###### 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. ARIMA-LSTM; RUL prediction; state detection; wavelet packet decomposition; water hydraulic HSV +
Footnote †: journal: Medical Informatics  
202212807114 June 202214 June 202  
many tests. They obtained the accuracy of the model by comparing eigenvalues obtained from the test data with the simulation results. [PERSON] et al. [12] proposed a physics-based model to predict future clogging levels and estimate the RUL of fuel filters. They applied their proposed model to a particulate filter to predict the filter clogging level. [PERSON] et al. [13] also presented a fault detection method for wind turbines based on the fuzzy sliding mode observer, which was shown to have good performance. [PERSON] et al. [14] adopted a frequency response analysis and a diagnosis method to increase the accuracy of detection of winding faults within a power transformer. [PERSON] et al. [15] presented a physics-based model of a rolling bearing, which uses a wavelet neural network to predict RUL. The data-driven approach establishes the relationship between system states and system measurements by analyzing previously collected information on the system. The data-driven approach does not rely on a physical model of the system or a-priori knowledge [16]. [PERSON] et al. [17] proposed a gated recurrent unit network that extends particle filters to the case when the measurement equation cannot be established. This method has also been used to predict the RUL of the ball screw. Results have shown that accelerated degradation test systems can accurately predict the degradation performance and RUL of ball screws. [PERSON] et al. [18] used a data-driven fault diagnosis strategy on solid oxide fuel cells to predict their RUL. Their strategy achieved a fault recognition rate of 97% and RUL prediction error of less than 20%. [PERSON] et al. [19] used a data-driven aero-engine performance degradation and RUL prediction strategy. Their strategy was verified using NASA's aero-engine data. Their method accurately predicted the degradation of aero-engine performance. [PERSON] et al. [20] used a fault identification method based on the Kolmogorov-Smirnov test, a self-organizing diagram, and a Kalman filter. Validation experiment results showed that their method can effectively detect degradation of a bearing and predict its RUL. [PERSON] et al. [21] used an enhanced Kalman filter and an expectation maximization algorithm to predict the RUL of bearings. The advantages of data-driven methods are that sufficient prior knowledge is not necessary and there is no need to establish an accurate physical model; state diagnosis can be realized by analyzing changes in state signals. The accuracy of state prediction can be greatly improved using a data-driven model. The data-driven method has many sub methods [22; 23; 24]. The support vector machine (SVM) and relevance vector machine (RVM) models require little training data, but they are not optimal for application to time series problems [25].