Shannon Entropy Improved Variational Mode Decomposition and Fault Features Extraction
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摘要: 为了解决变分模态分解(VMD)的分解层数K选定困难的问题,提出了利用归一化香农熵对变分模态分解进行参数优化,从而可以自适应地确定分解层数K,可以避免信号过分解与欠分解。首先在程序中预先设定分解层数,让程序进行预分解;计算分解后各本征模态函数(IMF)频带的香农熵,再将香农熵归一化处理,以归一化熵值大小作为循环停止条件来进行自适应确定分解层数K;最后对各IMF分量进行包络分析,提取信号中的故障特征频率。将该方法利用仿真信号和实际故障数据进行分析验证,结果表明该方法既能够自适应地确定K值,同时其分解出的各IMF分量均出现规律性故障振动信号或转频的倍频,证明了这种故障特征提取方法是有效的。Abstract: In order to solve the problem that the number of decomposition layers K of the variational mode decomposition (VMD) is difficult to select. It is proposed to use the normalized Shannon entropy to optimize the parameters of the variational mode decomposition, so that the number K can be determined adaptively, signal over and lack of decomposition can be avoided. First, the number of decomposition layers in the program is set so that the program can be pre-decomposed. Then Shannon entropy of each intrinsic modal function (IMF) band after decomposition is calculated and normalized, and the normalized entropy value is used as the loop stop condition to adaptively determine the decomposition layer number K. Finally, the envelope analysis of each IMF component is carried out to extract the fault features in the signal. This method is analyzed and verified by using the simulated signal and the actual fault data. The results show that the method can self-adaptively determine the K value, and at the same time, the decomposed IMF components contain regular fault vibration signals or frequency doubling of frequency conversion. It is proved that this fault feature extraction method is effective.
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表 1 K=3时各IMF归一化香农熵
IMF1 IMF2 IMF3 0.329 8 0.330 4 0.339 8 表 2 K=4时各IMF归一化香农熵
IMF1 IMF2 IMF3 IMF4 0.109 9 0.123 9 0.140 5 0.625 7 表 3 K=5时各IMF归一化香农熵
IMF1 IMF1 IMF3 IMF4 IMF5 0.073 6 0.073 7 0.084 0 0.156 5 0.612 2 表 4 不同K值最后一个IMF归一化香农熵
K=6 K=7 K=8 K=9 0.137 7 0.115 9 0.204 6 0.185 0 -
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