基于ATVFD和EEMD的滚动轴承故障特征频率提取方法研究

Study on Extracting Antifriction Bearing's Fault Character Frequency Based on ATVFD and EEMD

经验模式分解和集合经验模式分解方法存在模态混叠和端点效应问题,难以有效提取故障特征频率。为此,提出一种基于自适应时变滤波分解和集合经验模式分解相结合的新方法,该方法结合了自适应时变滤波分解方法分解精度高、抑制边界效应,以及集合经验模式分解避免模态混叠的优点,通过时频分析法边际谱有效地提取出故障特征频率。

Both mode mixing and boundary effect bothers empirical mode decomposition （EMD） and ensemble empirical mode decompositions （ EEMD ） methods while extracting the fault character frequency. A new method which combining adaptive time-varying filtering decomposition （ADVFD） with EEMD was proposed, and it boasts ATVFD＇ s high precision and restraining boundary effect and EEMD＇ s avoiding modal mixing. Through HHT（ Hilbert-Huang transform）marginal spectrum,the fault character frequency can be extracted effectively.