基于参数优化VMD-MCKD的强噪声背景下滚动轴承故障特征提取

Feature extraction of rolling bearing faults under strong noise background based on parameter optimized VMD-MCKD

摘要

针对强噪声背景滚动轴承故障特征难以被提取的问题，提出了参数优化变分模态分解（variational mode decomposition，VMD）和最大相关峭度反卷积（maximum correlation kurtosis deconvolution，MCKD）提取滚动轴承故障特征的方法。首先，采用改进麻雀算法对VMD参数进行离线寻优，得到最优参数组合并对原始信号进行分解。其次，根据包络谱峰值因子和样本熵构建出一种新筛选指标，对分解各固有模态函数（intrinsic mode function，IMF）分量进行筛选与重构。然后，对重构信号经改进麻雀算法在线法优化的MCKD进行增强。最后，对增强的信号进行包络解调分析，从而提取滚动轴承故障频率信息。仿真和实验结果表明，该方法能够增强淹没在强噪声中的冲击成分，有效提取滚动轴承故障特征。

Abstract

In order to solve the problem that rolling bearing fault features were difficult to be extracted under strong noise background， parameter optimized variational mode decomposition （VMD） and maximum correlation kurtosis deconvolution （MCKD） were proposed to extract rolling bearing fault features. Firstly， the original signal was decomposed by the optimal combination of parameters obtained by offline optimization of the VMD parameters using the improved sparrow algorithm. Secondly， in order to screen and reconstruct each IMF after decomposition， a new screening metric was constructed based on the envelope spectrum peak factor and sample entropy. Then， the reconstructed signal was augmented with MCKD optimized by the online method of the improved sparrow algorithm. Finally， the bearing failure frequency information was extracted from the enhanced signal by envelope demodulation analysis. Simulation and experimental results show that the proposed method is able to enhance the shock components submerged in the strong noise and effectively extract rolling bearing fault features.

关键词

特征提取 / 滚动轴承 / 变分模态分解 / 最大相关峭度反卷积 / 信号重构

Key words

feature extraction / rolling bearing / variational mode decomposition / maximum correlation kurtosis deconvolution / signal reconstruction

基金资助

国家自然科学基金(62003223)