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Guided Wave-Convolutional Neural Network Based Fatigue Crack Diagnosis of Aircraft Structures †
Fatigue crack diagnosis (FCD) is of great significance for ensuring safe operation, prolonging service time and reducing maintenance cost in aircrafts and many other safety-critical systems. As a promising method, the guided wave (GW)-based structural health monitoring method has been widely investi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6721778/ https://www.ncbi.nlm.nih.gov/pubmed/31443323 http://dx.doi.org/10.3390/s19163567 |
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author | Xu, Liang Yuan, Shenfang Chen, Jian Ren, Yuanqiang |
author_facet | Xu, Liang Yuan, Shenfang Chen, Jian Ren, Yuanqiang |
author_sort | Xu, Liang |
collection | PubMed |
description | Fatigue crack diagnosis (FCD) is of great significance for ensuring safe operation, prolonging service time and reducing maintenance cost in aircrafts and many other safety-critical systems. As a promising method, the guided wave (GW)-based structural health monitoring method has been widely investigated for FCD. However, reliable FCD still meets challenges, because uncertainties in real engineering applications usually cause serious change both to the crack propagation itself and GW monitoring signals. As one of deep learning methods, convolutional neural network (CNN) owns the ability of fusing a large amount of data, extracting high-level feature expressions related to classification, which provides a potential new technology to be applied in the GW-structural health monitoring method for crack evaluation. To address the influence of dispersion on reliable FCD, in this paper, a GW-CNN based FCD method is proposed. In this method, multiple damage indexes (DIs) from multiple GW exciting-acquisition channels are extracted. A CNN is designed and trained to further extract high-level features from the multiple DIs and implement feature fusion for crack evaluation. Fatigue tests on a typical kind of aircraft structure are performed to validate the proposed method. The results show that the proposed method can effectively reduce the influence of uncertainties on FCD, which is promising for real engineering applications. |
format | Online Article Text |
id | pubmed-6721778 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-67217782019-09-10 Guided Wave-Convolutional Neural Network Based Fatigue Crack Diagnosis of Aircraft Structures † Xu, Liang Yuan, Shenfang Chen, Jian Ren, Yuanqiang Sensors (Basel) Article Fatigue crack diagnosis (FCD) is of great significance for ensuring safe operation, prolonging service time and reducing maintenance cost in aircrafts and many other safety-critical systems. As a promising method, the guided wave (GW)-based structural health monitoring method has been widely investigated for FCD. However, reliable FCD still meets challenges, because uncertainties in real engineering applications usually cause serious change both to the crack propagation itself and GW monitoring signals. As one of deep learning methods, convolutional neural network (CNN) owns the ability of fusing a large amount of data, extracting high-level feature expressions related to classification, which provides a potential new technology to be applied in the GW-structural health monitoring method for crack evaluation. To address the influence of dispersion on reliable FCD, in this paper, a GW-CNN based FCD method is proposed. In this method, multiple damage indexes (DIs) from multiple GW exciting-acquisition channels are extracted. A CNN is designed and trained to further extract high-level features from the multiple DIs and implement feature fusion for crack evaluation. Fatigue tests on a typical kind of aircraft structure are performed to validate the proposed method. The results show that the proposed method can effectively reduce the influence of uncertainties on FCD, which is promising for real engineering applications. MDPI 2019-08-15 /pmc/articles/PMC6721778/ /pubmed/31443323 http://dx.doi.org/10.3390/s19163567 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Xu, Liang Yuan, Shenfang Chen, Jian Ren, Yuanqiang Guided Wave-Convolutional Neural Network Based Fatigue Crack Diagnosis of Aircraft Structures † |
title | Guided Wave-Convolutional Neural Network Based Fatigue Crack Diagnosis of Aircraft Structures † |
title_full | Guided Wave-Convolutional Neural Network Based Fatigue Crack Diagnosis of Aircraft Structures † |
title_fullStr | Guided Wave-Convolutional Neural Network Based Fatigue Crack Diagnosis of Aircraft Structures † |
title_full_unstemmed | Guided Wave-Convolutional Neural Network Based Fatigue Crack Diagnosis of Aircraft Structures † |
title_short | Guided Wave-Convolutional Neural Network Based Fatigue Crack Diagnosis of Aircraft Structures † |
title_sort | guided wave-convolutional neural network based fatigue crack diagnosis of aircraft structures † |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6721778/ https://www.ncbi.nlm.nih.gov/pubmed/31443323 http://dx.doi.org/10.3390/s19163567 |
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