@conference{10272/25037,
year = {2024},
month = {6},
url = {https://hdl.handle.net/10272/25037},
abstract = {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.},
publisher = {Universidad de Burgos},
title = {Integration of artificial intelligence with automation for predictive maintenance in sustainable hydrogen production plants},
doi = {10.36443/9788418465970},
author = {Abiola, Abiodun and Segura Manzano, Francisca and Andújar Márquez, José Manuel and Barragán Piña, Antonio Javier},
}