Prognostic analysis based on hybrid prediction method for axial piston pump

Abstract

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.