基于LMD EMD故障诊断分析及其研究

Fault diagnosis analysis and research based on LMD EMD

经验模态分解(EMD)及局域均值分解(LMD)都是转子故障诊断领域时频分析的有效方法。EMD为非平稳信号进行有意义的Hilbert变换起到了桥梁的作用,但是却会因此而产生了不能解释的负频率。而LMD将一个复杂的多分量信号分解为若干个瞬时频率有物理意义的乘积函数(Production Function PF),并且其局部均值函数与包络估计函数都是采用平滑处理的方法形成的避免了EMD方法中的过包络与欠包络现象。实验通过采用LMD与EMD方法对两类常见的转子故障信号的分析比对,得出LMD在高频和频率变化波动大的故障信号中比EMD效果更佳明显。

Both Empirical Mode Decomposition（EMD） and the Local Mean Decomposition（LMD） are useful methods in time-frequency analysis of rotor fault diagnosis.EMD plays the role of a bridge for the significant Hilbert transform of non-stationary signal,but it produces the negative frequency which cannot be explained in theory.While the LMD can decompose a complex multi-component signals into several Production Function（PF）,which includes the physical meaning instantaneous frequency,and it avoids the enveloping and owe envelope phenomenon which can be seen in MED,it takes the method of smoothing to deal with the local mean function and envelope estimation function.Throughout the comparison between the two normal rotor fault signal by using LMD and EMD,the results show that LMD is better in high frequency and big frequency fluctuation than EMD.