基于VMD-WPT和能量算子解调的滚动轴承故障诊断研究

发布时间：2018-03-12 14:09

本文选题：变分模态分解　切入点：小波包变换　出处：《图学学报》2017年02期 　论文类型：期刊论文

【摘要】：针对滚动轴承早期故障振动信号具有能量小、易受背景噪声干扰,导致故障特征提取困难等问题,提出基于变分模态分解(VMD)和小波包变换(WPT)相结合的方法来提取故障特征。首先将振动信号进行VMD分解,得到若干本征模态分量(IMF);其次,通过峭度准则选取峭度值较大的分量进行重构;最后将重构分量采用WPT方法进行分解,并计算小波包的能量、选取能量集中的频段进行能量算子解调,从而提取故障特征信息。将该方法应用到滚动轴承实测数据中,并与目前最常用的方法 EEMD-WPT对特征信号的提取效果作对比。实验结果表明该方法可以更精确地提取出的故障特征频率,验证了其有效性。
[Abstract]:In view of the problems of low energy and easy to be disturbed by background noise, the early fault vibration signal of rolling bearing is difficult to extract fault features. A method based on variational mode decomposition (VMD) and wavelet packet transform (WPT) is proposed to extract the fault features. Firstly, the vibration signal is decomposed by VMD, and some intrinsic modal components are obtained. Finally, the reconstructed component is decomposed by WPT method, the energy of wavelet packet is calculated, and the frequency band of energy concentration is selected to demodulate the energy operator. In order to extract the fault feature information, the method is applied to the measured data of rolling bearing. The experimental results show that the method can extract the fault feature frequency more accurately and verify its effectiveness.
【作者单位】：石家庄铁道大学电气与电子工程学院;
【基金】：国家自然科学基金项目(11227201,11372199,11572206) 河北省自然科学基金项目(A2014210142)
【分类号】：TH133.33

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