摘要
利用具体的非平稳齿轮箱振动信号,分别应用局域均值分解方法(Local Mean Decomposition,LMD)和经验模态分解方法(Empirical Mode Decomposition,EMD)进行了模态分解,并计算得出能量熵。物理意义明确且非常直观,用LMD方法分解齿轮箱振动信号模态混叠程度要轻于EMD方法分解所得模态混叠程度。同时,从端点效应和分解速度两方面将两种分解方法做了对比,LMD方法抑制端点效应的能力强于EMD方法,且分解速度较EMD方法快。
Through some specific non-stationary vibration signals of a gear case, the mode decomposition is made by using both the methods of local mean decomposition and the empirical mode decompositionrespectively. Meanwhile, the energy entropy is gained by calculation, which has a definite and intuitve meaning. It is found that modal mixture using LMD is less than that using EMD. At the same time, the contrast is made from both the endpoint effect and the decomposition rate. The conclusion is drawn ： the ability of LMD method to suppress the endpoint effect is stronger than EMD method, and the decomposing speed of the LMD method is more quickly than the EMD method.
出处
《机械设计与研究》
CSCD
北大核心
2012年第3期38-40,54,共4页
Machine Design And Research
基金
内蒙古自治区资助高等学校科学研究项目(NJZY11148)
关键词
LMD分解
EMD分解
能量熵
模态混叠
端点效应
local mean decomposition
empirical mode decomposition
energy entropy
modal mixture
endpoint effect
