An Agentic Artificial Intelligence Observer for Predictive Maintenance in Electrolysers

Abiodun Abiola^*, Francisca Segura, José Manuel Andújar, Antonio Javier Barragán
Research Centre on Technology, Energy and Sustainability, University of Huelva, Huelva, 21071, Spain
* Corresponding Author: Abiodun Abiola. Email: email
(This article belongs to the Special Issue: Intelligent Control and Machine Learning for Renewable Energy Systems and Industries)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2025.070788

Received 24 July 2025; Accepted 09 October 2025; Published online 09 March 2026

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Abstract

This paper presents an artificial intelligence (AI)-based observer that combines fuzzy logic and neural networks to detect abnormalities in sensors embedded in an electrolyser. Electrolysers are hydrogen production plants that require effective maintenance to guarantee suitable operation, prevent degradation, and avoid loss of efficiency. In this sense, predictive maintenance arises as one of the most advisable techniques for maintenance in electrolysers by using sensor data to predict potential abnormalities. However, if the sensor fails, there will be an incorrect forecasting of abnormalities. Among the different types of operational faults that sensors can present are drift-related faults, which are probably the most difficult to detect due to a slow but progressive loss of accuracy in measurements. Another problem with predictive maintenance is that it often requires enormous training data, which is not available at the early stage of plant operation. The developed fuzzy system is responsible for detecting faulty readings arising from drift sensor signals, while the neural network complements the function of the fuzzy system by predicting sensor signals when enough training data are available. The AI-based observer and the fuzzy rules are validated in an experimental case study with a 1 Nm³/h electrolyser. The selected variables are electrolyser temperature and efficiency. Experimental results show that the rules of the fuzzy component of the AI-based observer guarantee an accuracy of ±0.25 within the range of 0 to 1, and the neural network component predicted correct sensor values with a root mean square error (RMSE) as low as 0.0016. The authors’ approach helps to determine drift faults without additional sensors or components installed in the plant.