基于SVMD-SES的滚动轴承故障诊断

Fault Diagnosis of Rolling Bearings Based on SVMD-SES

针对在进行复杂工业设备滚动轴承故障诊断时,由于强噪声影响使故障微弱瞬态冲击特征难以识别的问题,提出一种基于平方包络谱逐次变分模态分解的机械故障诊断方法。首先在变分模态分解的基础上进行逐次变分模态分解推导,降低模态混叠现象和计算复杂度。其次利用峭度指数和互相关系数的加权值构造相关峭度,通过筛选所得分量得到真实的故障模态成分。通过平方包络谱凸显信号瞬态冲击信息并进行特征提取。最后通过实验室平台采集轴承振动数据进行验证分析,实验结果表明:采用所提方法能准确识别周期性瞬态冲击,有效提取微弱特征,提高对复杂机器进行故障诊断的准确性和效率。

The fault diagnosis of rolling bearings in complex industrial equipment is studied.Aiming at the difficulty to identify the weak transient shock features in rolling bearings of complex industrial equipment due to solid noise interference,a mechanical fault diagnosis method based on the square envelope spectrum and successive variational mode decomposition is proposed.Firstly,the successive variational modal decomposition is derived to reduce the modal mixing phenomenon and the computational complexity.Furthermore,the weighted kurtosis and cross-correlation coefficient are used to form the weighted correlation kurtosis,which filters the obtained components and extracts the genuine fault mode components.After-ward,the signal transient impact information is highlighted by the squared envelope spectrum for feature extraction.Finally,the bearing vibration data are collected through the laboratory platform to verify the analysis.The experimental results show that the proposed method can accurately identify the periodic transient shocks,effectively extract the weak features,and im-prove the accuracy and efficiency of fault diagnosis analysis of complex machines.