文献类型：期刊文献

英文题名：Complex Bayesian group Lasso for defect imaging with guided waves

作者：Hu, Yue[1,2];Zhu, Yanping[3];Cui, Fangsen[4];Xiao, Jing[4];Cao, Shuai[4];Li, Fucai[3,5];Bao, Wenjie[3]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, Shanghai Key Lab Intelligent Sensing & Detect Tech, Shanghai, Peoples R China;[3]Shanghai Jiao Tong Univ, State Key Lab Mech Syst & Vibrat, Shanghai, Peoples R China;[4]ASTAR, Inst High Performance Comp, Singapore, Singapore;[5]Shanghai Jiao Tong Univ, State Key Lab Mech Syst & Vibrat, 800 Dongchuan Rd, Shanghai 200240, Peoples R China

年份：2023

卷号：22

期号：4

起止页码：2597

外文期刊名：STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL

收录：;EI(收录号：20224713136681);WOS:【SCI-EXPANDED(收录号:WOS:000884375500001)】；

基金：The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China (Grant Nos. 52105113 and 52175104), the Fundamental Research Funds for the Central Universities, and the Agency for Science, Technology and Research (A*STAR) Singapore under its RIE2020 AME Industry Alignment Fund - Pre-positioning Programme (IAF-PP) (Grant Nos. A20F5a0043, A19F1a0104, A19C9a0044).

语种：英文

外文关键词：Defect imaging; guided wave; sparse Bayesian learning; group Lasso; defect scattering

摘要：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.