基于ELMD和MED的滚动轴承早期故障诊断方法

Early Fault Diagnosis Method for Rolling Bearings Based on Ensemble Local Mean Decomposition and Minimum Entropy Deconvolution

针对滚动轴承早期故障振动信号的非平稳特性和现实中受环境噪声影响严重,故障特征信息难以识别的问题,提出基于ELMD和MED的故障诊断方法。首先,运用ELMD对采集到的轴承振动信号进行分解,得到一系列PF分量;然后,依据相关系数与峭度准则,选取包含故障特征信息较丰富的PF分量进行MED滤波处理以消除噪声影响,凸现故障特征信息;最后,对降噪信号进行Hilbert包络谱分析,从谱图中准确地识别轴承故障特征频率。

In view of non-stationary characteristics of early fault vibration signals of rolling bearings and difficult identification of fault feature information influenced seriously by environmental noise in actual environment,a fault diagnosis method is proposed based on ensemble local mean decomposition（ ELMD） and minimum entropy deconvolution（ MED）. Firstly,the collected vibration signals of the bearings are decomposed by using ELMD,and a series of product function components are obtained. According to correlation coefficient and kurtosis criterion,the PF component containing abundant fault feature information is selected to carry out MED filtering to eliminate the influence of noise and highlight the fault feature information. Finally,the Hilbert envelope spectrum analysis is carried out for denoised signals,the fault feature frequency of the bearings is identified accurately from spectrogram.