基于VMD和1.5维Teager能量谱的滚动轴承故障特征提取

发布时间：2018-02-24 15:40

本文关键词： 变分模式分解 .维Teager能量谱特征提取故障诊断滚动轴承　出处：《振动与冲击》2017年18期 　论文类型：期刊论文

【摘要】：为准确提取非线性、非平稳的滚动轴承故障信号中的故障特征,提出基于变分模式分解(Variational Mode Decomposition,VMD)和1.5维Teager能量谱的滚动轴承故障特征提取方法;变分模式分解(VMD)是一种新的信号自适应分解方法,1.5维Teager能量谱具有1.5维谱良好的降噪效果和Teager能量算子强化信号瞬态冲击的优点。故障特征提取过程:首先,对滚动轴承故障信号进行VMD分解得到一组分量,根据峭度-相关系数准则筛选出2个冲击特征明显分量进行信号重构;再次,对重构信号进行1.5维Teager能量谱分析;最后根据能量谱图的分析,提取出滚动轴承的内圈和滚动体故障特征。仿真信号和试验信号的分析都验证了提出方法的有效性;通过与EEMD分解比较,采用VMD变分模式分解和1.5维Teager能量谱的分析方法更具有区分性,可以有效识别滚动轴承的故障特征。
[Abstract]:In order to accurately extract fault features from nonlinear and non-stationary rolling bearing fault signals, a fault feature extraction method for rolling bearing based on variational mode decomposition (VMD) and 1.5 dimensional Teager energy spectrum is proposed. Variational mode decomposition (VMD) is a new signal adaptive decomposition method. The 1.5-dimensional Teager energy spectrum has good noise reduction effect and Teager energy operator can enhance the transient impulse of signal. The process of fault feature extraction: firstly, A group of components are obtained by VMD decomposition of rolling bearing fault signal, and two distinct components of impact characteristic are selected according to kurtosis correlation coefficient criterion. Thirdly, 1.5-dimensional Teager energy spectrum analysis of reconstructed signal is carried out. Finally, according to the analysis of energy spectrum, the fault characteristics of inner ring and rolling body of rolling bearing are extracted. The analysis of simulation signal and test signal verify the validity of the proposed method. The method of VMD variational mode decomposition and 1.5-dimensional Teager energy spectrum analysis is more discriminative and can effectively identify the fault characteristics of rolling bearings.
【作者单位】：华北电力大学机械工程系;
【基金】：国家自然科学基金资助项目(51675178;51475164) 河北省自然基金资助项目(E2013502226)
【分类号】：TH133.33

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