Machine Learning Applied in the Detection of Faults in Pipes by Acoustic Means
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s40032-021-00682-y http://hdl.handle.net/11449/208640 |
Resumo: | The Structural Health Monitoring evaluates the situation of aeronautical, civil or mechanical structures and provides a forecast of its remaining useful life, acting in decision making, being able to intervene in critical situations. It has emerged as a viable economic alternative for monitoring structures and preventing failures. Thus, this system is defined as a prophylactic measure, reliable and effective against structural failure. This work exposes the theoretical basis and a new technique for detection of failures in pipes by acoustic means, following the International Standard ISO10534-1 (1996) in the sampling. This method of fault detection using acoustic means requires considerably less training data than is usually used in the literature, with approximately 85% less data. The results presented in this work showed how it is possible and effective to detect failure in pipes by acoustic means using an artificial immune system for structural monitoring, with a 100% precision in the detection of failure. |
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Machine Learning Applied in the Detection of Faults in Pipes by Acoustic MeansArtificial immune systemDecision makingNegative selection algorithmPreventive diagnosisStructural Health MonitoringThe Structural Health Monitoring evaluates the situation of aeronautical, civil or mechanical structures and provides a forecast of its remaining useful life, acting in decision making, being able to intervene in critical situations. It has emerged as a viable economic alternative for monitoring structures and preventing failures. Thus, this system is defined as a prophylactic measure, reliable and effective against structural failure. This work exposes the theoretical basis and a new technique for detection of failures in pipes by acoustic means, following the International Standard ISO10534-1 (1996) in the sampling. This method of fault detection using acoustic means requires considerably less training data than is usually used in the literature, with approximately 85% less data. The results presented in this work showed how it is possible and effective to detect failure in pipes by acoustic means using an artificial immune system for structural monitoring, with a 100% precision in the detection of failure.Mechanical Engineering Department UNESP São Paulo State University “Julio de Mesquita Filho”, Av. Brasil Sul, 56, DonwtonwDepartment of Engineering Physics and Mathematics Institute of Chemistry UNESP São Paulo State University “Julio de Mesquita Filho”, Rua Prof. Francisco Degni, 55, QuitandinhaCivil Engineering Department UNESP São Paulo State University “Julio de Mesquita Filho”, Av. Brasil Sul, 56, DonwtonwFATEC Faculdade de Tecnologia de São Paulo “Prof. Fernando Amaral de Almeida Prado”, Av. Prestes Maia 1764 - Jd. IpanemaDepartment of Mathematics Indira Gandhi National Tribal University, Lalpur, AmarkantakMechanical Engineering Department UNESP São Paulo State University “Julio de Mesquita Filho”, Av. Brasil Sul, 56, DonwtonwDepartment of Engineering Physics and Mathematics Institute of Chemistry UNESP São Paulo State University “Julio de Mesquita Filho”, Rua Prof. Francisco Degni, 55, QuitandinhaCivil Engineering Department UNESP São Paulo State University “Julio de Mesquita Filho”, Av. Brasil Sul, 56, DonwtonwUniversidade Estadual Paulista (Unesp)Faculdade de Tecnologia de São Paulo “Prof. Fernando Amaral de Almeida Prado”Indira Gandhi National Tribal UniversityMerizio, Igor Feliciani [UNESP]Chavarette, Fábio Roberto [UNESP]Moro, Thiago Carreta [UNESP]Outa, RobertoMishra, Vishnu Narayan2021-06-25T11:15:28Z2021-06-25T11:15:28Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1007/s40032-021-00682-yJournal of The Institution of Engineers (India): Series C.2250-05532250-0545http://hdl.handle.net/11449/20864010.1007/s40032-021-00682-y2-s2.0-85105206686Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of The Institution of Engineers (India): Series Cinfo:eu-repo/semantics/openAccess2024-07-10T15:41:40Zoai:repositorio.unesp.br:11449/208640Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:38:42.637785Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Machine Learning Applied in the Detection of Faults in Pipes by Acoustic Means |
title |
Machine Learning Applied in the Detection of Faults in Pipes by Acoustic Means |
spellingShingle |
Machine Learning Applied in the Detection of Faults in Pipes by Acoustic Means Merizio, Igor Feliciani [UNESP] Artificial immune system Decision making Negative selection algorithm Preventive diagnosis Structural Health Monitoring |
title_short |
Machine Learning Applied in the Detection of Faults in Pipes by Acoustic Means |
title_full |
Machine Learning Applied in the Detection of Faults in Pipes by Acoustic Means |
title_fullStr |
Machine Learning Applied in the Detection of Faults in Pipes by Acoustic Means |
title_full_unstemmed |
Machine Learning Applied in the Detection of Faults in Pipes by Acoustic Means |
title_sort |
Machine Learning Applied in the Detection of Faults in Pipes by Acoustic Means |
author |
Merizio, Igor Feliciani [UNESP] |
author_facet |
Merizio, Igor Feliciani [UNESP] Chavarette, Fábio Roberto [UNESP] Moro, Thiago Carreta [UNESP] Outa, Roberto Mishra, Vishnu Narayan |
author_role |
author |
author2 |
Chavarette, Fábio Roberto [UNESP] Moro, Thiago Carreta [UNESP] Outa, Roberto Mishra, Vishnu Narayan |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Faculdade de Tecnologia de São Paulo “Prof. Fernando Amaral de Almeida Prado” Indira Gandhi National Tribal University |
dc.contributor.author.fl_str_mv |
Merizio, Igor Feliciani [UNESP] Chavarette, Fábio Roberto [UNESP] Moro, Thiago Carreta [UNESP] Outa, Roberto Mishra, Vishnu Narayan |
dc.subject.por.fl_str_mv |
Artificial immune system Decision making Negative selection algorithm Preventive diagnosis Structural Health Monitoring |
topic |
Artificial immune system Decision making Negative selection algorithm Preventive diagnosis Structural Health Monitoring |
description |
The Structural Health Monitoring evaluates the situation of aeronautical, civil or mechanical structures and provides a forecast of its remaining useful life, acting in decision making, being able to intervene in critical situations. It has emerged as a viable economic alternative for monitoring structures and preventing failures. Thus, this system is defined as a prophylactic measure, reliable and effective against structural failure. This work exposes the theoretical basis and a new technique for detection of failures in pipes by acoustic means, following the International Standard ISO10534-1 (1996) in the sampling. This method of fault detection using acoustic means requires considerably less training data than is usually used in the literature, with approximately 85% less data. The results presented in this work showed how it is possible and effective to detect failure in pipes by acoustic means using an artificial immune system for structural monitoring, with a 100% precision in the detection of failure. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06-25T11:15:28Z 2021-06-25T11:15:28Z 2021-01-01 |
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://dx.doi.org/10.1007/s40032-021-00682-y Journal of The Institution of Engineers (India): Series C. 2250-0553 2250-0545 http://hdl.handle.net/11449/208640 10.1007/s40032-021-00682-y 2-s2.0-85105206686 |
url |
http://dx.doi.org/10.1007/s40032-021-00682-y http://hdl.handle.net/11449/208640 |
identifier_str_mv |
Journal of The Institution of Engineers (India): Series C. 2250-0553 2250-0545 10.1007/s40032-021-00682-y 2-s2.0-85105206686 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal of The Institution of Engineers (India): Series C |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128959053824000 |