Integration of ARIMA and LSTM Models for Remaining Useful Life Prediction of a Water Hydraulic High-Speed On/Off Valve

Abstract

Some water hydraulic manipulators used for the remote operation of reactors are controlled by a high-speed on/off valve (HSV). Water hydraulic HSVs operate through a process of high-frequency switching, and since their work environment is poorly lubricated, their components are prone to failure. The present study proposed a hybrid model to detect the state and predict the RUL of water hydraulic HSVs used for manipulators, including (1) an HSV state detection method based on the fuzzy neural network (FNN) algorithm; (2) a remaining useful life (RUL) prediction method based on the integration between the autoregressive integrated moving average (ARIMA) model and the long short-term memory (LSTM) model. Final results showed that the accuracy of state detection based on the FNN method was 93.3%. The relative error of the RUL prediction based on the ARIMA–LSTM was less than 1.6%. The developed method can provide guidance for operation and maintenance personnel to plan maintenance reasonably.

Keywords

• ARIMA–LSTM
• RUL prediction
• state detection
• wavelet packet decomposition
• water hydraulic HSV