Machine Learning Applied in the Detection of Faults in Pipes by Acoustic Means

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorFaculdade de Tecnologia de São Paulo “Prof. Fernando Amaral de Almeida Prado”-
Autor(es): dc.contributorIndira Gandhi National Tribal University-
Autor(es): dc.creatorMerizio, Igor Feliciani [UNESP]-
Autor(es): dc.creatorChavarette, Fábio Roberto [UNESP]-
Autor(es): dc.creatorMoro, Thiago Carreta [UNESP]-
Autor(es): dc.creatorOuta, Roberto-
Autor(es): dc.creatorMishra, Vishnu Narayan-
Data de aceite: dc.date.accessioned2022-02-22T00:53:59Z-
Data de disponibilização: dc.date.available2022-02-22T00:53:59Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2020-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s40032-021-00682-y-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/208640-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/208640-
Descrição: dc.descriptionThe 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.-
Descrição: dc.descriptionMechanical Engineering Department UNESP São Paulo State University “Julio de Mesquita Filho”, Av. Brasil Sul, 56, Donwtonw-
Descrição: dc.descriptionDepartment of Engineering Physics and Mathematics Institute of Chemistry UNESP São Paulo State University “Julio de Mesquita Filho”, Rua Prof. Francisco Degni, 55, Quitandinha-
Descrição: dc.descriptionCivil Engineering Department UNESP São Paulo State University “Julio de Mesquita Filho”, Av. Brasil Sul, 56, Donwtonw-
Descrição: dc.descriptionFATEC Faculdade de Tecnologia de São Paulo “Prof. Fernando Amaral de Almeida Prado”, Av. Prestes Maia 1764 - Jd. Ipanema-
Descrição: dc.descriptionDepartment of Mathematics Indira Gandhi National Tribal University, Lalpur, Amarkantak-
Descrição: dc.descriptionMechanical Engineering Department UNESP São Paulo State University “Julio de Mesquita Filho”, Av. Brasil Sul, 56, Donwtonw-
Descrição: dc.descriptionDepartment of Engineering Physics and Mathematics Institute of Chemistry UNESP São Paulo State University “Julio de Mesquita Filho”, Rua Prof. Francisco Degni, 55, Quitandinha-
Descrição: dc.descriptionCivil Engineering Department UNESP São Paulo State University “Julio de Mesquita Filho”, Av. Brasil Sul, 56, Donwtonw-
Idioma: dc.languageen-
Relação: dc.relationJournal of The Institution of Engineers (India): Series C-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectArtificial immune system-
Palavras-chave: dc.subjectDecision making-
Palavras-chave: dc.subjectNegative selection algorithm-
Palavras-chave: dc.subjectPreventive diagnosis-
Palavras-chave: dc.subjectStructural Health Monitoring-
Título: dc.titleMachine Learning Applied in the Detection of Faults in Pipes by Acoustic Means-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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