TY - JOUR
T1 - Quantitative identification of damage in composite structures using sparse sensor arrays and multi-domain-feature fusion of guided waves
AU - Tang, Lingquan
AU - Li, Yehai
AU - Bao, Qiao
AU - Hu, Weiwei
AU - Wang, Qiang
AU - Su, Zhongqing
AU - Yue, Dong
N1 - Funding Information:
The research described in this paper was financially supported by the National Natural Science Foundation of China (Grant No.52105152 and Grant No.12002172), the Postdoctoral Research Foundation of China (Grant No.2020M681680), and the Natural Science Foundation of Jiangsu Province (Grant No.BK20190739).
Funding Information:
Submitted to Measurement on Oct 5, 2022. The research described in this paper was financially supported by the National Natural Science Foundation of China (Grant No.52105152 and Grant No.12002172), the Postdoctoral Research Foundation of China (Grant No.2020M681680), and the Natural Science Foundation of Jiangsu Province (Grant No.BK20190739).
Publisher Copyright:
© 2023
PY - 2023/2/28
Y1 - 2023/2/28
N2 - Damage detection techniques using Lamb waves have shown excellent capabilities in the diagnosis of composite structures. However, structural health monitoring of composite structures is challenging, especially for damage classification. This study proposes a machine learning-based method with a sparse sensor array to achieve quantitative classification of the damage location and severity on a composite plate. First, multi features extraction is used to construct a support vector machine (SVM) damage localization model. Second, optimal path extraction combined with principal component analysis (PCA) is used to construct an SVM model for classification. To reduce the operational burden of structures, the sparse array is employed. To improve the damage classification accuracy, Fisher clustering is proposed to extract the optimal detection path. Then, PCA is used to achieve data fusion. Experimental results on a glass fiber-reinforced epoxy composite laminate plate demonstrate that the proposed technique can accurately locate and classify the quantitative artificial damage.
AB - Damage detection techniques using Lamb waves have shown excellent capabilities in the diagnosis of composite structures. However, structural health monitoring of composite structures is challenging, especially for damage classification. This study proposes a machine learning-based method with a sparse sensor array to achieve quantitative classification of the damage location and severity on a composite plate. First, multi features extraction is used to construct a support vector machine (SVM) damage localization model. Second, optimal path extraction combined with principal component analysis (PCA) is used to construct an SVM model for classification. To reduce the operational burden of structures, the sparse array is employed. To improve the damage classification accuracy, Fisher clustering is proposed to extract the optimal detection path. Then, PCA is used to achieve data fusion. Experimental results on a glass fiber-reinforced epoxy composite laminate plate demonstrate that the proposed technique can accurately locate and classify the quantitative artificial damage.
KW - Composite structures
KW - Lamb wave
KW - Quantitative classification
KW - Sparse sensor array
KW - Support vector machine (SVM)
UR - http://www.scopus.com/inward/record.url?scp=85146429599&partnerID=8YFLogxK
U2 - 10.1016/j.measurement.2023.112482
DO - 10.1016/j.measurement.2023.112482
M3 - Journal article
AN - SCOPUS:85146429599
SN - 0263-2241
VL - 208
JO - Measurement: Journal of the International Measurement Confederation
JF - Measurement: Journal of the International Measurement Confederation
M1 - 112482
ER -