广义经验模式分解的齿轮断齿故障分析

Fault Diagnosis of Gear Breaking Based on Generalized Empirical Mode Decomposition

在齿轮故障研究中,对信号进行自适应的分析能取得很好的效果。在振动信号上,经验模式分解(EMD)能够很好地满足信号分析方法的自适应性。但经验模式分解不够完善,该研究在经验模式分解的基础上提出了极值点等差分组的广义经验模式分解(Generalized Empirical Mode Decomposition,GEMD),并在齿轮断齿故障中进行试验分析,取得了一定的效果。广义经验模式分解(GEMD)包含了EMD,是对EMD的补充与完善。

In gear fault research, adaptive signal analysis can achieve good results. In vibration signal, empirical mode decomposition(EMD)can well meet the self-adaptability of signal analysis method. However, the theory of empirical mode decomposition is not perfect. Based on empirical mode decomposition, the generalized empirical mode decomposition is proposed in this paper. Generalized empirical mode decomposition(GEMD)contains EMD, which is complementary to and perfect for EMD.