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High-Dimensional Phase Space Reconstruction with a Convolutional Neural Network for Structural Health Monitoring
In order to accurately diagnose the health of high-order statically indeterminate structures, most existing structural health monitoring (SHM) methods require multiple sensors to collect enough information. However, comprehensive data collection from multiple sensors for high degree-of-freedom struc...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8158099/ https://www.ncbi.nlm.nih.gov/pubmed/34070068 http://dx.doi.org/10.3390/s21103514 |
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author | Chen, Yen-Lin Chiang, Yuan Chiu, Pei-Hsin Huang, I-Chen Xiao, Yu-Bai Chang, Shu-Wei Huang, Chang-Wei |
author_facet | Chen, Yen-Lin Chiang, Yuan Chiu, Pei-Hsin Huang, I-Chen Xiao, Yu-Bai Chang, Shu-Wei Huang, Chang-Wei |
author_sort | Chen, Yen-Lin |
collection | PubMed |
description | In order to accurately diagnose the health of high-order statically indeterminate structures, most existing structural health monitoring (SHM) methods require multiple sensors to collect enough information. However, comprehensive data collection from multiple sensors for high degree-of-freedom structures is not typically available in practice. We propose a method that reconciles the two seemingly conflicting difficulties. Takens’ embedding theorem is used to augment the dimensions of data collected from a single sensor. Taking advantage of the success of machine learning in image classification, high-dimensional reconstructed attractors were converted into images and fed into a convolutional neural network (CNN). Attractor classification was performed for 10 damage cases of a 3-story shear frame structure. Numerical results show that the inherently high dimension of the CNN model allows the handling of higher dimensional data. Information on both the level and the location of damage was successfully embedded. The same methodology will allow the extraction of data with unsupervised CNN classification to be consistent with real use cases. |
format | Online Article Text |
id | pubmed-8158099 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81580992021-05-28 High-Dimensional Phase Space Reconstruction with a Convolutional Neural Network for Structural Health Monitoring Chen, Yen-Lin Chiang, Yuan Chiu, Pei-Hsin Huang, I-Chen Xiao, Yu-Bai Chang, Shu-Wei Huang, Chang-Wei Sensors (Basel) Article In order to accurately diagnose the health of high-order statically indeterminate structures, most existing structural health monitoring (SHM) methods require multiple sensors to collect enough information. However, comprehensive data collection from multiple sensors for high degree-of-freedom structures is not typically available in practice. We propose a method that reconciles the two seemingly conflicting difficulties. Takens’ embedding theorem is used to augment the dimensions of data collected from a single sensor. Taking advantage of the success of machine learning in image classification, high-dimensional reconstructed attractors were converted into images and fed into a convolutional neural network (CNN). Attractor classification was performed for 10 damage cases of a 3-story shear frame structure. Numerical results show that the inherently high dimension of the CNN model allows the handling of higher dimensional data. Information on both the level and the location of damage was successfully embedded. The same methodology will allow the extraction of data with unsupervised CNN classification to be consistent with real use cases. MDPI 2021-05-18 /pmc/articles/PMC8158099/ /pubmed/34070068 http://dx.doi.org/10.3390/s21103514 Text en © 2021 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 Chen, Yen-Lin Chiang, Yuan Chiu, Pei-Hsin Huang, I-Chen Xiao, Yu-Bai Chang, Shu-Wei Huang, Chang-Wei High-Dimensional Phase Space Reconstruction with a Convolutional Neural Network for Structural Health Monitoring |
title | High-Dimensional Phase Space Reconstruction with a Convolutional Neural Network for Structural Health Monitoring |
title_full | High-Dimensional Phase Space Reconstruction with a Convolutional Neural Network for Structural Health Monitoring |
title_fullStr | High-Dimensional Phase Space Reconstruction with a Convolutional Neural Network for Structural Health Monitoring |
title_full_unstemmed | High-Dimensional Phase Space Reconstruction with a Convolutional Neural Network for Structural Health Monitoring |
title_short | High-Dimensional Phase Space Reconstruction with a Convolutional Neural Network for Structural Health Monitoring |
title_sort | high-dimensional phase space reconstruction with a convolutional neural network for structural health monitoring |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8158099/ https://www.ncbi.nlm.nih.gov/pubmed/34070068 http://dx.doi.org/10.3390/s21103514 |
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