Using Support Vector Machine Model for Fault Detection along a Water canal

Detalhes bibliográficos
Autor(a) principal: Duarte, J
Data de Publicação: 2014
Outros Autores: Rato, L, Rijo, M
Tipo de documento: Artigo
Idioma: por
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10174/12600
Resumo: This paper reports a work in progress, the training of a Support Vector Machine model to detect faults in an experimental water supply canal. The work took place at the experimental canal of Núcleo de Hidráulica e Controlo de Canais at the Universidade de Évora. The main objective is to identify faults in the water depth sensors and to detect unauthorized water withdrawals using pattern recognition. The preliminary accuracy tests, in and out of sample, have shown an accuracy over 90% to identify 28 different patterns.
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spelling Using Support Vector Machine Model for Fault Detection along a Water canalWateráguaFault detectiondeteção de avariascontrolcontrolocanalThis paper reports a work in progress, the training of a Support Vector Machine model to detect faults in an experimental water supply canal. The work took place at the experimental canal of Núcleo de Hidráulica e Controlo de Canais at the Universidade de Évora. The main objective is to identify faults in the water depth sensors and to detect unauthorized water withdrawals using pattern recognition. The preliminary accuracy tests, in and out of sample, have shown an accuracy over 90% to identify 28 different patterns.APRP - Associação Portuguesa de Reconhecimento de Padrões2015-02-18T12:24:59Z2015-02-182014-10-31T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/12600http://hdl.handle.net/10174/12600porDuarte, J, Rato, L, Rijo, M;Using Support Vector Machine model for fault detection along a water canal, RECPAD2014, 20th Portuguese Conference on Pattern Recognition, 48-49, 2014.http://www.aprp.pt/wp-content/uploads/RecPad2014_proceedings.pdfCITIUEjduarte@uevora.ptlmr@uevora.ptrijo@uevora.pt498Duarte, JRato, LRijo, Minfo: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:RCAAP2023-08-08T04:08:08ZPortal AgregadorONG
dc.title.none.fl_str_mv Using Support Vector Machine Model for Fault Detection along a Water canal
title Using Support Vector Machine Model for Fault Detection along a Water canal
spellingShingle Using Support Vector Machine Model for Fault Detection along a Water canal
Duarte, J
Water
água
Fault detection
deteção de avarias
control
controlo
canal
title_short Using Support Vector Machine Model for Fault Detection along a Water canal
title_full Using Support Vector Machine Model for Fault Detection along a Water canal
title_fullStr Using Support Vector Machine Model for Fault Detection along a Water canal
title_full_unstemmed Using Support Vector Machine Model for Fault Detection along a Water canal
title_sort Using Support Vector Machine Model for Fault Detection along a Water canal
author Duarte, J
author_facet Duarte, J
Rato, L
Rijo, M
author_role author
author2 Rato, L
Rijo, M
author2_role author
author
dc.contributor.author.fl_str_mv Duarte, J
Rato, L
Rijo, M
dc.subject.por.fl_str_mv Water
água
Fault detection
deteção de avarias
control
controlo
canal
topic Water
água
Fault detection
deteção de avarias
control
controlo
canal
description This paper reports a work in progress, the training of a Support Vector Machine model to detect faults in an experimental water supply canal. The work took place at the experimental canal of Núcleo de Hidráulica e Controlo de Canais at the Universidade de Évora. The main objective is to identify faults in the water depth sensors and to detect unauthorized water withdrawals using pattern recognition. The preliminary accuracy tests, in and out of sample, have shown an accuracy over 90% to identify 28 different patterns.
publishDate 2014
dc.date.none.fl_str_mv 2014-10-31T00:00:00Z
2015-02-18T12:24:59Z
2015-02-18
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 http://hdl.handle.net/10174/12600
http://hdl.handle.net/10174/12600
url http://hdl.handle.net/10174/12600
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv Duarte, J, Rato, L, Rijo, M;Using Support Vector Machine model for fault detection along a water canal, RECPAD2014, 20th Portuguese Conference on Pattern Recognition, 48-49, 2014.
http://www.aprp.pt/wp-content/uploads/RecPad2014_proceedings.pdf
CITIUE
jduarte@uevora.pt
lmr@uevora.pt
rijo@uevora.pt
498
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv APRP - Associação Portuguesa de Reconhecimento de Padrões
publisher.none.fl_str_mv APRP - Associação Portuguesa de Reconhecimento de Padrões
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
repository.mail.fl_str_mv
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