中文说明：经验模态分解（Empirical Mode Decomposition，简称EMD))方法被认为是2000年来以傅立叶变换为基础的线性和稳态频谱分析的一个重大突破 [1] ，该方法是依据数据自身的时间尺度特征来进行信号分解，无须预先设定任何基函数。这一点与建立在先验性的谐波基函数和小波基函数上的傅里叶分解与小波分解方法具有本质性的差别。正是由于这样的特点，EMD 方法在理论上可以应用于任何类型的信号的分解， 因而在处理非平稳及非线性数据上，具有非常明显的优势,适合于分析非线性、非平稳信号序列，具有很高的信噪比。所以，EMD方法一经提出就在不同的工程领域得到了迅速有效的应用，例如用在海洋、大气、天体观测资料与地震记录分析、机械故障诊断、密频动力系统的阻尼识别以及大型土木工程结构的模态参数识别方面。
English Description：
Empirical mode decomposition (EMD) method is considered to be a major breakthrough in linear and steady-state spectrum analysis based on Fourier transform since 2000. This is essentially different from Fourier decomposition and wavelet decomposition based on prior harmonic and wavelet basis functions. Because of this characteristic, EMD method can be applied to any type of signal decomposition in theory, so it has obvious advantages in dealing with non-stationary and non-linear data. It is suitable for analyzing non-linear and non-stationary signal sequence and has high signal-to-noise ratio. Therefore, EMD method has been applied rapidly and effectively in different engineering fields, such as ocean, atmosphere, celestial observation data and seismic record analysis, mechanical fault diagnosis, damping identification of dense frequency dynamic system and modal parameter identification of large civil engineering structure.