RT Journal Article
T1 A Novel Deep Reinforcement Learning (DRL) Algorithm to Apply Artificial Intelligence-Based Maintenance in Electrolysers
A1 Abiola, Abiodun
A1 Segura Manzano, Francisca
A1 Andújar Márquez, José Manuel
AB Hydrogen provides a clean source of energy that can be produced with the aid of electrolysers.For electrolysers to operate cost-effectively and safely, it is necessary to define an appropriatemaintenance strategy. Predictive maintenance is one of such strategies but often relies on data fromsensors which can also become faulty, resulting in false information. Consequently, maintenancewill not be performed at the right time and failure will occur. To address this problem, the artificialintelligence concept is applied to make predictions on sensor readings based on data obtained fromanother instrument within the process. In this study, a novel algorithm is developed using DeepReinforcement Learning (DRL) to select the best feature(s) among measured data of the electrolyser,which can best predict the target sensor data for predictive maintenance. The features are used asinput into a type of deep neural network called long short-term memory (LSTM) to make predictions.The DLR developed has been compared with those found in literatures within the scope of this study.The results have been excellent and, in fact, have produced the best scores. Specifically, its correlationcoefficient with the target variable was practically total (0.99). Likewise, the root-mean-square error(RMSE) between the experimental sensor data and the predicted variable was only 0.1351.
PB MDPI
SN 1999-4893 (electrónico)
YR 2023
FD 2023-11
LK https://hdl.handle.net/10272/22700
UL https://hdl.handle.net/10272/22700
LA eng
NO Abiola, A., Manzano, F. S., & Andújar, J. M. (2023). A Novel Deep Reinforcement Learning (DRL) Algorithm to Apply Artificial Intelligence-Based Maintenance in Electrolysers. In Algorithms (Vol. 16, Issue 12, p. 541). MDPI AG. https://doi.org/10.3390/a16120541
NO This research was funded by the Spanish Government, grant (1) Ref: PID2020-116616RBC31and grant (2) Ref: RED2022-134588-T REDGENERA.
DS Repositorio Institucional de la Universidad de Huelva
RD 1 jun 2026