基于自适应CEEMD的非平稳信号分析方法

由于标准的互补集总经验模态分解(complementary ensemble empirical mode decomposition,简称CEEMD)在处理模态混叠问题时缺乏自适应性,其本质是分解信号获得的本征模态函数(intrinsic mode function,简称IMF)之间产生了一定的信息耦合现象,使IMF分量不能正确地反映信号的真实成分。因此,提出了在使用CEEMD分解信号的过程中嵌入网格搜索算法(grid search algorithm,简称GSA),以最小二乘互信息(least squares mutual information,简称LSMI)为网格搜索算法的适应度函数,构造一个自适应CEEMD方法。该算法通过自适应地搜索最佳的白噪声幅值,修正信号分解过程中产生的少量的耦合频率成分,确保每个IMF分量之间信息的正交性,以进一步抑制模态混叠问题。最后,通过仿真实验验证了该方法的有效性,并将该方法用于提取滚动轴承微故障的特征频率。实验结果表明,该算法在滚动轴承的微故障特征提取应用中具有更少的迭代数、IMF分量以及相对更小的计算量。

Non-stationary Signal Analysis Method Based on Adaptive CEEMD

The problem of "mode mixing" is one of the main problems limiting the empirical mode decomposition in engineering applications. An improved algorithm of complementary ensemble empirical mode decomposition (CEEMD) as empirical mode decomposition (EMD) improves the mode mixing problem of EMD to some extent. However, the standard CEEMD method still empirically set the amplitude of white noise, and it is not adaptive to deal with the mode mixing problem. By studying the phenomenon of modal aliasing, its essence is that the intrinsic mode function (IMF) is obtained by decomposing the signal generates certain information coupling phenomenon, which cannot make the IMF component accurately reflect the real components of the signal. Therefore, this paper proposes to embed grid search algorithm (GSA) in the process of decomposing signals with CEEMD, and to construct an adaptive CEEMD method by taking least squares mutual information (LSMI) as the fitness function of GSA. The algorithm adaptively searches for the optimal white noise amplitude, corrects a small number of coupling frequency components generated during signal decomposition, ensures the orthogonality of information between each IMF component, and further inhibits the mode aliasing problem. Finally, the effectiveness of the proposed method is verified by simulation test, and it is used to extract the characteristic frequency of micro-fault of rolling bearing. The experimental results show that the algorithm has less iteration numbers, less IMF components and relatively less calculation amounts in the application of micro-fault feature extraction of rolling bearing.