Complex Bayesian group Lasso for defect imaging with guided waves. (July 2023)

• Record Type: Journal Article
• Authors: Hu, Yue; Zhu, Yanping; Cui, Fangsen; Xiao, Jing; Cao, Shuai; Li, Fucai; Bao, Wenjie
• Is Part Of: Structural health monitoring. Volume 22:Number 4(2023)
• Page Start: 2597
• Page End: 2608
• Publication Date: 2023-07
• Subjects: Defect imaging -- guided wave -- sparse Bayesian learning -- group Lasso -- defect scattering; Structural health monitoring -- Periodicals; Structural stability -- Periodicals; Strength of materials -- Periodicals; Nondestructive testing -- Periodicals; Constructions -- Stabilité -- Périodiques; Résistance des matériaux -- Périodiques; Contrôle non destructif -- Périodiques; Electronic journals; 624.17
• DOI: 10.1177/14759217221130132
• Languages: English
• ISSNs: 1475-9217

Abstract

The defect imaging based on guided wave provides an intuitive way for defect localization. Recently, sparse representation methods based on the damage sparsity assumption have been developed for defect imaging, where few sensors are used in these methods. However, these sparse imaging methods need repeatedly tuning the regularization parameter to obtain a good imaging performance. In this paper, an adaptive method based on complex Bayesian group Lasso is developed for localizing the damage. A group Lasso model is constructed to represent the defect imaging problem, and formulated by a sparse Bayesian learning (SBL) framework, where a hierarchical model of a Laplace prior is built to represent the group Lasso regularization. Estimations of the model variables are derived by using variational inference. In the proposed method, the model parameters are automatically updated without needing priori information. The effectiveness of the proposed method is verified by analyzing an experimental data.