摘要
在总体平均经验模态分解(Ensemble empirical mode decomposition，EEMD)降噪过程中，对本征模态分量（Intrinsic Mode Function，IMF）的有效处理一直是影响降噪效果的关键。为此，提出一种基于改进EEMD的去噪方法。首先基于“3σ”法则和奇异值分解(Singular value decomposition，SVD)提取第一个IMF分量中有用信号细节。然后，利用连续均方误差准则对剩余IMF分量进行高低频区分，分别使用SVD和S-G算法提取高低频分量的有用信号，可以有效避免了高频部分有用信号的流失，同时剔除低频分量中的部分噪声，克服了EEMD去噪时IMFs难以有效处理的不足。为了验证本文方法的有效性，进行了数字仿真与双势阱混沌振动试验，结果表明，本文方法的降噪效果优于小波加权和EEMD去噪方法。
Abstract
In the process of on ensemble empirical mode decomposition(EEMD) noise reduction, the effective treatment of intrinsic mode function(IMF) is the key to the effect of noise reduction. Therefore, an improved EEMD de-noising method was proposed. Firstly, the signal details of the first IMF are extracted by using the “3σ” criterion and singular value decomposition(SVD). Then the remaining IMFs are divided into high frequency and low frequency components based on consecutive mean square error(CMSE). Secondly, the useful signals of high frequency components and low frequency components are extracted based on singular value decomposition(SVD) and Savitzky-Golay(SG) filtering method respectively. Thus, the useful signals loss of high frequency components is avoided while the noise signals of low frequency components are removed effectively. In order to evaluate the performance of the proposed method, the experimental rig of leaf spring based on the double potential well theory was carried out in this paper, and the experimental results showed that the proposed method both outperformed the wavelet weighted parameters method and EEMD de-noising method.
关键词
总体平均经验模态分解 /
混沌信号 /
奇异值分解 /
降噪 /
S-G滤波
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Key words
ensemble empirical mode decomposition /
chaotic signal /
singular value decomposition /
demoising /
Savitzky-Golay filtering
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位秀雷1， 林瑞霖2，刘树勇2，杨庆超2.
改进EEMD方法及混沌降噪应用研究[J]. 振动与冲击, 2017, 36(17): 35-41
Wei Xiulei1,Lin Ruilin2,Liu Shuyong2,Yang Qingchao2.
De-noising Method Based on Improved EEMD for the Chaotic Signal[J]. Journal of Vibration and Shock, 2017, 36(17): 35-41
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