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结构设计中重要的临界频率范围小于10 Hz，从基于傅立叶的PSD、FDM的边际谱和EMD的边际谱可以看出，该数据中几乎所有的能量都在10 Hz 以内，如图13所示。FDM增强了TFE跟踪，因为它提供了不同波如何从中心传播到记录站的更好和更精细的细节，例如振幅小但频率范围较高(10–20 Hz)的压缩波、振幅最强且频率范围较低(5 Hz 以下)的剪切波和表面波，它们造成了大部分损害，以及在整个数据跨度期间存在的其他体剪切波。在离散时间信号的情况下，只受采样率的影响，可以获得任意精度的时间和频率分辨率。

4. Multivariate Fourier decomposition method4. 多元傅里叶分解法

3. The Fourier decomposition method3. 傅里叶分解方法We propose a class of functions termed as the FIBFs, belonging to C∞[a,b], here with the following formal definition.Definition 3.1. Let x(t) be an arbitrary signal, defined in the interval [a,b], following t

Brief overview of the analytic signal and empirical mode decomposition algorithm 2.简要概述了解析信号和经验模态分解算法The EMD is a well-established adaptive signal decomposition algorithm that decomposes a nonlinear and non-stationary data into a set of finite band-limi.

【Abstract】For many decades, there has been a general perception in the literature that Fourier methods are not suitable for the analysis of nonlinear and non-stationary data. In this paper, we propose a novel and adaptive Fourier decomposition method (FDM)