Prognostic Analysis Based on Hybrid Prediction Method for Axial Piston Pump

摘要：

Health monitoring and prognostics of axial piston pump is very helpful for the safety of aerial hydraulic system. Directing to the difficulties of measuring the wear loss between the valve plate and cylinder barrel, this paper presents a hybrid prediction method based on EMD (Empirical Mode Decomposition) and SVM (Support Vector Machine), in which EMD is used to get the pump’s health state and PSO (Particle Swarm Optimization)-SVM is used to make a prediction for the pump’s remaining useful lifetime. Oil-return flow was selected to indicate the wear condition of pump with several IMFs. SVM was trained by these IMFs, and then be used to predict the oil-return flow one-step ahead or multi-step ahead. In the SVM’s training process, PSO was used to search the optimal parameter of SVM’s kernel function. Applications show that hybrid prediction method has higher prediction precision and could be applied for the remaining useful lifetime prognostics.

关键词： failure prognostics axial piston pump particle swarm optimization support vector machine

作者: Zhaomin He Shaoping Wang Kang Wang Kai Li

作者单位: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China Science and Technology on Aircraft Control Laboratory, Beijing 100191, China

会议类型: 国际会议

会议名称: IEEE 10th International Conference on Industrial Informatics(第十届IEEE工业信息学国际学术会议 INDIN2012)

会议地点: 北京

会议语种:英文

页码: 688-692

在线出版日期: 2012-07-25（万方平台首次上网日期，不代表论文的发表时间）