摘要
针对强噪声干扰下滚动轴承故障特征难以提取的问题,提出一种变分模态分解和Teager能量增强谱的滚动轴承故障诊断方法。该方法首先通过变分模态分解(Variational Mode Decomposition,VMD)将非平稳的轴承故障振动信号分解成一系列平稳的窄带分量;然后根据峭度-相关性最大准则挑选包含故障特征信息最丰富的窄带分量作为主分量;最后对选取的主分量进行Teager能量增强谱,提取滚动轴承的故障特征。通过仿真和实例分析的结果表明:该方法能有效地提取出滚动轴承早期故障特征,且能够抑制强烈的噪声干扰和增强故障冲击特征,优于传统包络谱分析和基于经验模态分解(Empirical Mode Decomposition,EMD)和Teager能量谱的方法的分析结果。
In view of it is difficult to extract the fault features of rolling bearing under the strong noise interference,a rolling bearing fault diagnosis method was proposed based on variational mode decomposition and Teager energy enhanced spectrum. Firstly the original acceleration vibration signal of rolling bearing was decomposed by variational mode decomposition（VMD） to several narrowband components; Then according to the criterion of maximum kurtosis and correlation coefficient,the narrow-band components containing the most fault characteristic information were chosen as the main components; Finally the teager energy enhanced spectrum of the selective narrow-band components was calculated to extract the fault feature of rolling bearing. Simulation and practical examples show that the proposed diagnosis method can more effectively and accurately extract the early fault feature of rolling bearing and suppress noise interference and enhance impact characteristics,superior to the traditional envelope spectrum analysis and the diagnosis method based on empirical mode decomposition（EMD） and Teager energy spectrum.
出处
《机床与液压》
北大核心
2016年第15期178-183,共6页
Machine Tool & Hydraulics
基金
河北省自然科学基金资助项目(E2014502052)
关键词
变分模态分解
Teager能量增强谱
滚动轴承
故障诊断
Variational mode decomposition
Teager energy enhanced spectrum
Rolling bearing
Fault diagnosis
