## 1 Introduction  
The hydrogen technology deployment is heavily dependent on cost-effectiveness. Regarding hydrogen-based microgrids, their economic impact is conditioned by costs of different nature (investment costs, operation and maintenance costs, and replacement costs) [1]. Among them, operation and maintenance are the ones that are repeated along the lifespan and affect the replacement costs. If equipment is not properly maintained, it arrives early at the end of its lifespan, and the equipment will need to be replaced much earlier.  
In the case of electrolysers, they can only function effectively to produce hydrogen at the desired parameters if their components do not fail during the period of operation. Failures can be avoided if they are detected early during operation and resolved. The process of taking actions to monitor, detect, and resolve failures of a system is known as maintenance practice, as defined according to EN13306 [2]. Adequate maintenance of electrolysers will guarantee optimum operation at the designed level of efficiency, long-term cost-effectiveness, and safety.  
There are various maintenance strategies of which the main categories are corrective maintenance, preventive maintenance, and predictive maintenance [3]. Corrective maintenance involves technical interventions carried out after a fault or damage has occurred, while preventive maintenance involves restorative actions performed at scheduled times to maintain the electrolyser in good condition. The challenge with preventive maintenance is that it does not consider the condition of the electrolyser before being deployed.  
There are cases where the electrolyser is still in good condition and when maintenance is deployed too early, it results in a waste of resources such as labour and spares. Predictive maintenance was introduced as an advancement of preventive maintenance by predicting when failure will occur in the electrolyser. With the aid of such a predictive approach, labour, and spares are deployed just before the occurrence of faults [4]. The benefit of this type of maintenance strategy is that the electrolyser is able to operate for the designed lifespan. Predictive maintenance monitors the condition of the electrolyser using sensors embedded to capture data about its condition. The sensor data are analysed and used to predict imminent failure.  
Various literature discussed predictive maintenance concepts as applied to electrolysis. In the study by [PERSON] et al. [5], a PEM electrolyser was investigated using electrochemical techniques to individualise key degradation issues and predict MEA endurance under real-life operation. Also, [PERSON] et al. [6] investigated iron contaminations in feedwater for predictive maintenance considering that if not detected on time, can lead to accumulation both on the membrane and the catalytic layer, leading to increased ohmic and charge transfer resistance. Also [7; 8] discussed the use of temperature in predictive maintenance to determine membrane drying and associated faults.  
This paper presents a novel algorithm developed using Deep Reinforcement Learning (DRL) hybridised with a Long Short-term Memory (LSTM) neural network for artificial intelligent maintenance of an electrolyser. Figure 1 illustrates the focus of the paper.