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A Lamb wave signal reconstruction method for high-resolution damage imaging

A Lamb wave signal reconstruction method for high-resolution damage imaging

作     者:Xiaopeng WANG Jian CAI Zhiquan ZHOU 

作者机构:State Key Lab of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and Astronautics 

出 版 物:《Chinese Journal of Aeronautics》 (中国航空学报(英文版))

年 卷 期:2019年第32卷第5期

页      面:1087-1099页

核心收录:

学科分类:08[工学] 0802[工学-机械工程] 0825[工学-航空宇航科学与技术] 0801[工学-力学(可授工学、理学学位)] 

基  金:supported by the Fundamental Research Funds for the Central Universities China(No.NS2016012) 

主  题:Damage imaging Dispersion compensation High resolution Lamb waves Signal reconstruction 

摘      要:In Lamb wave-based Structural Health Monitoring(SHM), a high-enough spatial resolution is highly required for Lamb wave signals to ensure the resolution and accuracy of damage detection. However, besides the dispersion characteristic, the signal spatial resolution is also largely restricted by the space duration of excitation waveforms, i.e., the Initial Spatial Resolution(ISR)for the signals before travelling. To resolve the problem of inferior signal spatial resolution of Lamb waves, a Lamb Wave Signal Reconstruction(LWSR) method is presented and applied for highresolution damage imaging in this paper. In LWSR, not only a new linearly-dispersive signal is reconstructed from an original Lamb wave signal, but also the group velocity at the central frequency is sufficiently decreased. Then, both dispersion compensation and ISR improvement can be realized to achieve a satisfying signal spatial resolution. After the frequency domain sensing model and spatial resolution of Lamb wave signals are firstly analyzed, the basic idea and numerical realization of LWSR are discussed. Numerical simulations are also implemented to preliminarily validate LWSR. Subsequently, LWSR-based high-resolution damage imaging is developed. An experiment of adjacent multiple damage identification is finally conducted to demonstrate the efficiency of LWSR and LWSR-based imaging methods.

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