基于EMD模态能量分析的滚动轴承故障特征提取

针对滚动轴承振动信号具有非平稳性的特点,提出一种提取相同工况条件下正常信号与故障信号各固有模态函数能量比构建特征向量的特征提取方法。由于EMD分解后各模态分量存在模态混叠现象,导致分解结果具有不确定性,因此传统的能量特征提取方法在滚动轴承故障诊断中的故障识别率较低。通过引入相同工况条件下的正常信号,将各模态分量的能量特点转化为相对于正常信号的能量特征。仿真实验表明,本文所提方法能够有效地提取滚动轴承的故障特征,进而实现其故障诊断。

Fault Feature Extraction of Roller Bearing Based on EMD Modal Energy Analysis

According to the non-stationary signal of rolling bearing, a feature extraction method of extracting energy ratio of intrinsic mode function(IMF) of normal signal and fault signal to construct characteristic vectors is proposed. Because of the modal aliasing by EMD, the decomposition results are uncertain, and the fault recognition rate of traditional energy feature extraction is low. By introducing normal signal under the same working conditions, the energy characteristics of various mode vectors are changed into energy features relative to normal signal. The experimental results show that the proposed method can effectively extract the fault feature of roller bearing and achieve its fault diagnosis.