基于MCKD和VMD的滚动轴承微弱故障特征提取

发布时间：2018-05-28 17:39

本文选题：滚动轴承 + 最大相关峭度解卷积　；参考：《振动与冲击》2017年20期

【摘要】：针对滚动轴承早期故障特征非常微弱,易受随机噪声和其他信号干扰而难以提取等现象,提出了用最大相关峭度解卷积(Maximum Correlated Kurtosis Deconvolution,MCKD)和变分模态分解(Variational Mode Decomposition,VMD)相结合的方法提取滚动轴承故障特征。首先用MCKD进行信号增强,然后利用VMD得到一系列模态,应用互相关系数和峭度准则筛选包含故障信息较为丰富的模态进行重构降噪,最后对重构信号进行包络解调提取故障特征。通过仿真分析和轴承故障模拟实验验证了该方法的有效性,可以精确地分离轴承故障振动信号的不同频率成分。
[Abstract]:The early fault characteristics of rolling bearings are very weak and difficult to extract due to random noise and other signal interference. The maximum Correlated Kurtosis Deconvolution (MCKD) and variational Mode decomposition (VMD) are used to extract the fault features of rolling bearings. Firstly, the signal is enhanced by MCKD, then a series of modes are obtained by using VMD, and the modes with abundant fault information are selected by using the correlation number and kurtosis criterion to reconstruct and reduce the noise. Finally, the fault features are extracted by envelope demodulation. The effectiveness of this method is verified by simulation analysis and bearing fault simulation experiments, and the different frequency components of bearing fault vibration signals can be accurately separated.
【作者单位】：军事交通学院军用车辆工程技术研究中心;
【分类号】：TH133.33

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