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A Damage Classification Approach for Structural Health Monitoring Using Machine Learning
Inspection strategies with guided wave-based approaches give to structural health monitoring (SHM) applications several advantages, among them, the possibility of the use of real data from the structure which enables continuous monitoring and online damage identification. These kinds of inspection s...
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Published in: | Complexity (New York, N.Y.) N.Y.), 2018-01, Vol.2018 (2018), p.1-14 |
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description | Inspection strategies with guided wave-based approaches give to structural health monitoring (SHM) applications several advantages, among them, the possibility of the use of real data from the structure which enables continuous monitoring and online damage identification. These kinds of inspection strategies are based on the fact that these waves can propagate over relatively long distances and are able to interact sensitively with and uniquely with different types of defects. The principal goal for SHM is oriented to the development of efficient methodologies to process these data and provide results associated with the different levels of the damage identification process. As a contribution, this work presents a damage detection and classification methodology which includes the use of data collected from a structure under different structural states by means of a piezoelectric sensor network taking advantage of the use of guided waves, hierarchical nonlinear principal component analysis (h-NLPCA), and machine learning. The methodology is evaluated and tested in two structures: (i) a carbon fibre reinforced polymer (CFRP) sandwich structure with some damages on the multilayered composite sandwich structure and (ii) a CFRP composite plate. Damages in the structures were intentionally produced to simulate different damage mechanisms, that is, delamination and cracking of the skin. |
doi_str_mv | 10.1155/2018/5081283 |
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These kinds of inspection strategies are based on the fact that these waves can propagate over relatively long distances and are able to interact sensitively with and uniquely with different types of defects. The principal goal for SHM is oriented to the development of efficient methodologies to process these data and provide results associated with the different levels of the damage identification process. As a contribution, this work presents a damage detection and classification methodology which includes the use of data collected from a structure under different structural states by means of a piezoelectric sensor network taking advantage of the use of guided waves, hierarchical nonlinear principal component analysis (h-NLPCA), and machine learning. The methodology is evaluated and tested in two structures: (i) a carbon fibre reinforced polymer (CFRP) sandwich structure with some damages on the multilayered composite sandwich structure and (ii) a CFRP composite plate. Damages in the structures were intentionally produced to simulate different damage mechanisms, that is, delamination and cracking of the skin.</description><identifier>ISSN: 1076-2787</identifier><identifier>EISSN: 1099-0526</identifier><identifier>DOI: 10.1155/2018/5081283</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Algorithms ; Analysis ; Aprenentatge automàtic ; Artificial intelligence ; Carbon fiber reinforced plastics ; Classification ; Composite structures ; Cracking (fracturing) ; Damage detection ; Enginyeria electrònica ; Fiber composites ; Fiber reinforced polymers ; Fracture mechanics ; Identification ; Inspection ; Machine learning ; Methods ; Nonlinear analysis ; Pattern recognition ; Piezoelectricity ; Plates (structural members) ; Principal components analysis ; Resistència estructural ; Sandwich structures ; Sensors ; Structural damage ; Structural health monitoring ; Wavelet transforms ; Àrees temàtiques de la UPC</subject><ispartof>Complexity (New York, N.Y.), 2018-01, Vol.2018 (2018), p.1-14</ispartof><rights>Copyright © 2018 Diego Tibaduiza et al.</rights><rights>COPYRIGHT 2018 John Wiley & Sons, Inc.</rights><rights>Copyright © 2018 Diego Tibaduiza et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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These kinds of inspection strategies are based on the fact that these waves can propagate over relatively long distances and are able to interact sensitively with and uniquely with different types of defects. The principal goal for SHM is oriented to the development of efficient methodologies to process these data and provide results associated with the different levels of the damage identification process. As a contribution, this work presents a damage detection and classification methodology which includes the use of data collected from a structure under different structural states by means of a piezoelectric sensor network taking advantage of the use of guided waves, hierarchical nonlinear principal component analysis (h-NLPCA), and machine learning. The methodology is evaluated and tested in two structures: (i) a carbon fibre reinforced polymer (CFRP) sandwich structure with some damages on the multilayered composite sandwich structure and (ii) a CFRP composite plate. 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These kinds of inspection strategies are based on the fact that these waves can propagate over relatively long distances and are able to interact sensitively with and uniquely with different types of defects. The principal goal for SHM is oriented to the development of efficient methodologies to process these data and provide results associated with the different levels of the damage identification process. As a contribution, this work presents a damage detection and classification methodology which includes the use of data collected from a structure under different structural states by means of a piezoelectric sensor network taking advantage of the use of guided waves, hierarchical nonlinear principal component analysis (h-NLPCA), and machine learning. The methodology is evaluated and tested in two structures: (i) a carbon fibre reinforced polymer (CFRP) sandwich structure with some damages on the multilayered composite sandwich structure and (ii) a CFRP composite plate. 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subjects | Algorithms Analysis Aprenentatge automàtic Artificial intelligence Carbon fiber reinforced plastics Classification Composite structures Cracking (fracturing) Damage detection Enginyeria electrònica Fiber composites Fiber reinforced polymers Fracture mechanics Identification Inspection Machine learning Methods Nonlinear analysis Pattern recognition Piezoelectricity Plates (structural members) Principal components analysis Resistència estructural Sandwich structures Sensors Structural damage Structural health monitoring Wavelet transforms Àrees temàtiques de la UPC |
title | A Damage Classification Approach for Structural Health Monitoring Using Machine Learning |
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