基于相关峭度准则EEMD及改进形态滤波的轴承故障诊断方法

Rolling Bearings Fault Diagnosis Method Using EEMD and Improved Morphological Filter Based on Correlated Kurtosis

针对滚动轴承早期微弱故障常被强烈的背景噪声湮没,造成故障特征提取困难的特点,提出了基于相关峭度准则EEMD及改进形态滤波的轴承故障诊断方法。首先利用EEMD将轴承故障信号分解成有限个IMF分量,然后采用相关峭度准则选取分量并重构,再利用基于相关峭度准则的改进形态滤波对重构信号进行滤波解调,最后将滤波后的信号进行Hilbert包络谱分析,找出故障特征进行识别。试验表明:该方法能有效抑制噪声,特征提取效果更加明显,适用于轴承故障的精确诊断。

The early weak fault of rolling bearing is often covered by the strong background noise, which is difficult to extract fault information ,so a fault diagnosis method using EEMD and improved morphological filter based on correlated kurtosis is proposed. First, the bearing fault signal is decomposed by EEMD, according to correlated kurtosis choose the IMF components, then the morphological filter based on correlated kurtosis is used to filter the reconstructed signal. Finally, the filtered signal is analyzed by the Hilbert envelope spectrum, finding the fault features to identify. Test shows that the proposed method can effectively eliminate the noise and the effect of feature extraction is more obvious, it is suitable for the fault diagnosis of rolling bearing.