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Principal Component Analysis Method with Space and Time Windows for Damage Detection

Long-term structural health monitoring (SHM) has become an important tool to ensure the safety of infrastructures. However, determining methods to extract valuable information from large amounts of data from SHM systems for effective identification of damage still remains a major challenge. This pap...

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Detalles Bibliográficos
Autores principales: Zhang, Ge, Tang, Liqun, Zhou, Licheng, Liu, Zejia, Liu, Yiping, Jiang, Zhenyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603565/
https://www.ncbi.nlm.nih.gov/pubmed/31159466
http://dx.doi.org/10.3390/s19112521
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author Zhang, Ge
Tang, Liqun
Zhou, Licheng
Liu, Zejia
Liu, Yiping
Jiang, Zhenyu
author_facet Zhang, Ge
Tang, Liqun
Zhou, Licheng
Liu, Zejia
Liu, Yiping
Jiang, Zhenyu
author_sort Zhang, Ge
collection PubMed
description Long-term structural health monitoring (SHM) has become an important tool to ensure the safety of infrastructures. However, determining methods to extract valuable information from large amounts of data from SHM systems for effective identification of damage still remains a major challenge. This paper provides a novel effective method for structural damage detection by introduction of space and time windows in the traditional principal component analysis (PCA) technique. Numerical results with a planar beam model demonstrate that, due to the presence of space and time windows, the proposed double-window PCA method (DWPCA) has a higher sensitivity for damage identification than the previous method moving PCA (MPCA), which combines only time windows with PCA. Further studies indicate that the developed approach, as compared to the MPCA method, has a higher resolution in localizing damage by space windows and also in quantitative evaluation of damage severity. Finally, a finite-element model of a practical bridge is used to prove that the proposed DWPCA method has greater sensitivity for damage detection than traditional methods and potential for applications in practical engineering.
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spelling pubmed-66035652019-07-17 Principal Component Analysis Method with Space and Time Windows for Damage Detection Zhang, Ge Tang, Liqun Zhou, Licheng Liu, Zejia Liu, Yiping Jiang, Zhenyu Sensors (Basel) Article Long-term structural health monitoring (SHM) has become an important tool to ensure the safety of infrastructures. However, determining methods to extract valuable information from large amounts of data from SHM systems for effective identification of damage still remains a major challenge. This paper provides a novel effective method for structural damage detection by introduction of space and time windows in the traditional principal component analysis (PCA) technique. Numerical results with a planar beam model demonstrate that, due to the presence of space and time windows, the proposed double-window PCA method (DWPCA) has a higher sensitivity for damage identification than the previous method moving PCA (MPCA), which combines only time windows with PCA. Further studies indicate that the developed approach, as compared to the MPCA method, has a higher resolution in localizing damage by space windows and also in quantitative evaluation of damage severity. Finally, a finite-element model of a practical bridge is used to prove that the proposed DWPCA method has greater sensitivity for damage detection than traditional methods and potential for applications in practical engineering. MDPI 2019-06-02 /pmc/articles/PMC6603565/ /pubmed/31159466 http://dx.doi.org/10.3390/s19112521 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Ge
Tang, Liqun
Zhou, Licheng
Liu, Zejia
Liu, Yiping
Jiang, Zhenyu
Principal Component Analysis Method with Space and Time Windows for Damage Detection
title Principal Component Analysis Method with Space and Time Windows for Damage Detection
title_full Principal Component Analysis Method with Space and Time Windows for Damage Detection
title_fullStr Principal Component Analysis Method with Space and Time Windows for Damage Detection
title_full_unstemmed Principal Component Analysis Method with Space and Time Windows for Damage Detection
title_short Principal Component Analysis Method with Space and Time Windows for Damage Detection
title_sort principal component analysis method with space and time windows for damage detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603565/
https://www.ncbi.nlm.nih.gov/pubmed/31159466
http://dx.doi.org/10.3390/s19112521
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