摘要
该文针对轴承故障诊断中信号处理的端点效应问题,提出基于极值波延拓与窗函数的改进集合经验模态分解EEMD方法。首先对原始信号进行极值波延拓,其次对延拓后的信号加入组合窗体,最后对信号进行EEMD分解,通过仿真验证改进EEMD方法的有效性。同时,进一步结合Hilbert变换建立了改进EEMD-Hilbert的轴承故障诊断模型,利用轴承故障的实测信号证明了该模型在提高轴承故障诊断效率方面有一定优势。
In this paper,aiming at the endpoint effect of signal processing in bearing fault diagnosis problem,put forward based on the extreme value of wave continuation and window function improved collection of Empirical Mode Decomposition(Ensemble Empirical Mode Decomposition,the EEMD)methods:first,the original signal is extreme wave continuation of second signal after the continuation to join the combination form,finally,to EEMD signal Decomposition,improve the effectiveness of the EEMD method is validated by computer simulation;At the same time,an improved eemd-hilbert bearing fault diagnosis model was established based on Hilbert transformation. The measured signals of bearing faults proved that the model had certain advantages in improving the efficiency of bearing fault diagnosis.
作者
邹啸天
王鹏
张宁超
ZOU Xiao-tian;WANG Peng;ZHANG Ning-chao(School of Electronic and Information Engineering,Xi’an Technological University,Xi’an 710016,China)
出处
《自动化与仪表》
2020年第1期38-42,46,共6页
Automation & Instrumentation
基金
陕西省创新人才推进计划-青年科技新星项目（2019KJXX-034）
关键词
集合经验模态分解
极值波延拓
窗函数
端点效应
ensemble empirical mode decomposition(EEMD)
extreme wave extension
window function
end effect
