A distributed topology for identifying anomalies in an industrial environment
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , |
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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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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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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 |
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