Abstract: A problem of mode mixing occurred in implementation process of local mean decomposition (LMD) method,a fault diagnosis approach for rolling bearing based on ensemble local mean decomposition (ELMD) and least squares support vector machine (LS-SVM) was proposed.Firstly,by using ELMD method,the vibrational signal of rolling bearing was decomposed a series of product function (PF) components,and then the PF components which contain main fault information were selected,and the kurtosis coefficients and energy characteristic parameters extracted from selected PF components were regarded as fault feature which was served as input parameters of LS-SVM to identify the working status and fault types of rolling bearing.The analytic results of fault-free,inner-race fault and outer-race fault of rolling bearing indicate that the working status and fault types of rolling bearing can be identified accurately and effectively by using the approach based on ELMD and LS-SVM.

Key words: ELMD, mode mixing, LS-SVM, rolling bearing, fault diagnosis

摘要： 针对局部均值分解（local mean decomposition，LMD）实现过程中存在的模式混淆现象，提出了一种基于总体局部均值分解（ensemble local mean decomposition，ELMD）与最小二乘支持向量机（least squares support vector machine，LS-SVM）相结合的滚动轴承故障诊断方法。该方法先对滚动轴承振动信号进行ELMD分解，并得到若干乘积函数（product function，PF），然后选取包含主要故障信息的PF分量，提取其峭度系数与能量特征参数以构造故障特征向量，并作为LS-SVM的输入来识别滚动轴承的工作状态和故障类型。通过对滚动轴承正常状态，内圈故障和外圈故障的分析结果表明，基于ELMD与LS-SVM的诊断方法可以准确有效识别滚动轴承的工作状态和故障类型。

关键词: ELMD, 模式混淆, LS-SVM, 滚动轴承, 故障诊断