基于形态学的一种齿轮故障特征提取方法  

A Gear Fault Feature Extraction Method Based on Morphology

作　　者：朱一[1] 吕勇[2] 侯高雁 黄浩[2] 

机构地区：[1]中国人民解放军96618部队 [2]武汉科技大学机械自动化学院

出　　处：《机床与液压》2014年第23期192-194,共3页Machine Tool & Hydraulics

基　　金：国家自然科学基金青年基金资助项目(51105284);湖北省高校优秀中青年创新团队计划资助项目(T200905)

摘　　要：针对数学形态学滤除白噪声能力不足的问题,提出了奇异值分解(SVD)、局域均值分解(LMD)和数学形态学相结合的特征提取方法。将信号进行奇异值分解,对分解后的主要成分取均值,然后进行局域均值分解,选取主要分量求和重构,再用形态学差值滤波器提取故障信号的频率特征。通过数值仿真试验和齿轮局部故障模拟实验,结果表明:该方法能够清晰地提取出故障信号的频率特征,并与奇异值分解形态滤波法相比较,证明了该方法的有效性。Aimed at the problem of shortage of capacity of the mathematical morphology filtering white noise, the feature extrac-tion method was proposed in combining of singular value decomposition （ SVD） , local mean decomposition （ LMD） and mathematical morphology. The SVD was used on the signal, the main ingredients in average were obtained after decomposition, and then the LMD was used. Main component was selected for sum re-fabricating, and then morphology difference filter was used to extract the frequency characteristics of the fault signal. Through numerical simulation test and local gear fault simulation experiment, the results show that this method can clearly extract the fault signal, and is compared with SVD and morphological filtering method, which has proved the ef-fectiveness of the proposed method.

关 键 词：奇异值分解 局域均值分解 数学形态学 特征提取 

分 类 号：TH165[机械工程—机械制造及自动化]