基于局部均值分解的边际谱在滚动轴承故障诊断中的应用  

Application of marginal spectrum based on local mean decomposition in rolling bearing fault diagnosis

作　　者：李慧梅[1,2] 安钢[1] 黄梦[1] 

机构地区：[1]装甲兵工程学院机械工程系,北京100072 [2]军事交通学院汽车工程系,天津300161

出　　处：《振动与冲击》2014年第3期5-8,13,共5页Journal of Vibration and Shock

基　　金：军队科研计划项目

摘　　要：局部均值分解(Local Mean Decomposition,LMD)将复杂的多分量信号自适应地分解为有限个乘积函数(PF)的和,在计算了各个分量的瞬时幅值(IA)和瞬时频率(IF)后,可以计算出基于LMD的边际谱。针对直接法求取瞬时频率存在端点误差大问题,提出一种改进的直接求取瞬时频率的方法;提出了基于LMD的边际谱的滚动轴承故障诊断方法,将该方法应用于实际滚动轴承故障诊断中,结果表明该方法能有效地提取出滚动轴承的故障特征频率,从而确定故障部位。Local mean decomposition（LMD）can be used to decompose a complex multi-component signal into a linear combination of several product functions （PFs ）.After obtaining the instantaneous amplitudes and instantaneous frequencies of all PF components,the marginal spectrum based on LMD can be calculated.Aiming at the big error problem of the instantaneous frequency at end-points extracted with the direct method,an improved direct method was put forward.The marginal spectrum method based on LMD for rolling bearing fault diagnosis was proposed,and it was applied in actual rolling bearing fault diagnosis.The analysis results showed that the fault characteristic frequency can be extracted effectively,and the fault position can be determined.

关 键 词：局部均值分解 边际谱 滚动轴承 故障诊断 瞬时频率 

分 类 号：TN911.7[电子电信—通信与信息系统] TH165.3[电子电信—信息与通信工程]