## 1 Introduction
### Background and Previous Works  
The challenge with predictive maintenance is that sensors can fail thus giving false information to the predictive model used. Very limited studies exist within the field of intelligent maintenance of electrolysers that deal with this problem. To address the above-mentioned issues, this study seeks to develop an intelligent predictive maintenance model to forecast sensor data in the electrolyser within some time frame. The model takes input data from other sensors for use in predicting a desired parameter which is referred to as the output of prediction. Hydrogen temperature is one such desired data based on its importance in proton exchange membrane (PEM) electrolysers as discussed in [8; 9]. With the predicted data, an intelligent predictive model then allows monitoring and detection of any worsening condition of the electrolyser before failure. Furthermore, to predict accurately, it is important that only data that have a strong correlation to the output data (hydrogen temperature) are selected, to ensure the accuracy  
Figure 1: Illustration of problem statement and focus of the paper. of prediction and reduction of computation time. Various input data that are captured by different sensors are referred to as features. Some of these features include pressure, voltage, cooling water temperature, and hydrogen temperature. The technique of selecting the correct feature (input) for use in accurate prediction is called feature selection [10]. According to previous works [10; 11], there are various methods that can be used to perform feature selection which includes filters, wrappers, and embedded methods. Artificial intelligence has been used in various studies to perform feature selection such as carried out by [PERSON] et al. [10] and [PERSON] et al. [12]. These papers used the reinforcement learning method to select the best features of a data set for prediction. The authors introduced algorithms to conduct an optimum search of features in the state space. An evaluation function is used to estimate the merit of each state according to selected features. Regarding the scientific literature, very few authors focus their work on addressing the problem of maintenance in PEM electrolysers using artificial intelligence. For example, [PERSON] et al. [13] propose an artificial neural network based on LSTM capable of detecting and localising faults at every time step without any pre-processing. But the artificial intelligence-based faults detection system is only applied to power electronics; that is, the DC/DC converter that supplies the electrolyser, but the neural system is not able to detect faults in the electrolyser. The works carried out by [PERSON] et al. [14] are a clear example of using machine learning to predict the optimal design of PEM electrolytic cells. The machine learning-based model predicts up to eleven different parameters of the electrolytic cell using only four input parameters. In addition to the goodness of the model, it is not useful to detect faults or to define maintenance strategies. But, as discussed earlier [1], to guarantee long-term cost-effectiveness and safety in electrolysers, it is necessary to define prognostics and health management (PHM) protocols that detect damages and helps to prevent catastrophic failure. With this premise, [PERSON] et al. [15] present a PHM model based on machine learning to predict the load voltage of the electrolyser for the state of health information. The voltage is used as a state of health indicator which increases according to the time elapsed, and it is caused by the degradation of the electrolyser. [PERSON] et al. show in [16] the application of artificial neural networks in terms of modelling and simulating the aging process and the degradation of PEM electrolytic stacks. A broader model was developed by [PERSON] et al. in [17], where a data-driven digital-twin model is used to develop a dynamic model for predicting up to three variables (power consumption, hydrogen production, and temperature).