Qingtong Liu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Qingtong Liu has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Hui Ji
Beijing University of Technology
2 shared publications - Songlin Nie
Beijing University of Technology
2 shared publications Ruidong Hong
1 shared publication- Ruidong Hong
Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing, China
1 shared publication Shuang Nie
1 shared publication- Shuang Nie
Edward S. Rogers Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada
1 shared publication
Latest Publications
- Open Access
- 12 August 2022
- Published by MDPI AG in Applied Sciences
- Vol. 12 (16) , 8071
Integration of ARIMA and LSTM Models for Remaining Useful Life Prediction of a Water Hydraulic High-Speed On/Off Valve
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.
- 12 August 2022
- Published by MDPI AG in Applied Sciences
- Vol. 12 (16) , 8071
Open AccessIntegration of ARIMA and LSTM Models for Remaining Useful Life Prediction of a Water Hydraulic High-Speed On/Off Valve
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.