局部均值分解在齿轮故障诊断中的应用研究  

Application of local mean decomposition in gear fault diagnosis

作　　者：何田[1] 林意洲[1] 郜普刚[1] 申永军[2] 

机构地区：[1]北京航空航天大学交通科学与工程学院,北京100191 [2]石家庄铁道学院机械工程分院,石家庄050043

出　　处：《振动与冲击》2011年第6期196-201,共6页Journal of Vibration and Shock

基　　金：国家自然科学基金项目(50875010)

摘　　要：局部均值分解(Local Mean Decomposition,简称LMD)作为一种新的自适应的时频分析方法,在故障诊断领域开始得到研究。利用仿真信号研究了LMD算法的特性,验证了LMD处理描述齿轮故障信号特征的多分量调幅调频信号的有效性;在此基础上将LMD综合应用于断齿、磨损和剥落三种齿轮故障诊断中,并与传统解调方法进行了对比。结果表明,LMD方法可以有效提取故障齿轮的故障特征,消除虚假成分的影响,从而提高了齿轮故障诊断的准确性。As a new kind of self-adaptation time-frequency analysis approach,local mean decomposition（LMD） is beginning to be applied in the field of fault diagnosis.The simulation signals were used to illustrate the effectiveness of the proposed method in processing multi-components modulated signals which express the characteristics of gear fault signals.Then,the LMD method was introduced to diagnose gear faults including wear,fracture and spalling by extracting the fault features from the test signals.The analysis results demonstrate that LMD can diagnose gear faults and improve the accuracy of diagnosis,compared with the traditional demodulation approach.

关 键 词：局部均值分解 齿轮 故障诊断 HILBERT变换 

分 类 号：TH212[机械工程—机械制造及自动化]