Sparse identification of multiple dispersive guided-wave modes in plate structures
Guided-wave(GW)testing has been widely used to inspect engineering structures,there are still major challenges associated with its applications,mostly originate from the dispersive and multi-mode nature of GW signals as well as noise contamination.To deal with these challenges,an effective signal processing technique is introduced that enables one to accurately identify and recover multiple modes from noisy dispersive GW signals.To characterize the dispersion phenomenon,the chirp model is introduced first,based on this which an over-complete dictionary is designed by considering all possible defect locations.Then the prior knowledge that only a small number of modes are included in the GW signals is exploited by a robust sparse Bayesian learning(SBL)framework.After sparse signal decomposition,the information of propagation paths of GW modes are used to identify each GW mode.Illustrative results from experiment studies on plate structures are presented to demonstrate the capability of the proposed method.
guided wave dispersion signal processing sparse Bayesian learning nondestructive testing
Biao Wu Yong Huang Hui Li
College of Civil Engineering,Nanjing Tech University,Nanjing,China School of Civil Engineering,Harbin Institute of Technology,Harbin,China
国际会议
The 7th World Conference on Structural Control and Monitoring(7WCSCM)(第七届结构控制与监测世界大会)
青岛
英文
1833-1840
2018-07-22（万方平台首次上网日期，不代表论文的发表时间）