用于飞机复合结构撞击定位的多频概率成像融合技术

Deshuang Deng, Xu Zeng, Zhengyan Yang, Yu Yang, Sheng Zhang, Shuyi Ma, Hao Xu, Lei Yang, Zhanjun Wu
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引用次数: 0

摘要

由于撞击对飞机复合材料结构造成的内部几乎不可见的破坏是一个关键问题,因此撞击监测对飞机复合材料结构的完整性和可靠性至关重要。本文提出了一种多频概率成像融合方法,用于定位飞机复合材料结构受到的冲击。为了捕捉撞击信号,需要在结构上安装分布式传感器网络。然后使用连续小波变换 (CWT) 对撞击信号进行处理,以提取多频窄带λ波信号。利用归一化方差序列中采用的平均技术测量到达时间差(TDOA),这是冲击源的一个关键特征。随后,建立概率成像函数,并将各频率窄带λ波信号的 TDOA 作为特征输入,生成多频率概率成像结果。为了确定成像结果在每个频率上的性能,引入了一个效率指数,允许保留或放弃成像结果。利用保留的多频概率成像结果,拟议方法通过成像融合实现了撞击定位。在加劲飞机复合材料面板上进行了实验验证,并与现有的两种方法:双曲定位成像法和虚拟时间反转成像法进行了比较。结果表明,与现有方法相比,所提出的方法能显著提高定位精度,即使在存在测量噪声的情况下也很有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-frequency probabilistic imaging fusion for impact localization on aircraft composite structures
Since the internal barely visible damage of aircraft composite structures caused by the impact is a critical problem, impact monitoring is essential for the integrity and reliability of aircraft composite structures. This paper presents a multi-frequency probabilistic imaging fusion method for localizing impacts on aircraft composite structures. To capture the impact signals, a network of distributed sensors is mounted on the structure. The impact signals are then processed using the continuous wavelet transform (CWT) to extract the multi-frequency narrowband Lamb wave signals. The time difference of arrival (TDOA), a key feature of the impact source, is measured using averaging techniques employed in the normalized variance sequence. Subsequently, a probabilistic imaging function is established, and the TDOA of narrowband Lamb wave signals at each frequency is used as the feature input to generate the multi-frequency probabilistic imaging results. To determine the performance of the imaging results at each frequency, an efficiency index is introduced, allowing for the retention or abandonment of the imaging results. By utilizing the retained multi-frequency probabilistic imaging results, the proposed method achieves impact localization through imaging fusion. Experimental verification is conducted on a stiffened aircraft composite panel, and a comparison is made with two existing methods: the hyperbolic locus imaging method and the virtual time reversal imaging method. The results show that the proposed method can significantly improve localization accuracy compared to the existing methods, and is effective even in the presence of measurement noise.
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