基于变分模态分解改进方法的滚动轴承故障特征提取

针对滚动轴承早期故障振动信号信噪比低、故障特征提取困难的问题，提出了基 于多相关-变分模态分解(MC-VMD)的滚动轴承故障诊断方法。首先对多加速度传感器采集到的 信号进行多相关处理以突出故障信号特征；然后通过VMD 自适应地将信号分解成多个本征模 态分量(IMFs)，运用谱峭度法和包络解调对相关峭度较大的分量进行分析；最后通过包络谱识 别出滚动轴承的工作状态和故障类型。将该方法应用到滚动轴承故障实例数据中，实验结果表 明，该方法可有效提取滚动轴承故障特征频率信息。

Rolling Bearing Fault Feature Extraction Based on Improved Variational Mode Decomposition

In order to solve the problems that the fault feature of rolling bearing in early failure period is difficult to extract, a method for fault diagnosis of rolling bearings based on multi-correlation variational mode decomposition (MC-VMD) was presented. First, vibration signal is jointly acquired through multiple acceleration sensors and the multi-correlation process is made for the signal in order to prominent fault signal characteristics. Then VMD was used to decompose the fault signal into several intrinsic mode functions (IMFs), and then the IMF of biggest related kurtosis was analyzed by the spectral kurtosis and envelope demodulation. Finally identify the working status and fault type of rolling bearings through envelope spectrum. The proposed method was applied to actual signals. The results show that this method enables accurate diagnosis of rolling bearing fault, the analysis results demonstrated the effectiveness of the proposed method.