基于峭度准则VMD及平稳小波的轴承故障诊断

发布时间：2018-06-22 00:05

  本文选题：变分模态分解 + 平稳小波　； 参考：《机械设计与研究》2017年01期

【摘要】：为了从强噪声背景下的轴承振动信号中准确稳定地提取滚动轴承故障特征,提出了基于峭度准则VMD及平稳小波的轴承故障诊断方法。使用变分模态分解对同一负荷下的故障信号进行预处理,通过峭度准则筛选出最佳和次佳信号分量进行重构并使用平稳小波进行去噪处理,最后分析信号的包络谱来对轴承的故障类型进行判断。通过对仿真滚动轴承内圈故障信号进行分析,该方法可成功提取出微弱特征频率信息,噪声抑制效果优于EMD。由此表明,基于峭度准则VMD及平稳小波的轴承故障诊断可有效提取强声背景下的滚动轴承早期故障信息,具有一定的可靠性和应用价值。
[Abstract]:In order to extract the fault features of rolling bearing accurately and stably from the vibration signal of bearing under strong noise, a bearing fault diagnosis method based on kurtosis criterion VMD and stationary wavelet is proposed. The variational mode decomposition is used to preprocess the fault signal under the same load. The best and sub-optimal signal components are selected by kurtosis criterion and the stationary wavelet is used to Denoise the fault signal. Finally, the envelope spectrum of the signal is analyzed to judge the fault type of bearing. By analyzing the fault signal of the inner ring of rolling bearing, the method can extract the weak characteristic frequency information successfully, and the noise suppression effect is better than that of EMD. It is shown that the bearing fault diagnosis based on kurtosis criterion VMD and stationary wavelet can effectively extract the early fault information of rolling bearing under strong sound background and has certain reliability and application value.
【作者单位】： 兰州交通大学机电工程学院;
【分类号】：TH133.3

【参考文献】

中国期刊全文数据库 前8条

1 韩中合;徐搏超;朱霄s，

本文编号：2050634