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A frequency-based approach for the detection and classification of structural changes using t-SNE

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10.3390/s19235097
 
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hdl:2117/172868

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Agis Cherta, DavidMés informacióMés informació
Pozo Montero, FrancescMés informacióMés informacióMés informació
Document typeArticle
Defense date2019-11-21
PublisherMultidisciplinary Digital Publishing Institute (MDPI)
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 3.0 Spain
This work is protected by the corresponding intellectual and industrial property rights. Except where otherwise noted, its contents are licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
This work presents a structural health monitoring (SHM) approach for the detection and classification of structural changes. The proposed strategy is based on t-distributed stochastic neighbor embedding (t-SNE), a nonlinear procedure that is able to represent the local structure of high-dimensional data in a low-dimensional space. The steps of the detection and classification procedure are: (i) the data collected are scaled using mean-centered group scaling (MCGS); (ii) then principal component analysis (PCA) is applied to reduce the dimensionality of the data set; (iii) t-SNE is applied to represent the scaled and reduced data as points in a plane defining as many clusters as different structural states; and (iv) the current structure to be diagnosed will be associated with a cluster or structural state based on three strategies: (a) the smallest point-centroid distance; (b) majority voting; and (c) the sum of the inverse distances. The combination of PCA and t-SNE improves the quality of the clusters related to the structural states. The method is evaluated using experimental data from an aluminum plate with four piezoelectric transducers (PZTs). Results are illustrated in frequency domain, and they manifest the high classification accuracy and the strong performance of this method.
CitationAgis, D.; Pozo, F. A frequency-based approach for the detection and classification of structural changes using t-SNE. "Sensors", 21 Novembre 2019, vol. 2019, núm. 19, p. 1-26. 
URIhttp://hdl.handle.net/2117/172868
DOI10.3390/s19235097
ISSN1424-8220
Publisher versionhttps://www.mdpi.com/1424-8220/19/23/5097
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  • Doctorat en Matemàtica Aplicada - Articles de revista [112]
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