基于改进的共振稀疏分解的滚动轴承故障诊断

针对滚动轴承早期微弱故障湮没在高强背景噪声中、造成故障特征信息提取困难的特点,提出一种改进的共振稀疏分解方法。首先采用变分模态对信号去噪,根据峭度-相关系数准则选取包含故障特征信息量多的分量进行信号重构;然后对重构后的信号进行粒子群优化的共振稀疏分解;最后对分解得到的低共振分量进行包络分析,提取故障特征频率。实验结果证明了该方法比传统共振稀疏分解更能有效地提取故障特征频率,有效地减少了干扰成分。

Fault Diagnosis of Rolling Bearing Based on Improved Resonance Sparse Decomposition

The early weak fault of rolling bearing is annihilated in the high strength background noise, which makes the fault feature information extraction difficult. An improved resonance sparse decomposition method was proposed. Variational mode decomposition was used to denoise the collected signal. According to the kurtosis-correlation coefficient criterion, the component containing the large amount of fault feature information was selected for signal reconstruction. Then the resonance sparse decomposition of particle swarm optimization was performed on the reconstructed signal. Finally, the envelope analysis of the low resonance components obtained was used to extract the fault feature frequency.Experimental results show that this method can effectively extract the fault feature frequency and reduce the interference component compared with traditional resonance sparse decomposition.