文献类型：期刊文献

英文题名：Tensor robust principal component analysis based on Bayesian Tucker decomposition for thermographic inspection

作者：Hu, Yue[1,2];Cui, Fangsen[3];Zhao, Yifan[4];Li, Fucai[5];Cao, Shuai[3];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]ASTAR, Inst High Performance Comp, Singapore, Singapore;[4]Cranfield Univ, Sch Aerosp Transport & Mfg, Cranfield, England;[5]Shanghai Jiao Tong Univ, State Key Lab Mech Syst & Vibrat, Shanghai, Peoples R China

年份：2023

卷号：204

外文期刊名：MECHANICAL SYSTEMS AND SIGNAL PROCESSING

收录：;EI(收录号：20233814735648);WOS:【SCI-EXPANDED(收录号:WOS:001149740900001)】；

基金：This work was supported by the National Natural Science Foundation of China (Grants Nos. 52105113 and 52175104) and the Fundamental Research Funds for the Central Universities. The authors thank Prof. Bin Gao with the University of Electronic Science and Technology, China, for his valuable help in the EJSLRMD algorithm.

语种：英文

外文关键词：Tensor robust principal component analysis; Bayesian Tucker decomposition; Thermographic inspection; Nondestructive testing; Generalized student-t distribution

摘要：Thermographic inspection is considered an effective and promising nondestructive testing tool because of its intuitiveness, wide range and noncontact property. Despite this, the detection of weak defects and the recovery of their shape remain difficult, particularly when the surface being inspected is the opposite of the surface being drilled. This study proposes a new tensor robust principal component analysis method based on Bayesian Tucker decomposition to improve the spatial resolution of thermography. A hierarchical form of a generalized Student-t prior is imposed on the model parameters in the Bayesian framework so as to approximate the low-rank component related to the defect feature. Through variational Bayesian inference, all model parameters are adaptively estimated. Based on two experimental data, it appears that the proposed method is capable of improving the spatial resolution and detection accuracy of the thermographic inspection system.