Delamination area quantification in composite structures using Gaussian process regression and auto-regressive models

Detalhes bibliográficos
Autor(a) principal: Paixão, Jessé [UNESP]
Data de Publicação: 2020
Outros Autores: da Silva, Samuel [UNESP], Figueiredo, Eloi, Radu, Lucian, Park, Gyuhae
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1177/1077546320966183
http://hdl.handle.net/11449/205373
Resumo: After detecting initial delamination damage in a hotspot region of a composite structure monitored through a data-driven approach, the user needs to decide if there is an imminent structural failure or if the system can be kept in operation under monitoring to track the damage progression and its impact on the structural safety condition. Therefore, this study proposes delamination area quantification by stochastically interpolating global damage indices based on Gaussian process regression and taking into account uncertainty. Auto-regressive models are applied to extract damage-sensitive features from Lamb wave signals, and the Mahalanobis squared distance is used to compute damage indices. Two sets of laboratory tests are used to demonstrate the effectiveness of this methodology—one in carbon–epoxy laminate with simulated damage under temperature changes to show the general steps of the procedure, and a second test involving a set of carbon fiber–reinforced polymer coupons with actual delamination caused by repeated fatigue loads. Various levels of progression damage, measured by the covered area of delamination, are monitored using piezoelectric lead zirconate titanate patches bonded to the structural surfaces of these setups. The Gaussian process regression proved to be capable of accommodating the uncertainties to relate the damage indices versus the damaged area. The results exhibit a smooth and adequate prediction of the damaged area for both simulated damage and actual delamination.
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spelling Delamination area quantification in composite structures using Gaussian process regression and auto-regressive modelsauto-regressive modelsComposite structuresdamage quantificationdelaminationGaussian process regressionguided waveAfter detecting initial delamination damage in a hotspot region of a composite structure monitored through a data-driven approach, the user needs to decide if there is an imminent structural failure or if the system can be kept in operation under monitoring to track the damage progression and its impact on the structural safety condition. Therefore, this study proposes delamination area quantification by stochastically interpolating global damage indices based on Gaussian process regression and taking into account uncertainty. Auto-regressive models are applied to extract damage-sensitive features from Lamb wave signals, and the Mahalanobis squared distance is used to compute damage indices. Two sets of laboratory tests are used to demonstrate the effectiveness of this methodology—one in carbon–epoxy laminate with simulated damage under temperature changes to show the general steps of the procedure, and a second test involving a set of carbon fiber–reinforced polymer coupons with actual delamination caused by repeated fatigue loads. Various levels of progression damage, measured by the covered area of delamination, are monitored using piezoelectric lead zirconate titanate patches bonded to the structural surfaces of these setups. The Gaussian process regression proved to be capable of accommodating the uncertainties to relate the damage indices versus the damaged area. The results exhibit a smooth and adequate prediction of the damaged area for both simulated damage and actual delamination.Departamento de Engenharia Mecânica UNESP-Universidade Estadual PaulistaFaculty of Engineering Lusófona UniversityCONSTRUCT Faculdade de Engenharia Universidade do PortoDepartment of Mechanical Engineering Chonnam National UniversityDepartamento de Engenharia Mecânica UNESP-Universidade Estadual PaulistaUniversidade Estadual Paulista (Unesp)Lusófona UniversityUniversidade do PortoChonnam National UniversityPaixão, Jessé [UNESP]da Silva, Samuel [UNESP]Figueiredo, EloiRadu, LucianPark, Gyuhae2021-06-25T10:14:13Z2021-06-25T10:14:13Z2020-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1177/1077546320966183JVC/Journal of Vibration and Control.1741-29861077-5463http://hdl.handle.net/11449/20537310.1177/10775463209661832-s2.0-85093931158Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJVC/Journal of Vibration and Controlinfo:eu-repo/semantics/openAccess2021-10-23T12:39:48Zoai:repositorio.unesp.br:11449/205373Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T12:39:48Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Delamination area quantification in composite structures using Gaussian process regression and auto-regressive models
title Delamination area quantification in composite structures using Gaussian process regression and auto-regressive models
spellingShingle Delamination area quantification in composite structures using Gaussian process regression and auto-regressive models
Paixão, Jessé [UNESP]
auto-regressive models
Composite structures
damage quantification
delamination
Gaussian process regression
guided wave
title_short Delamination area quantification in composite structures using Gaussian process regression and auto-regressive models
title_full Delamination area quantification in composite structures using Gaussian process regression and auto-regressive models
title_fullStr Delamination area quantification in composite structures using Gaussian process regression and auto-regressive models
title_full_unstemmed Delamination area quantification in composite structures using Gaussian process regression and auto-regressive models
title_sort Delamination area quantification in composite structures using Gaussian process regression and auto-regressive models
author Paixão, Jessé [UNESP]
author_facet Paixão, Jessé [UNESP]
da Silva, Samuel [UNESP]
Figueiredo, Eloi
Radu, Lucian
Park, Gyuhae
author_role author
author2 da Silva, Samuel [UNESP]
Figueiredo, Eloi
Radu, Lucian
Park, Gyuhae
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Lusófona University
Universidade do Porto
Chonnam National University
dc.contributor.author.fl_str_mv Paixão, Jessé [UNESP]
da Silva, Samuel [UNESP]
Figueiredo, Eloi
Radu, Lucian
Park, Gyuhae
dc.subject.por.fl_str_mv auto-regressive models
Composite structures
damage quantification
delamination
Gaussian process regression
guided wave
topic auto-regressive models
Composite structures
damage quantification
delamination
Gaussian process regression
guided wave
description After detecting initial delamination damage in a hotspot region of a composite structure monitored through a data-driven approach, the user needs to decide if there is an imminent structural failure or if the system can be kept in operation under monitoring to track the damage progression and its impact on the structural safety condition. Therefore, this study proposes delamination area quantification by stochastically interpolating global damage indices based on Gaussian process regression and taking into account uncertainty. Auto-regressive models are applied to extract damage-sensitive features from Lamb wave signals, and the Mahalanobis squared distance is used to compute damage indices. Two sets of laboratory tests are used to demonstrate the effectiveness of this methodology—one in carbon–epoxy laminate with simulated damage under temperature changes to show the general steps of the procedure, and a second test involving a set of carbon fiber–reinforced polymer coupons with actual delamination caused by repeated fatigue loads. Various levels of progression damage, measured by the covered area of delamination, are monitored using piezoelectric lead zirconate titanate patches bonded to the structural surfaces of these setups. The Gaussian process regression proved to be capable of accommodating the uncertainties to relate the damage indices versus the damaged area. The results exhibit a smooth and adequate prediction of the damaged area for both simulated damage and actual delamination.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-01
2021-06-25T10:14:13Z
2021-06-25T10:14:13Z
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/10.1177/1077546320966183
JVC/Journal of Vibration and Control.
1741-2986
1077-5463
http://hdl.handle.net/11449/205373
10.1177/1077546320966183
2-s2.0-85093931158
url http://dx.doi.org/10.1177/1077546320966183
http://hdl.handle.net/11449/205373
identifier_str_mv JVC/Journal of Vibration and Control.
1741-2986
1077-5463
10.1177/1077546320966183
2-s2.0-85093931158
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv JVC/Journal of Vibration and Control
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
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