An EMD-based long-term LSSVR for machine condition prognostics

摘要：

　　Machine condition prognostics is very important for system safety and condition-based maintenance.Only one-step ahead forecasting of machine condition is considered because of the poor performance of multi-step ahead forecasting.To find effective method for non-stationary long-term machine condition prognostics,this paper firstly reviews multi-step ahead forecasting strategies followed with a comparison of different multi-step ahead forecasting strategies using support vector regression(SVR)and least squares support vector regression(LSSVR)in two datasets,and then develops an recursive multi-step LSSVR(MSLSSVR)model by introducing empirical mode decomposition(EMD)to realize machine condition prognostics.EMD is used to get more stationary signals instead of the non-stationary original signal,and MSLSSVR is constructed to make multi-step prediction with decomposed signals individually.All predicted values are combined eventually to get the future trajectory of condition indicator.Moreover,the proposed algorithm is validated in the TE process.The experimental result shows that the proposed EMD-MSLSSVR model can forecast the failure status in advance,and the performance is satisfied.

关键词： Machine condition prognostics Least square support vector machine Empirical mode decomposition

作者: LI Sai FANG Huajing

作者单位: School of Automation,Huazhong University of Science and Technology,Wuhan 430074,China

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 5133-5138

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）