基于VMD与快速谱峭度的滚动轴承故障诊断

Fault diagnosis of rolling bearings based on VMD and fast spectral kurtosis

针对滚动轴承故障信号易受环境噪声干扰,故障特征信息获取相对困难的问题,提出了基于变分模态分解(VMD)与快速谱峭度的滚动轴承故障特征提取方法。首先将轴承信号分解为若干个固有模态分量(IMF),然后利用最大相关峭度解卷积算法对各阶模态分量进行计算,选取相关峭度值相对较大的几个IMF分量作为故障信息最突出的研究对象,并对其进行快速谱峭度分析;最后根据快速谱峭度图结果设置滤波频率,对滤波信号进行平方包络谱分析得到轴承的故障特征信息。通过公开数据和实验分析表明了该方法可以成功诊断轴承故障。

Aiming at the problem that rolling bearing fault signals are easily disturbed by environmental noise and it is relatively difficult to obtain fault feature information, a rolling bearing fault feature extraction method based on VMD and fast spectral kurtosis is proposed. First, the bearing signal is decomposed into several IMF components, and then the maximum correlation kurtosis deconvolution algorithm is used to calculate the modal components of each order, and several IMF components with relatively large correlation kurtosis values are selected as the most prominent study of the fault information object and perform fast spectral kurtosis analysis on it;finally, set the filter frequency range according to the results of the fast spectral kurtosis map, and perform square envelope spectrum analysis on the filtered signal to obtain the fault characteristic information of the bearing. Public data and experimental analysis show that this method can successfully diagnose bearing faults.