Transfer Component Analysis for Compensation of Temperature Effects on the Impedance-Based Structural Health Monitoring
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s10921-021-00794-6 http://hdl.handle.net/11449/221928 |
Resumo: | The effects of temperature fluctuations in the impedance measurements’ spectral estimates confuse the procedures to distinguish actual states’ classification, demanding compensation. The present paper demonstrates a new method to achieve temperature compensation based on a Transfer Component Analysis (TCA), a subtype of transfer learning, of the features from a source domain (in a well-known labeled condition) to another target domain (in an unknown condition). This procedure assumes only the labeled features data in the healthy condition (baseline) and damaged state in a specific known temperature as source data. The features computed are the Root Mean Square Deviation (RMSD) indices of the real and imaginary impedance signals. A machine-learning algorithm based on Mahalanobis squared distance (D2) is trained using the features computed from the baseline condition in the reference temperature. Also, the other temperature and structural conditions data are assumed as testing data of the target condition. TCA’s main idea is mapping the features from the original features space to a new subspace where the detection becomes possible using the same training data in the source domain. The results performed in a testbench with a piezoelectric element (PZT) bonded under a set of temperatures monitored, and simulated damage confirmed that the proposed method could recognize the real states correctly by transferring the knowledge from the features of the source domain into the target domain, assuming different temperatures. |
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Repositório Institucional da UNESP |
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Transfer Component Analysis for Compensation of Temperature Effects on the Impedance-Based Structural Health MonitoringDomain adaptationElectromechanical impedanceTemperature effectsTransfer component analysisThe effects of temperature fluctuations in the impedance measurements’ spectral estimates confuse the procedures to distinguish actual states’ classification, demanding compensation. The present paper demonstrates a new method to achieve temperature compensation based on a Transfer Component Analysis (TCA), a subtype of transfer learning, of the features from a source domain (in a well-known labeled condition) to another target domain (in an unknown condition). This procedure assumes only the labeled features data in the healthy condition (baseline) and damaged state in a specific known temperature as source data. The features computed are the Root Mean Square Deviation (RMSD) indices of the real and imaginary impedance signals. A machine-learning algorithm based on Mahalanobis squared distance (D2) is trained using the features computed from the baseline condition in the reference temperature. Also, the other temperature and structural conditions data are assumed as testing data of the target condition. TCA’s main idea is mapping the features from the original features space to a new subspace where the detection becomes possible using the same training data in the source domain. The results performed in a testbench with a piezoelectric element (PZT) bonded under a set of temperatures monitored, and simulated damage confirmed that the proposed method could recognize the real states correctly by transferring the knowledge from the features of the source domain into the target domain, assuming different temperatures.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Department of Mechanical Engineering São Paulo State University (UNESP), Avenida Brasil 56Department of Mechanical Engineering São Paulo State University (UNESP), Avenida Brasil 56CNPq: 306526/2019-0CAPES: 88882.433643/2019-01Universidade Estadual Paulista (UNESP)Silva, Samuel da [UNESP]Yano, Marcus Omori [UNESP]Gonsalez-Bueno, Camila Gianini [UNESP]2022-04-28T19:41:26Z2022-04-28T19:41:26Z2021-09-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1007/s10921-021-00794-6Journal of Nondestructive Evaluation, v. 40, n. 3, 2021.1573-48620195-9298http://hdl.handle.net/11449/22192810.1007/s10921-021-00794-62-s2.0-85109590371Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of Nondestructive Evaluationinfo:eu-repo/semantics/openAccess2022-04-28T19:41:26Zoai:repositorio.unesp.br:11449/221928Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-28T19:41:26Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Transfer Component Analysis for Compensation of Temperature Effects on the Impedance-Based Structural Health Monitoring |
title |
Transfer Component Analysis for Compensation of Temperature Effects on the Impedance-Based Structural Health Monitoring |
spellingShingle |
Transfer Component Analysis for Compensation of Temperature Effects on the Impedance-Based Structural Health Monitoring Silva, Samuel da [UNESP] Domain adaptation Electromechanical impedance Temperature effects Transfer component analysis |
title_short |
Transfer Component Analysis for Compensation of Temperature Effects on the Impedance-Based Structural Health Monitoring |
title_full |
Transfer Component Analysis for Compensation of Temperature Effects on the Impedance-Based Structural Health Monitoring |
title_fullStr |
Transfer Component Analysis for Compensation of Temperature Effects on the Impedance-Based Structural Health Monitoring |
title_full_unstemmed |
Transfer Component Analysis for Compensation of Temperature Effects on the Impedance-Based Structural Health Monitoring |
title_sort |
Transfer Component Analysis for Compensation of Temperature Effects on the Impedance-Based Structural Health Monitoring |
author |
Silva, Samuel da [UNESP] |
author_facet |
Silva, Samuel da [UNESP] Yano, Marcus Omori [UNESP] Gonsalez-Bueno, Camila Gianini [UNESP] |
author_role |
author |
author2 |
Yano, Marcus Omori [UNESP] Gonsalez-Bueno, Camila Gianini [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Silva, Samuel da [UNESP] Yano, Marcus Omori [UNESP] Gonsalez-Bueno, Camila Gianini [UNESP] |
dc.subject.por.fl_str_mv |
Domain adaptation Electromechanical impedance Temperature effects Transfer component analysis |
topic |
Domain adaptation Electromechanical impedance Temperature effects Transfer component analysis |
description |
The effects of temperature fluctuations in the impedance measurements’ spectral estimates confuse the procedures to distinguish actual states’ classification, demanding compensation. The present paper demonstrates a new method to achieve temperature compensation based on a Transfer Component Analysis (TCA), a subtype of transfer learning, of the features from a source domain (in a well-known labeled condition) to another target domain (in an unknown condition). This procedure assumes only the labeled features data in the healthy condition (baseline) and damaged state in a specific known temperature as source data. The features computed are the Root Mean Square Deviation (RMSD) indices of the real and imaginary impedance signals. A machine-learning algorithm based on Mahalanobis squared distance (D2) is trained using the features computed from the baseline condition in the reference temperature. Also, the other temperature and structural conditions data are assumed as testing data of the target condition. TCA’s main idea is mapping the features from the original features space to a new subspace where the detection becomes possible using the same training data in the source domain. The results performed in a testbench with a piezoelectric element (PZT) bonded under a set of temperatures monitored, and simulated damage confirmed that the proposed method could recognize the real states correctly by transferring the knowledge from the features of the source domain into the target domain, assuming different temperatures. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-09-01 2022-04-28T19:41:26Z 2022-04-28T19:41:26Z |
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/10.1007/s10921-021-00794-6 Journal of Nondestructive Evaluation, v. 40, n. 3, 2021. 1573-4862 0195-9298 http://hdl.handle.net/11449/221928 10.1007/s10921-021-00794-6 2-s2.0-85109590371 |
url |
http://dx.doi.org/10.1007/s10921-021-00794-6 http://hdl.handle.net/11449/221928 |
identifier_str_mv |
Journal of Nondestructive Evaluation, v. 40, n. 3, 2021. 1573-4862 0195-9298 10.1007/s10921-021-00794-6 2-s2.0-85109590371 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal of Nondestructive Evaluation |
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_ |
1797790257642471424 |