基于ICEEMDAN和小波阈值的滚动轴承故障特征提取方法

[目的]针对滚动轴承故障信号非线性、非平稳特征导致的故障特征频率难以提取的问题，提出了一种基于改进的带有自适应白噪声的完全集合经验模态分解（ICEEMDAN）和小波阈值降噪的滚动轴承故障特征提取方法。[方法]首先用小波阈值降噪对故障信号进行预处理，然后利用ICEEMDAN对降噪后的信号进行模态分解，产生一系列的固有模态函数（IMF），并根据互相关系数法提取与原信号相关的模态分量，作各层模态分量的包络谱图，提取滚动轴承的故障特征频率。[结果]通过仿真试验与滚动轴承故障试验分析，并将其与集合经验模态分解（EEMD）处理的进行比较，基于ICEEMDAN方法分解后的包络谱幅值更加明显。[结论]本研究提出的方法能精确地提取滚动轴承的故障特征频率。
[Objectives]To effectively extract the nonlinear and non-stationary characteristics of rolling bearing fault signal,a fault diagnosis method of rolling bearing was proposed based on improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)and wavelet threshold denoising method.[Methods]First of all,the fault signal was pro-processed by wavelet threshold denoising method. Then,ICEEMDAN was used to decompose the target signal into a series of intrinsic mode function(IMF). And the valid IMF was extracted based on correlation coefficient method. Next,the envelope spectrum was drawn and the fault characteristics frequency of rolling bearing was extracted.[Results]Through the simulation experiment and rolling bearing fault experiment,the method was compared with ensemble empirical mode decomposition(EEMD). The amplitude of the envelope spectrum after the decomposition based on the ICEEMDAN method was more pronounced.[Conclusions]The results showed that this method can extract the fault characteristics frequency accurately