文献类型：期刊文献

英文题名：Bayesian hierarchical hyper-Laplacian priors for high-resolution defect imaging in pipe structures

作者：Hu, Yue[1,2];Jiang, Xiaoqian[1,2];Zhu, Yanping[3];Cao, Shuai[4];Cui, Fangsen[4];Li, Fucai[5];Gao, Yang[1,2,6];Xuan, Fu-zhen[1,2]

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]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China;[4]ASTAR, Inst High Performance Comp, Singapore, Singapore;[5]Shanghai Jiao Tong Univ, State Key Lab Mech Syst & Vibrat, Shanghai, Peoples R China;[6]Wuhan Text Univ, State Key Lab New Text Mat & Adv Proc Technol, Wuhan, Peoples R China

年份：2024

卷号：214

外文期刊名：MECHANICAL SYSTEMS AND SIGNAL PROCESSING

收录：;EI(收录号：20241515867023);WOS:【SCI-EXPANDED(收录号:WOS:001224146200001)】；

基金：This work was supported by the National Natural Science Foundation of China (Grants No. 52105113 and 52175104) , the Fundamental Research Funds for the Central Universities, State Key Laboratory of New Textile Materials and Advanced Processing Technologies (Grant No. FZ2022006) .

语种：英文

外文关键词：Defect imaging; Bayesian hierarchical hyper -Laplacian; Pipe structure; Guided wave

摘要：The sparse defect imaging using guided waves for pipe structures has attracted a lot of attention due to its intuitive way of defect localization. However, there exist few attempts to employ the lp norm (p < 1) in sparse imaging to improve imaging performance. In this study, a new sparse imaging method based on Bayesian hierarchical hyper-Laplacian prior is proposed for pipe defect detection. A hyper-Laplacian prior is considered to obtain a sparser resolution, which aims at improving the imaging performance including defect imaging resolution and defect detection accuracy. A solid theoretical framework is constructed and derived to represent the hyperLaplacian prior and to adaptively estimate all model parameters. Meanwhile, a two-step discretizing strategy is designed to reduce the computational cost. The experimental results demonstrate the superiority of the proposed method.