本文通过分析经验模式分解方法的原理，对其关键技术进行研究并提出了一种改进算法。
This paper analyzes the principle of empirical mode decomposition method, and it analyzes key technologies and proposed an improved algorithm.
为了避免经验模式分解(EMD)过程中不同时间尺度函数间的模式混叠，采用基于高斯白噪声加入的经验模式分解方法，并将之应用于旋转机械故障诊断中。
The EMD added Gauss white noise is proposed to avoid mode mixing of different time-scale IMF, and is applied in fault diagnosis for rotating machine.
采用经验模式分解(EMD)与小波分析相结合的方法探讨结构响应数据信号，进行建筑结构损伤检测诊断。
The use of empirical mode decomposition (EMD) method and wavelet analysis in combination is explored for the detection of changes in the structural response data from structural damage diagnosis.
提出了一种基于经验模式分解的气液两相流流型识别方法。
A method of flow regime identification based on empirical mode decomposition was proposed.
本文基于经验模式分解理论提出了一种新的分割方法。
This paper proposes a new image segmentation method based on the theory of EMD.
为了提高风电场风速短期预测的精度，提出了将经验模式分解与数据挖掘方法相结合对风速时间序列进行建模预测。
In order to improve the forecast precision, a forecasting method based on empirical mode decomposition (EMD) and data mining method is proposed.
虽然HVD方法和希尔伯特黄变换（HHT）方法这两者均以希尔伯特变换为基础，但HVD方法避免了复杂的经验模式分解（EMD）过程。
The proposed HVD method was based on the Hilbert transform(HT), just as Hilbert-Huang transform(HHT), but did not involve complicated empirical mode decomposition(EMD).
研究结果表明：基于经验模式分解的时频分析方法可以很有效地提取到非平稳故障特征信号，是一种适合于非线性信号处理的方法。
The experiment result shows that the time-frequency method based on EMD can effectively extract the feature of unbalanced fault signal and is proper for non-frequency modulation signal procession.
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。
Empirical mode decomposition(EMD) is a signal processing technique to decompose data set into several intrinsic mode functions(IMF) by a sifting process.
应用经验模式分解(E MD)方法分析抽油机系统效率变化的趋势项。讨论了EMD方法的端点效应。
We apply the empirical mode decomposition (EMD) method to analyzing the trend of change in the efficiency of an oil well system and deal with the end effects of the EMD.
研究了强噪声混合条件下微弱信号的经验模式分解（EMD）问题，提出了一种基于随机共振降噪的EMD分解方法。
To deal with this problem, comparison is made between the empirical mode decomposition(EMD) and the wavelet method in terms of signal trend extraction.
然后应用SVR方法对系统效率测试原始数据进行双边延拓，对延拓后的数据信号进行经验模式分解。
The comparison of the test data with measurement data shows that the regression and prediction with the SVR method are highly accurate.
然后应用SVR方法对系统效率测试原始数据进行双边延拓，对延拓后的数据信号进行经验模式分解。
The comparison of the test data with measurement data shows that the regression and prediction with the SVR method are highly accurate.
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