## 1 Introduction
### Background and Previous Works
Based on the literature review carried out, the contributions of this paper are as follows:  
1. A novel algorithm developed using DRL hybridised with LSTM. For predictive maintenance of an electrolyser, not all measured features can accurately predict the target sensor data, as the correlations between different features may be different. Any feature that is not well correlated with the data of interest will give an unassumable error during prediction. Hence, it is important to select appropriate feature(s) that, when used as input to the LSTM predictive model, will provide accurate prediction. In the case of multiple features obtained from the electrolyser, a manual process to select each feature(s) as input for the LSTM model followed by training with several parameter settings to reduce the prediction error is tedious. The developed DRL algorithm solves this problem by quickly searching through the feature set to select the one with the highest correlation to the sensor data (feature) of interest. A very important novelty of the DRL algorithm proposed in this paper lies in the unique method for evaluating each feature during iteration. The evaluation method is based on the comparison between a reference root-mean-square error (RMSE) and another one obtained from selected features. RMSE is an evaluation criterion used for LSTM models [18]. In the DRL algorithm, a simplified initial LSTM with one input and one output layer is used for evaluation. The best feature selected by the DRL algorithm is the one with the lowest RMSE difference, which is then used as input to a main LSTM neural network to predict the sensor data of interest for predictive maintenance. 2. Reduction of computational time when multiple features have to be evaluated. Another important novelty presented by the developed DRL algorithm is that when it has to select a feature set consisting of data from several sensors, it selects the average value, which gives a single representation of the combination. In addition, only a small sample of the feature set is needed. This approach saves computation time. 3. Experimental and real-time operation. The algorithm takes information from experimental data and selects the best dataset (also called feature) that accurately predicts the sensor data of interest. This reinforces the advantages of the hybrid DRL-LSTM model for the maintenance of actual electrolysers. 4. Optimisation of the parameters of the main LSTM prediction model. The parameters of the main LSTM neural network used to predict the target variable are optimised by keeping some constant and varying others to observe the effect on the RMSE. This study provides a graphical guide to selecting the optimal combination for accurate prediction with minimal adjustment and retraining. 5. Digital twin approach. This study also demonstrates the digital twins approach to visualise electrolyser performance by comparing the actual sensor reading with the predicted output to identify electrolyser failures, allowing maintenance to be planned well in advance. Table 1 summarises the main contributions of this study in comparison with other studies found in the scientific literature. The studies are classified based on key contributions and content relating to feature selection, evaluation, and deployment of artificial intelligence for the maintenance of electrolysers.