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A single frequency component-based re-estimated MUSIC algorithm for impact localization on complex composite structures

delete2015-09-10
delete30
PRE
AI
S
Shenfang Yuan *
Q
Qiao Bao
L
Lei Qiu
Y
Yongteng Zhong
DOI:10.1088/0964-1726/24/10/105021delete
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摘要

摘要

En 中文
The growing use of composite materials on aircraft structures has attracted much attention for impact monitoring as a kind of structural health monitoring (SHM) method. Multiple signal classification (MUSIC)-based monitoring technology is a promising method because of its directional scanning ability and easy arrangement of the sensor array. However, for applications on real complex structures, some challenges still exist. The impact-induced elastic waves usually exhibit a wide-band performance, giving rise to the difficulty in obtaining the phase velocity directly. In addition, composite structures usually have obvious anisotropy, and the complex structural style of real aircrafts further enhances this performance, which greatly reduces the localization precision of the MUSIC-based method. To improve the MUSIC-based impact monitoring method, this paper first analyzes and demonstrates the influence of measurement precision of the phase velocity on the localization results of the MUSIC impact localization method. In order to improve the accuracy of the phase velocity measurement, a single frequency component extraction method is presented. Additionally, a single frequency component-based re-estimated MUSIC (SFCBR-MUSIC) algorithm is proposed to reduce the localization error caused by the anisotropy of the complex composite structure. The proposed method is verified on a real composite aircraft wing box, which has T-stiffeners and screw holes. Three typical categories of 41 impacts are monitored. Experimental results show that the SFCBR-MUSIC algorithm can localize impact on complex composite structures with an obviously improved accuracy.
Keyword:
multiple signal classification
complex composite structure
impact localization
single frequency component
re-estimation
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Smart Materials and Structures 封面图
Smart Materials and Structures
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论文数:
8.5K
被引数:
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