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Optimal Sensor Placement in Reduced-Order Models Using Modal Constraint Conditions

Sensor measurements of civil structures provide basic information on their performance. However, it is impossible to install sensors at every location owing to the limited number of sensors available. Therefore, in this study, we propose an optimal sensor placement (OSP) algorithm while reducing the...

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Detalles Bibliográficos
Autores principales: Lee, Eun-Taik, Eun, Hee-Chang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8779765/
https://www.ncbi.nlm.nih.gov/pubmed/35062551
http://dx.doi.org/10.3390/s22020589
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author Lee, Eun-Taik
Eun, Hee-Chang
author_facet Lee, Eun-Taik
Eun, Hee-Chang
author_sort Lee, Eun-Taik
collection PubMed
description Sensor measurements of civil structures provide basic information on their performance. However, it is impossible to install sensors at every location owing to the limited number of sensors available. Therefore, in this study, we propose an optimal sensor placement (OSP) algorithm while reducing the system order by using the constraint condition between the master and slave modes from the target modes. The existing OSP methods are modified in this study, and an OSP approach using a constrained dynamic equation is presented. The validity and comparison of the proposed methods are illustrated by utilizing a numerical example that predicts the OSPs of the truss structure. It is observed that the proposed methods lead to different sensor layouts depending on the algorithm criteria. Thus, it can be concluded that the OSP algorithm meets the measurement requirements for various methods, such as structural damage detection, system identification, and vibration control.
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spelling pubmed-87797652022-01-22 Optimal Sensor Placement in Reduced-Order Models Using Modal Constraint Conditions Lee, Eun-Taik Eun, Hee-Chang Sensors (Basel) Article Sensor measurements of civil structures provide basic information on their performance. However, it is impossible to install sensors at every location owing to the limited number of sensors available. Therefore, in this study, we propose an optimal sensor placement (OSP) algorithm while reducing the system order by using the constraint condition between the master and slave modes from the target modes. The existing OSP methods are modified in this study, and an OSP approach using a constrained dynamic equation is presented. The validity and comparison of the proposed methods are illustrated by utilizing a numerical example that predicts the OSPs of the truss structure. It is observed that the proposed methods lead to different sensor layouts depending on the algorithm criteria. Thus, it can be concluded that the OSP algorithm meets the measurement requirements for various methods, such as structural damage detection, system identification, and vibration control. MDPI 2022-01-13 /pmc/articles/PMC8779765/ /pubmed/35062551 http://dx.doi.org/10.3390/s22020589 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
Lee, Eun-Taik
Eun, Hee-Chang
Optimal Sensor Placement in Reduced-Order Models Using Modal Constraint Conditions
title Optimal Sensor Placement in Reduced-Order Models Using Modal Constraint Conditions
title_full Optimal Sensor Placement in Reduced-Order Models Using Modal Constraint Conditions
title_fullStr Optimal Sensor Placement in Reduced-Order Models Using Modal Constraint Conditions
title_full_unstemmed Optimal Sensor Placement in Reduced-Order Models Using Modal Constraint Conditions
title_short Optimal Sensor Placement in Reduced-Order Models Using Modal Constraint Conditions
title_sort optimal sensor placement in reduced-order models using modal constraint conditions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8779765/
https://www.ncbi.nlm.nih.gov/pubmed/35062551
http://dx.doi.org/10.3390/s22020589
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