The vibration signal is decomposed into a sum of intrinsic mode functions.
振动信号被分解为一系列固有模态函数。
In the course of filtration, interpolation is the essential step to produce the intrinsic mode functions, and the basis of the Hilbert spectral analysis.
在筛选过程中，插值是产生本征模函数关键的一步，是希尔伯特谱分析的基础。
Empirical mode decomposition(EMD) is a signal processing technique to decompose data set into several intrinsic mode functions(IMF) by a sifting process.
经验模式分解（EMD）通过筛分过程将原始信号分解成若干个基本模式分量（IMF），可看作无需预设带宽的自适应高通滤波方法。
In addition, complicated tested data are decomposed into several intrinsic mode functions by this way, which low analyzing error, and predigest processing.
另外，通过局域波分析可把复杂的实测数据分解成有限个基本模式分量，从而简化信号分析过程，降低信号分析误差。
Firstly, the theory of empirical mode decomposition (EMD) is introduced, and the reconstructing and filtering algorithm of intrinsic mode functions (IMF) from EMD is given in this paper.
本文介绍了经验模态分解的方法与原理，给出了由经验模态分解产生的固有模态函数重构组合滤波器的原理与详细算法。
The main innovations embodied in this method are the introduction of the intrinsic mode functions based on local properties of signals, which make the instantaneous frequency meaningful.
局域波分析方法的重大突破在于用基于信号局部特征的多个基本模式分量来描述信号，并赋予每个基本模式分量具有实际物理意义的瞬时频率。
The main innovations embodied in this method are the introduction of the intrinsic mode functions based on local properties of signals, which make the instantaneous frequency meaningful.
局域波分析方法的重大突破在于用基于信号局部特征的多个基本模式分量来描述信号，并赋予每个基本模式分量具有实际物理意义的瞬时频率。
应用推荐