基于LMD能量熵的滚动轴承故障特征提取

Fault Feature Extraction for Rolling Bearing based on LMD Energy Entropy

为了对滚动轴承运行状态进行有效的判断,利用局部均值分解(LMD)对滚动轴承振动信号进行分解,将复杂的多分量信号分解成多个单分量信号;针对分解后的单分量信号在各频域范围分布不均匀特点,利用LMD能量熵提取出滚动轴承振动信号的故障特征。实验结果表明,LMD能量熵具有较强的信号表征能力,可以有效提取出滚动轴承故障特征。

In order to judge the running status of rolling bearing effectively in the case of small sample,by using the local mean decomposition(LMD),the rolling bearing vibration signal is decomposed.The complex multi-component signal will be decomposed into multiple single component signals.For the characteristic that the distribution of decomposed single component signal is not uniform in the frequency range,by using the LMD energy entropy,the fault feature of rolling bearing vibration signal is extracted.The experimental results show that LMD energy entropy has a strong signal characterization capability,which can effectively extract the rolling bearing fault characteristic.