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Vision-Based Structural Modal Identification Using Hybrid Motion Magnification

As a promising alternative to conventional contact sensors, vision-based technologies for a structural dynamic response measurement and health monitoring have attracted much attention from the research community. Among these technologies, Eulerian video magnification has a unique capability of analy...

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Detalles Bibliográficos
Autores principales: Zhang, Dashan, Zhu, Andong, Hou, Wenhui, Liu, Lu, Wang, Yuwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9739241/
https://www.ncbi.nlm.nih.gov/pubmed/36501990
http://dx.doi.org/10.3390/s22239287
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author Zhang, Dashan
Zhu, Andong
Hou, Wenhui
Liu, Lu
Wang, Yuwei
author_facet Zhang, Dashan
Zhu, Andong
Hou, Wenhui
Liu, Lu
Wang, Yuwei
author_sort Zhang, Dashan
collection PubMed
description As a promising alternative to conventional contact sensors, vision-based technologies for a structural dynamic response measurement and health monitoring have attracted much attention from the research community. Among these technologies, Eulerian video magnification has a unique capability of analyzing modal responses and visualizing modal shapes. To reduce the noise interference and improve the quality and stability of the modal shape visualization, this study proposes a hybrid motion magnification framework that combines linear and phase-based motion processing. Based on the assumption that temporal variations can represent spatial motions, the linear motion processing extracts and manipulates the temporal intensity variations related to modal responses through matrix decomposition and underdetermined blind source separation (BSS) techniques. Meanwhile, the theory of Fourier transform profilometry (FTP) is utilized to reduce spatial high-frequency noise. As all spatial motions in a video are linearly controllable, the subsequent phase-based motion processing highlights the motions and visualizes the modal shapes with a higher quality. The proposed method is validated by two laboratory experiments and a field test on a large-scale truss bridge. The quantitative evaluation results with high-speed cameras demonstrate that the hybrid method performs better than the single-step phase-based motion magnification method in visualizing sound-induced subtle motions. In the field test, the vibration characteristics of the truss bridge when a train is driving across the bridge are studied with a commercial camera over 400 m away from the bridge. Moreover, four full-field modal shapes of the bridge are successfully observed.
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spelling pubmed-97392412022-12-11 Vision-Based Structural Modal Identification Using Hybrid Motion Magnification Zhang, Dashan Zhu, Andong Hou, Wenhui Liu, Lu Wang, Yuwei Sensors (Basel) Article As a promising alternative to conventional contact sensors, vision-based technologies for a structural dynamic response measurement and health monitoring have attracted much attention from the research community. Among these technologies, Eulerian video magnification has a unique capability of analyzing modal responses and visualizing modal shapes. To reduce the noise interference and improve the quality and stability of the modal shape visualization, this study proposes a hybrid motion magnification framework that combines linear and phase-based motion processing. Based on the assumption that temporal variations can represent spatial motions, the linear motion processing extracts and manipulates the temporal intensity variations related to modal responses through matrix decomposition and underdetermined blind source separation (BSS) techniques. Meanwhile, the theory of Fourier transform profilometry (FTP) is utilized to reduce spatial high-frequency noise. As all spatial motions in a video are linearly controllable, the subsequent phase-based motion processing highlights the motions and visualizes the modal shapes with a higher quality. The proposed method is validated by two laboratory experiments and a field test on a large-scale truss bridge. The quantitative evaluation results with high-speed cameras demonstrate that the hybrid method performs better than the single-step phase-based motion magnification method in visualizing sound-induced subtle motions. In the field test, the vibration characteristics of the truss bridge when a train is driving across the bridge are studied with a commercial camera over 400 m away from the bridge. Moreover, four full-field modal shapes of the bridge are successfully observed. MDPI 2022-11-29 /pmc/articles/PMC9739241/ /pubmed/36501990 http://dx.doi.org/10.3390/s22239287 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Dashan
Zhu, Andong
Hou, Wenhui
Liu, Lu
Wang, Yuwei
Vision-Based Structural Modal Identification Using Hybrid Motion Magnification
title Vision-Based Structural Modal Identification Using Hybrid Motion Magnification
title_full Vision-Based Structural Modal Identification Using Hybrid Motion Magnification
title_fullStr Vision-Based Structural Modal Identification Using Hybrid Motion Magnification
title_full_unstemmed Vision-Based Structural Modal Identification Using Hybrid Motion Magnification
title_short Vision-Based Structural Modal Identification Using Hybrid Motion Magnification
title_sort vision-based structural modal identification using hybrid motion magnification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9739241/
https://www.ncbi.nlm.nih.gov/pubmed/36501990
http://dx.doi.org/10.3390/s22239287
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