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Title: 1883. Research on lamb wave wavenumber response and PZT 2D cross-shaped array based damage imaging method of aircraft composite structures
Authors: Qiu, L
Yuan, S
Su, Z 
Liu, B
Keywords: Composite structure
Damage imaging
Lamb wave
PZT 2D cross-shaped array
Spatial Fourier transform
Structural health monitoring
Issue Date: 2016
Publisher: Vibromechanika
Source: Journal of vibroengineering, 2016, v. 18, no. 1, p. 190-201 How to cite?
Journal: Journal of vibroengineering 
Abstract: In recent decay, composite materials have widely applied to important load-bearing structures of advanced aircrafts. On-line damage monitoring of composite structure is an important research topic in the field of Structural Health Monitoring (SHM). Among the existing SHM techniques for composite structures, Lamb wave and Piezoelectric Transducer (PZT) array based damage imaging methods have been widely studied because they have the advantages of high signal-to-noise ratio and high damage sensitivity, and they can be applied to large scale structure monitoring. In this paper, a new on-line damage imaging method of composite structure which is based on Lamb wave wavenumber response and PZT 2D cross-shaped array is proposed. In this method, the PZT 2D cross-shaped array constructed by two linear PZT arrays is placed on composite structure to acquire Lamb wave damage scattering signal in spatial domain on-line. For each linear PZT array, a wavenumber-time image of the damage scattering signal can be obtained by using spatial Fourier transform and a time scanning process. Based on the two wavenumber-time images, the wavenumbers of the damage scattering signal projecting at the two arrays can be obtained. By combining with the two projection wavenumbers, the damage can be localized without blind angle. The method validation performed on a carbon fiber composite laminate plate shows a good damage localization accuracy. This method is simple but efficiency and it can be applied to on-line damage monitoring of composite structure easily.
ISSN: 1392-8716
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