_short A distributed topology for identifying anomalies in an industrial environment
title_full A distributed topology for identifying anomalies in an industrial environment
title_fullStr A distributed topology for identifying anomalies in an industrial environment
title_full_unstemmed A distributed topology for identifying anomalies in an industrial environment
title_sort A distributed topology for identifying anomalies in an industrial environment
author Zayas-Gato, Francisco
author_facet Zayas-Gato, Francisco
Michelena, Alvaro
Jove, Esteban
Casteleiro-Roca, Jose-Luis
Quintian, Hector
Novais, Paulo
Albino Mendez-Perez, Juan
Luis Calvo-Rolle, Jose
author_role author
author2 Michelena, Alvaro
Jove, Esteban
Casteleiro-Roca, Jose-Luis
Quintian, Hector
Novais, Paulo
Albino Mendez-Perez, Juan
Luis Calvo-Rolle, Jose
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Zayas-Gato, Francisco
Michelena, Alvaro
Jove, Esteban
Casteleiro-Roca, Jose-Luis
Quintian, Hector
Novais, Paulo
Albino Mendez-Perez, Juan
Luis Calvo-Rolle, Jose
dc.subject.por.fl_str_mv Anomaly detection
One-class
Control system
kNN
MST
NCBoP
PCA
SVDD
Ciências Naturais::Ciências da Computação e da Informação
Science & Technology
topic Anomaly detection
One-class
Control system
kNN
MST
NCBoP
PCA
SVDD
Ciências Naturais::Ciências da Computação e da Informação
Science & Technology
description 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.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/1822/86271
url https://hdl.handle.net/1822/86271
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Zayas-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/s00521-022-07106-7
0941-0643
10.1007/s00521-022-07106-7
https://link.springer.com/article/10.1007/s00521-022-07106-7
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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