局部均值分解在滚动轴承故障综合诊断中的应用

局部均值分解(LMD)是在经验模态分解(EMD)的基础上提出的一种新的自适应时频分析方法，在故障诊断领域展现出较好的应用前景。本文改进了LMD算法，提高LMD计算速度，并利用仿真信号研究了LMD算法的特性，验证了LMD处理多分量调幅调频信号的有效性；针对轴承故障信号的调制特点以及背景信号对故障信号的影响，本文提出将其应用于滚动轴承外圈点蚀、内圈点蚀和滚动体点蚀的故障综合诊断中，结果表明LMD方法能够有效地提取出故障特征频率，对故障类型做出准确判断

Roller bearing comprehensive fault diagnosis based on LMD

LMD(Local mean decomposition) is a new kind of adaptive time-frequency analysis method,which exhibits a good application prospect in filed of bearing fault diagnosis.The LMD was advanced to improve its calculation speed.Then,a synthetic signal was used to illustrate the effectiveness of the proposed method to process multi-component modulated signals.In view of the modulation characteristics of the vibration acceleration signal of the faulty bearing and the impact of background signals,LMD method was proposed to diagnose the bearing with outer-race,inner-race or elements faults.The results indicate that the characteristic frequencies can be extracted effectively using LMD method and be used to make correct judge of the fault type.