A REVIEW OF ARTIFICIAL INTELLIGENCE IN NUCLEAR POWER PLANTS
DOI:
https://doi.org/10.18690/jet.17.4.34-45.2024Keywords:
Artificial Intelligence, Machine Learning, Supervised Learning, Unsupervised Learning, nuclear power plant, maintenanceAbstract
Nuclear power plants are recognised as complex systems, where maintenance is critical for ensuring safety and operational stability. Time-based preventive maintenance programmes are employed in most nuclear power plants, relying on periodic inspections to prevent equipment failures. However, this method is considered resource-intensive and not always efficient. An alternative is offered by Artificial Intelligence and condition-based maintenance, which allow early fault detection, reduce unnecessary maintenance tasks, and lower operational costs. The potential of Artificial Intelligence in nuclear power plants is vast, ranging from operational improvements to predictive maintenance. Techniques such as Supervised and Unsupervised Learning are highlighted as essential tools for fault detection, pattern recognition, and predictive modelling. In Supervised Learning, known input-output pairs are used to train models, while Unsupervised Learning is employed to identify hidden patterns in unlabelled data, which is particularly useful in the large, unstructured datasets found commonly in nuclear power plants. The challenges in integrating Artificial Intelligence into nuclear power plant operations shall be noted, including the lack of standardised procedures for selecting and applying algorithms. Despite these challenges, AI-driven tools, including Deep Learning and hybrid models, have shown promising results in fault detection and prediction in nuclear power plants. These advancements support the broader goal of improving safety and operational efficiency. In conclusion, although Artificial Intelligence has not yet been adopted fully across all nuclear power plants, it is seen as a promising advancement for the future of nuclear energy operations. Its implementation enhances fault detection, reduces operational risks, and ensures more reliable energy production.
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