基于EMD和信息熵的滚动轴承故障诊断

Fault Diagnosis of Rolling Bearings Based on EMD and Information Entropy

提出了一种基于EMD和信息熵的滚动轴承故障诊断方法。利用EMD将滚动轴承振动信号分解为多个IMF分量,计算各个IMF分量的信息熵,设定有效的熵阈值来取舍IMF分量,利用保留的IMF分量重构信号,并对重构信号进行Hilbert包络谱分析,提取滚动轴承故障特征频率。对实测滚动轴承振动信号分析表明,该方法能有效提取滚动轴承的故障特征频率。

A new method for roiling bearing fault diagnosis is proposed based on EMD and information entropy. EMD is used to decompose the fault signal of the rolling bearing into several IMFs. The information entropy of each IMF is calculated, an effective threshold is set to select IMFs. The IMFs selected are used to reconstruct the signal, the reconstructed signal is analyzed with Hilbert envelope spectrum, and then the fault frequency is extracted form the envelope spectrum. The factual fault signal of the rolling bearing is analyzed with this method, and the result shows that this method can effectively extract the fault frequency of the rolling bearing.