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首页> 外文期刊>IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control >Lamb Wave Mode Decomposition Based on Cross-Wigner-Ville Distribution and Its Application to Anomaly Imaging for Structural Health Monitoring
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Lamb Wave Mode Decomposition Based on Cross-Wigner-Ville Distribution and Its Application to Anomaly Imaging for Structural Health Monitoring

机译：基于跨维格纳-维勒分布的兰姆波模式分解及其在结构健康监测异常成像中的应用

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摘要

Lamb waves are characterized by their multimodal and dispersive propagation, which often complicates analysis. This paper presents a method for separation of the mode components and reflected components in sensor signals in an active structural health monitoring (SHM) system. The system is trained using linear chirp signals but works for arbitrary excitation signals. The training process employs the cross-Wigner-Ville distribution (xWVD) of the excitation signal and the sensor signal to separate the temporally overlapped modes in the time-frequency domain. The mode decomposition method uses a ridge extraction algorithm to separate each signal component in the time-frequency distribution. Once the individual modes are separated in the time-frequency domain, they are reconstructed in the time domain using the inverse xWVD operation. The propagation impulse response associated with each component can be directly estimated for chirp inputs. The estimated propagation impulse response can be used to separate the modes resulting from arbitrary excitation signals as long as their frequency components fall in the range of the chirp signal. The usefulness of the mode decomposition algorithm is demonstrated on a new health monitoring system for composite structures. This system performs anomaly imaging using the first arriving mode extracted from sensor array signals acquired from the structure. The anomaly maps are computed using a sparse tomographic reconstruction algorithm. The reconstructed map can locate anomalies on the structure and estimate their boundaries. Comparisons with methods that do not employ mode decomposition and/or sparse reconstruction techniques indicate a substantially better performance for the method of this paper.
机译：兰姆波的特征在于其多峰和分散传播，这通常会使分析变得复杂。本文提出了一种在主动结构健康监测（SHM）系统中分离传感器信号中的模式分量和反射分量的方法。该系统使用线性线性调频信号进行训练，但适用于任意激励信号。训练过程使用激励信号和传感器信号的交叉Wigner-Ville分布（xWVD）来分离时频域中的时间重叠模式。模式分解方法使用脊提取算法来分离时频分布中的每个信号分量。一旦在时频域中分离了各个模式，便可以使用xWVD逆运算在时域中对其进行重构。对于线性调频输入，可以直接估计与每个分量相关的传播脉冲响应。只要其激励信号的频率分量落在线性调频信号的范围内，就可以将估计的传播脉冲响应用于分离由任意激励信号产生的模式。在新的复合结构健康监测系统上证明了模式分解算法的有用性。该系统使用从从结构获取的传感器阵列信号中提取的第一到达模式执行异常成像。使用稀疏层析重建算法计算异常图。重建的地图可以定位结构上的异常并估计其边界。与不采用模式分解和/或稀疏重构技术的方法进行比较表明，该方法的性能明显更好。

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