Performance Degradation Prediction for a Hydraulic Servo System Based on Elman Network and Support Vector Regression

摘要

Hydraulic servo system is highly nonlinear. Building an accurate model of the system and predicting its remaining life are difficult. Thus, this study focuses on the prediction of the Hydraulic servo System based on Support vector regression (SVR). Elman neural network is utilized to build an observer to estimate the normal state output. The residual that contains a large amount of fault information is obtained, by calculating the difference between the estimated and actual values. Then we defined degradation index (DI) value which reflect the health of the system to normalize the residual. Lastly, a prediction model based on SVR established. The algorithm is verified by experiment.