School of Civil Engineering, Qingdao University of Technology, Qingdao 266033, China.
Faculty of Engineering, Universidade Lusófona, 1749-024 Lisbon, Portugal.
Sensors (Basel). 2019 Oct 2;19(19):4280. doi: 10.3390/s19194280.
This paper proposes a methodology to process and interpret the complex signals acquired from the health monitoring of civil structures via scale-space empirical wavelet transform (EWT). The FREEVIB method, a widely used instantaneous modal parameters identification method, determines the structural characteristics from the individual components separated by EWT first. The scale-space EWT turns the detecting of the frequency boundaries into the scale-space representation of the Fourier spectrum. As well, to find meaningful modes becomes a clustering problem on the length of minima scale-space curves. The Otsu's algorithm is employed to determine the threshold for the clustering analysis. To retain the time-varying features, the EWT-extracted mono-components are analyzed by the FREEVIB method to obtain the instantaneous modal parameters and the linearity characteristics of the structures. Both simulated and real SHM signals from civil structures are used to validate the effectiveness of the present method. The results demonstrate that the proposed methodology is capable of separating the signal components, even those closely spaced ones in frequency domain, with high accuracy, and extracting the structural features reliably.
本文提出了一种通过尺度空间经验小波变换（EWT）处理和解释从民用结构健康监测中获取的复杂信号的方法。FREEVIB 方法是一种广泛使用的瞬时模态参数识别方法，首先通过 EWT 分离单个分量来确定结构特征。尺度空间 EWT 将检测频率边界转换为傅里叶谱的尺度空间表示。同样，找到有意义的模式成为了在极小尺度空间曲线上的长度上的聚类问题。Otsu 算法用于确定聚类分析的阈值。为了保留时变特征，通过 FREEVIB 方法对 EWT 提取的单分量进行分析，以获得结构的瞬时模态参数和线性特征。本文使用来自民用结构的模拟和真实的 SHM 信号来验证该方法的有效性。结果表明，所提出的方法能够以高精度分离信号分量，即使是在频域中紧密间隔的分量，并可靠地提取结构特征。
