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?，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。
　　　　　　该方法的关键是经验模式分解，它能使复杂信号分解为有限个本征模函数（Intrinsic Mode Function，简称IMF），所分解出来的各IMF分量包含了原信号的不同时间尺度的局部特征信号。经验模态分解法能使非平稳数据进行平稳化处理，然后进行希尔伯特变换获得时频谱图，得到有物理意义的频率。(The Empirical Mode Decomposition (EMD) method is considered as a major breakthrough in the linear and steady state spectrum analysis based on Fu Liye transform in 2000. The method is based on the time scale characteristics of the data itself to decompose the signal without setting any base functions in advance.

The key of this method is the empirical mode decomposition, which can decompose the complex signal into a limited eigenmode function (Intrinsic Mode Function, for short, IMF), and the decomposed IMF components include the local characteristic signals of the different time scales of the original signal. The empirical mode decomposition method can smooth the non-stationary data, and then obtain the time-frequency spectrum by Hilbert transform to get the physical meaning frequency.)