变分模态分解在轴承故障诊断中的应用

Application of Variational Mode Decomposition in Fault Diagnosis for Bearings

通过构造仿真信号,对比分析了VMD与EMD算法在分解过程中存在的模态混叠、伪分量、端点效应等问题,并将VMD算法与谱峭度相结合用于滚动轴承故障信号的分解与重构,通过包络谱分析进行故障判别,结果表明:VMD算法能实现信号频域内各分量的自适应剖分,在分解性能上优于EMD算法,能更好地刻画故障信号的包络谱特征。

The VMD and EMD algorithms are comparatively analyzed in mode mixing,pseudo components,end effect and other problems during decomposition process by constructing simulation signals. The VMD algorithm and spectral kurtosis is combined for decomposition and reconstruction of fault signals for rolling bearings,the fault is identified through envelope spectrum analysis. The results show that the VMD algorithm can achieve adaptive subdivision of each component in frequency domain of signals,which has a better decomposition performance than EMD algorithm. The envelope spectrum characteristics of fault signals are well described.