{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T21:55:45Z","timestamp":1781301345295,"version":"3.54.1"},"reference-count":34,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T00:00:00Z","timestamp":1621296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["109-2636-E-002-019, 109-2224-E-007-003, 109-2221-E-033-001"],"award-info":[{"award-number":["109-2636-E-002-019, 109-2224-E-007-003, 109-2221-E-033-001"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006477","name":"National Taiwan University","doi-asserted-by":"publisher","award":["108L891102, 109L891002"],"award-info":[{"award-number":["108L891102, 109L891002"]}],"id":[{"id":"10.13039\/501100006477","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019 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.<\/jats:p>","DOI":"10.3390\/s21103514","type":"journal-article","created":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T12:17:16Z","timestamp":1621340236000},"page":"3514","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["High-Dimensional Phase Space Reconstruction with a Convolutional Neural Network for Structural Health Monitoring"],"prefix":"10.3390","volume":"21","author":[{"given":"Yen-Lin","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, National Taiwan University, Taipei City 10617, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Chiang","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, National Taiwan University, Taipei City 10617, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9789-1476","authenticated-orcid":false,"given":"Pei-Hsin","family":"Chiu","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, National Taiwan University, Taipei City 10617, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"I-Chen","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Chung Yuan Christian University, Taoyuan City 320314, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Bai","family":"Xiao","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, National Taiwan University, Taipei City 10617, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu-Wei","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, National Taiwan University, Taipei City 10617, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang-Wei","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Chung Yuan Christian University, Taoyuan City 320314, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,18]]},"reference":[{"key":"ref_1","first-page":"617410","article-title":"Sensor placement optimization in structural health monitoring using genetic and evolutionary algorithms","volume":"Volume 6174","author":"Gao","year":"2006","journal-title":"Smart Structures and Materials 2006: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Manthei, G., and Plenkers, K. 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