The Rolling Bearing Fault Diagnosis Based on LMD and LS-SVM

摘要：

　　Rolling bearings vibration signal is complex and non-stationary signal.In order to diagnose the bearing failures accurately and quickly,propose an approach about rolling bearing fault diagnosis,which is based on LS-SVM and LMD.Firstly,decompose the original vibration signal by LMD(Local Mean Decomposition LMD)to get a series of PF(Production Function,PF); secondly,establish the AR model of PF components.And getting autoregressive parameters and residual variance of the AR model through the Burgrecursive algorithm,to constitute feature vector;finally,input the feature vector into the LS-SVM for determining the bearing running state.Experimental results show that: the method can diagnose the bearing failures quickly and accurately.

关键词： Rolling bearings LMD LS-SVM AR model Fault diagnosis

作者: Yongxia Bu Jiande Wu Jun Ma Xiaodong Wang Yugang Fan

作者单位: Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunmi Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunmi

会议类型: 国际会议

会议名称: 第26届中国控制与决策会议(2014 CCDC)

会议地点: 长沙

会议语种:英文

页码: 3797-3801

在线出版日期: 2014-05-31（万方平台首次上网日期，不代表论文的发表时间）