RT Conference Proceedings
T1 Integration of artificial intelligence with automation for predictive maintenance in sustainable hydrogen production plants
A1 Abiola, Abiodun
A1 Segura Manzano, Francisca
A1 Andújar Márquez, José Manuel
A1 Barragán Piña, Antonio Javier
AB Automation in modern industries is possible with the aid of sensors that measure signals needed for control, fault detection and decision making about a process. Such decisions include the time to perform predictive maintenance which is not possible when there is a failure in one of the sensors. Artificial intelligence techniques can be used to detect faults in a sensor and predict what its correct reading should be using signals from other sensors involved in the process. For accurate prediction, a signal from an alternative sensor, or a combination of signals from different sensors, should be selected that has a strong correlation with the signal to be predicted. In this study, to demonstrate the application of artificial intelligence in automation, an electrolyser operating in an automated process has been considered. A Deep Reinforcement Learning (DRL) algorithm was developed to select the best signal among others with the highest correlation coefficient of 0.99. The selected signal was then used in a long short-term memory (LSTM) to predict faulty temperature signals in the electrolyser. The root-mean-square error (RMSE) of the predicted signal was 0.1351.
PB Universidad de Burgos
SN 978-84-18465-97-0
YR 2024
FD 2024-06-19
LK https://hdl.handle.net/10272/25037
UL https://hdl.handle.net/10272/25037
LA eng
NO Abiola, A., Segura Manzano, F., Andújar Márquez, J.M., Barragán Piña, A.J.: "Integration of artificial intelligence with automation for predictive maintenance in sustainable hydrogen production plants".  En: editores: Jesús Enrique Sierra García, Mario Peñacoba Yagüe, Pedro J. Cabrera Santana. XIX Simposio CEA de Control Inteligente: libro de actas. (Universidad de Burgos, 19-21 de junio de 2024). ISBN 978-84-18465-97-0. https://doi.org/10.36443/9788418465970
DS Repositorio Institucional de la Universidad de Huelva
RD 30 may 2026