基于EEMD和Teager能量谱提取轴承故障特征

Extracting Fault Feature of Bearing Based on EEMD and Teager Energy Spectrum

针对轴承振动信号非平稳性以及故障特征难于提取等特点,提出结合集成经验模式分解和Teager能量算子解调,构造Teager能量谱提取轴承故障特征的方法。利用集成经验模式分解能够抑制经验模式分解在处理非平稳振动信号时的模态混叠,Teager能量算子解调能够抑制传统Hilbert变换中的端点效应,具有计算速度快、解调效果好等优点,分解轴承故障信号,计算本征模函数的Teager能量谱,并提取轴承故障特征。仿真和试验分析结果验证了该方法的有效性。

Considering the non-stationary of the vibration signals and it is difficult to extract the fault feature of bearing, the paper proposes a Teager energy spectrum method to extract fault feature by combining ensemble empirical mode decomposition （EEMD） and Teager energy operator （TEO）. EEMD can restrict the mode aliasing while processing non-stationary vi- bration signals with empirical mode decomposition （EMD） and TEO can restrict the end effects in Hilbert transform, and TEO possesses properties of fast computation and excellent demodulation effects. By using EEMD, it can decompose vibra- tion signals of bearing and calculate Teager energy spectrum of intrinsic mode function （ IMF）, and extract fault feature of bearing. The result of simulation and experiment analysis verifies the validity of the method.