Delamination area quantification in composite structures using Gaussian process regression and auto-regressive models
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , |
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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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 |
|
_version_ |
1799964445134290944 |