Quantitative identification of damage in composite structures using sparse sensor arrays and multi-domain-feature fusion of guided waves

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 classifi-cation. 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.

Automated summary

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.