Structural FRF acquisition via electric impedance measurement applied to damage location

Detalhes bibliográficos
Autor(a) principal: Lopes, Vicente [UNESP]
Data de Publicação: 2000
Outros Autores: Pereira, Joao Antonio [UNESP], Inman, Daniel J. [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/231689
Resumo: Continuing development of new materials makes systems lighter and stronger permitting more complex systems to provide more functionality and flexibility that demands a more effective evaluation of their structural health. Smart material technology has become an area of increasing interest in this field. The combination of smart materials and artificial neural networks can be used as an excellent tool for pattern recognition, turning their application adequate for monitoring and fault classification of equipment and structures. In order to identify the fault, the neural network must be trained using a set of solutions to its corresponding forward variational problem. After the training process, the net can successfully solve the inverse variational problem in the context of monitoring and fault detection because of their pattern recognition and interpolation capabilities. The use of structural frequency response function is a fundamental portion of structural dynamic analysis, and it can be extracted from measured electric impedance through the electromechanical interaction of a piezoceramic and a structure. In this paper we use the FRF obtained by a mathematical model (FEM) in order to generate the training data for the neural networks, and the identification of damage can be done by measuring electric impedance, since suitable data normalization correlates FRF and electrical impedance.
id UNSP_9a3d64d7dc04af4bd3d81f85a9055363
oai_identifier_str oai:repositorio.unesp.br:11449/231689
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
spelling Structural FRF acquisition via electric impedance measurement applied to damage locationContinuing development of new materials makes systems lighter and stronger permitting more complex systems to provide more functionality and flexibility that demands a more effective evaluation of their structural health. Smart material technology has become an area of increasing interest in this field. The combination of smart materials and artificial neural networks can be used as an excellent tool for pattern recognition, turning their application adequate for monitoring and fault classification of equipment and structures. In order to identify the fault, the neural network must be trained using a set of solutions to its corresponding forward variational problem. After the training process, the net can successfully solve the inverse variational problem in the context of monitoring and fault detection because of their pattern recognition and interpolation capabilities. The use of structural frequency response function is a fundamental portion of structural dynamic analysis, and it can be extracted from measured electric impedance through the electromechanical interaction of a piezoceramic and a structure. In this paper we use the FRF obtained by a mathematical model (FEM) in order to generate the training data for the neural networks, and the identification of damage can be done by measuring electric impedance, since suitable data normalization correlates FRF and electrical impedance.UNESP, Ilha SolteiraUNESP, Ilha SolteiraUniversidade Estadual Paulista (UNESP)Lopes, Vicente [UNESP]Pereira, Joao Antonio [UNESP]Inman, Daniel J. [UNESP]2022-04-29T08:46:55Z2022-04-29T08:46:55Z2000-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1549-1555Proceedings of the International Modal Analysis Conference - IMAC, v. 2, p. 1549-1555.1046-6770http://hdl.handle.net/11449/2316892-s2.0-0033877007Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the International Modal Analysis Conference - IMACinfo:eu-repo/semantics/openAccess2024-07-04T20:06:15Zoai:repositorio.unesp.br:11449/231689Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:28:42.873131Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Structural FRF acquisition via electric impedance measurement applied to damage location
title Structural FRF acquisition via electric impedance measurement applied to damage location
spellingShingle Structural FRF acquisition via electric impedance measurement applied to damage location
Lopes, Vicente [UNESP]
title_short Structural FRF acquisition via electric impedance measurement applied to damage location
title_full Structural FRF acquisition via electric impedance measurement applied to damage location
title_fullStr Structural FRF acquisition via electric impedance measurement applied to damage location
title_full_unstemmed Structural FRF acquisition via electric impedance measurement applied to damage location
title_sort Structural FRF acquisition via electric impedance measurement applied to damage location
author Lopes, Vicente [UNESP]
author_facet Lopes, Vicente [UNESP]
Pereira, Joao Antonio [UNESP]
Inman, Daniel J. [UNESP]
author_role author
author2 Pereira, Joao Antonio [UNESP]
Inman, Daniel J. [UNESP]
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
dc.contributor.author.fl_str_mv Lopes, Vicente [UNESP]
Pereira, Joao Antonio [UNESP]
Inman, Daniel J. [UNESP]
description Continuing development of new materials makes systems lighter and stronger permitting more complex systems to provide more functionality and flexibility that demands a more effective evaluation of their structural health. Smart material technology has become an area of increasing interest in this field. The combination of smart materials and artificial neural networks can be used as an excellent tool for pattern recognition, turning their application adequate for monitoring and fault classification of equipment and structures. In order to identify the fault, the neural network must be trained using a set of solutions to its corresponding forward variational problem. After the training process, the net can successfully solve the inverse variational problem in the context of monitoring and fault detection because of their pattern recognition and interpolation capabilities. The use of structural frequency response function is a fundamental portion of structural dynamic analysis, and it can be extracted from measured electric impedance through the electromechanical interaction of a piezoceramic and a structure. In this paper we use the FRF obtained by a mathematical model (FEM) in order to generate the training data for the neural networks, and the identification of damage can be done by measuring electric impedance, since suitable data normalization correlates FRF and electrical impedance.
publishDate 2000
dc.date.none.fl_str_mv 2000-01-01
2022-04-29T08:46:55Z
2022-04-29T08:46:55Z
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 Proceedings of the International Modal Analysis Conference - IMAC, v. 2, p. 1549-1555.
1046-6770
http://hdl.handle.net/11449/231689
2-s2.0-0033877007
identifier_str_mv Proceedings of the International Modal Analysis Conference - IMAC, v. 2, p. 1549-1555.
1046-6770
2-s2.0-0033877007
url http://hdl.handle.net/11449/231689
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Proceedings of the International Modal Analysis Conference - IMAC
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 1549-1555
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_ 1808129207493984256