An improved impedance-based damage classification using self-organizing maps

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorFraunhofer Joint Laboratory of Excellence on Advanced Production Technology (Fh-J_LEAPT Naples)-
Autor(es): dc.contributorUniversity of Naples Federico II-
Autor(es): dc.creatorJunior, Pedro Oliveira [UNESP]-
Autor(es): dc.creatorConte, Salvatore-
Autor(es): dc.creatorD'Addona, Doriana M.-
Autor(es): dc.creatorAguiar, Paulo [UNESP]-
Autor(es): dc.creatorBapstista, Fabricio [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:26:47Z-
Data de disponibilização: dc.date.available2022-02-22T00:26:47Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2019-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.procir.2020.05.057-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/199222-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/199222-
Descrição: dc.descriptionThe identification and severity of structural damages, especially in the early stage, is critical in structural health monitoring (SHM) systems. Among several approaches used to accomplish this goal, the electromechanical impedance (EMI) technique has taken place within nondestructive evaluation (NDE) methods. On the other hand, neural networks (NN) based on self-organizing maps (SOM) has been a promising tool in many engineering classification problems. However, there is a gap of application regarding the combination of the EMI technique and SOM NN. To encourage this, an enhanced EMI-based damage classification method using self-organizing features is proposed in the present research paper. A SOM NN architecture was implemented whose inputs were derived from representative features of the impedance signatures. As a result, self-organizing maps can be used as an effective tool to enhance the damage classification in EMI-based SHM applications. For the present application, the results indicated a promising and useful contribution to the grinding field.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionUniv. Estadual Paulista UNESP School of Engineering Department of Electrical and Mechanical Engineering-
Descrição: dc.descriptionFraunhofer Joint Laboratory of Excellence on Advanced Production Technology (Fh-J_LEAPT Naples)-
Descrição: dc.descriptionDept. of Chemical Materials and Industrial Production Engineering University of Naples Federico II, Piazzale Tecchio 80-
Descrição: dc.descriptionUniv. Estadual Paulista UNESP School of Engineering Department of Electrical and Mechanical Engineering-
Descrição: dc.descriptionFAPESP: #2016/02831-5-
Formato: dc.format330-334-
Idioma: dc.languageen-
Relação: dc.relationProcedia CIRP-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectDiagnostic and maintenance-
Palavras-chave: dc.subjectElectromechanical impedance-
Palavras-chave: dc.subjectGrinding-
Palavras-chave: dc.subjectNeural networks-
Palavras-chave: dc.subjectSelf-organizing maps-
Palavras-chave: dc.subjectSensor monitoring-
Palavras-chave: dc.subjectSHM-
Título: dc.titleAn improved impedance-based damage classification using self-organizing maps-
Aparece nas coleções:Repositório Institucional - Unesp

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