基于多准则融合的滚动轴承振动信号消噪方法

Denoising method of vibration signal of rolling bearing based on multi-criteria fusion

针对滚动轴承早期微弱故障特征难以提取的问题,提出了基于多准则融合的滚动轴承振动信号消噪方法。该方法采用集合经验模态分解(EEMD)方法对原始振动信号进行分解得到一组IMF分量,计算各阶IMF分量和原始振动信号的相关系数、各阶IMF分量和原始振动信号包络谱的J散度、各阶IMF分量的峭度值;分别根据相关系数准则、J散度准则、峭度准则选取有效IMF分量,将同时保留的IMF分量作为有效分量进行信号重构。实验结果表明,该方法可以有效地提取滚动轴承早期微弱故障信息,能够有效抑制经验模态分解(EMD)中的模态混叠问题,同时削弱低频噪声,突出高频共振成分,具有良好的自适应性。

In view of problem that early weak fault features of rolling bearings were difficult to extract,a denoising method of vibration signal of rolling bearing based on multi-criteria fusion was proposed.EEMD method was used to decompose original vibration signal to obtain a set of IMF components,then correlation coefficient of each IMF component and original vibration signal,J divergence of envelope spectrum of each order and original vibration signal,and kurtosis value of each IMF component are calculated.Effective IMF components are selected according to correlation coefficient criterion,J divergence criterion and kurtosis criterion,and the simultaneously retained IMF components are used as the effective component for signal reconstruction.The experimental results show that the can effectively suppress modal aliasing problem in EMD,and at the same time weaken low frequency noise and highlight high frequency resonance component,and has good adaptability.