基于MED和变分模态分解的滚动轴承早期故障诊断方法

Incipient Fault Diagnosis Method for Rolling Bearing based on MED and Variational Mode Decomposition

针对滚动轴承早期微弱故障特征容易淹没于环境噪声中而难以提取的问题,提出了最小熵解卷积(MED)降噪和变分模态分解(VMD)相结合的滚动轴承早期故障诊断方法。首先以峭度最大为准则利用MED对轴承振动信号进行降噪处理,然后采用新的高精度多分量信号分解方法——VMD将降噪信号分解为若干个分量,最后通过分析最大峭度分量包络谱中故障频率成分诊断轴承故障。轴承实验分析结果表明了该方法的有效性。

Aiming at the problem that the incipient fault feature of rolling bearing is easily submerged in the environmental noise and is difficult to extract, an incipient fault diagnosis method combined minimum entro- py deconvolution （MED） denoise with variational mode decomposition （VMD） is proposed. Firstly, the bear- ing vibration signal is denoised by using MED with kurtosis as maximization criteria. Then, the processed signal is decomposed into several components through VMD which is a new high precision multi - component decompo- sition method. At last, the bearing fault is diagnosed by analyzing fault frequency in the envelope spectrum of the maximal kurtosis component. The experimental method. signal analysis results show the validity of the proposed