Electromechanical impedance based self-diagnosis of piezoelectric smart structure using principal component analysis and LibSVM

Abstract The long-term use of a piezoelectric smart structure make it difficult to judge whether the structure or piezoelectric lead zirconate titanate (PZT) is damaged when the signal changes. If the sensor fault occurs, the cases and degrees of the fault are unknown based on the electromechanical...

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Autores principales:	Xie Jiang, Xin Zhang, Tao Tang, Yuxiang Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/67921aa485944365a2e95672e3df00b2
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spelling	oai:doaj.org-article:67921aa485944365a2e95672e3df00b22021-12-02T18:24:54ZElectromechanical impedance based self-diagnosis of piezoelectric smart structure using principal component analysis and LibSVM10.1038/s41598-021-90567-y2045-2322https://doaj.org/article/67921aa485944365a2e95672e3df00b22021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90567-yhttps://doaj.org/toc/2045-2322Abstract The long-term use of a piezoelectric smart structure make it difficult to judge whether the structure or piezoelectric lead zirconate titanate (PZT) is damaged when the signal changes. If the sensor fault occurs, the cases and degrees of the fault are unknown based on the electromechanical impedance method. Therefore, after the principal component analysis (PCA) of six characteristic indexes, a two-component solution that could explain 99.2% of the variance in the original indexes was obtained to judge whether the damage comes from the PZT. Then LibSVM was used to make an effective identification of four sensor faults (pseudo soldering, debonding, wear, and breakage) and their three damage degrees. The result shows that the identification accuracy of damaged PZT reached 97.5%. The absolute scores of PCA comprehensive evaluation for structural damages are less than 0.5 while for sensor faults are greater than 0.6. By comparing the scores of the samples under unknown conditions with the set threshold, whether the sensor faults occur is effectively judged; the intact and 12 possible damage states of PZT can be all classified correctly with the model trained by LibSVM. It is feasible to use LibSVM to classify the cases and degrees of sensor faults.Xie JiangXin ZhangTao TangYuxiang ZhangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xie Jiang Xin Zhang Tao Tang Yuxiang Zhang Electromechanical impedance based self-diagnosis of piezoelectric smart structure using principal component analysis and LibSVM
description	Abstract The long-term use of a piezoelectric smart structure make it difficult to judge whether the structure or piezoelectric lead zirconate titanate (PZT) is damaged when the signal changes. If the sensor fault occurs, the cases and degrees of the fault are unknown based on the electromechanical impedance method. Therefore, after the principal component analysis (PCA) of six characteristic indexes, a two-component solution that could explain 99.2% of the variance in the original indexes was obtained to judge whether the damage comes from the PZT. Then LibSVM was used to make an effective identification of four sensor faults (pseudo soldering, debonding, wear, and breakage) and their three damage degrees. The result shows that the identification accuracy of damaged PZT reached 97.5%. The absolute scores of PCA comprehensive evaluation for structural damages are less than 0.5 while for sensor faults are greater than 0.6. By comparing the scores of the samples under unknown conditions with the set threshold, whether the sensor faults occur is effectively judged; the intact and 12 possible damage states of PZT can be all classified correctly with the model trained by LibSVM. It is feasible to use LibSVM to classify the cases and degrees of sensor faults.
format	article
author	Xie Jiang Xin Zhang Tao Tang Yuxiang Zhang
author_facet	Xie Jiang Xin Zhang Tao Tang Yuxiang Zhang
author_sort	Xie Jiang
title	Electromechanical impedance based self-diagnosis of piezoelectric smart structure using principal component analysis and LibSVM
title_short	Electromechanical impedance based self-diagnosis of piezoelectric smart structure using principal component analysis and LibSVM
title_full	Electromechanical impedance based self-diagnosis of piezoelectric smart structure using principal component analysis and LibSVM
title_fullStr	Electromechanical impedance based self-diagnosis of piezoelectric smart structure using principal component analysis and LibSVM
title_full_unstemmed	Electromechanical impedance based self-diagnosis of piezoelectric smart structure using principal component analysis and LibSVM
title_sort	electromechanical impedance based self-diagnosis of piezoelectric smart structure using principal component analysis and libsvm
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/67921aa485944365a2e95672e3df00b2
work_keys_str_mv	AT xiejiang electromechanicalimpedancebasedselfdiagnosisofpiezoelectricsmartstructureusingprincipalcomponentanalysisandlibsvm AT xinzhang electromechanicalimpedancebasedselfdiagnosisofpiezoelectricsmartstructureusingprincipalcomponentanalysisandlibsvm AT taotang electromechanicalimpedancebasedselfdiagnosisofpiezoelectricsmartstructureusingprincipalcomponentanalysisandlibsvm AT yuxiangzhang electromechanicalimpedancebasedselfdiagnosisofpiezoelectricsmartstructureusingprincipalcomponentanalysisandlibsvm
_version_	1718378100396392448

Electromechanical impedance based self-diagnosis of piezoelectric smart structure using principal component analysis and LibSVM

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