1314 KB0 KB201320252024Friškovec, NejcIgrec, Daliborletnik:17številka:iss. 4str. 34-45ISSN:1855-5748COBISSID_HOST:228208643URN:URN:NBN:SI:doc-SCT2C1AEenFakulteta za energetikoJournal of energy technologyartificial intelligenceJedrske elektrarnemachine learningmaintenancenuclear power plantStrojno učenjesupervised LearningUmetna inteligencaunsupervised learningA review of artificial intelligence in nuclear power plants| Pregled uporabe umetne inteligence v jedrskih elektrarnah|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 productionJedrske elektrarne so poznane kot kompleksni sistemi, njihovo vzdrževanje pa je ključno za zagotavljanje varnosti in zanesljivega obratovanja. Trenutno se v jedrskih elektrarnah uporablja princip časovno zasnovanega vzdrževanja, ki temelji na periodičnih pregledih za preprečevanje okvar. Pomembno je poudariti, da takšen pristop zahteva veliko porabo sredstev in ni vedno učinkovit. Alternativno lahko uvedemo vzdrževanje na podlagi stanja opreme z uporabo umetne inteligence ob predčasnem zaznavanju okvar, s čimer zmanjšamo stroške vzdrževanja in obratovanja. Potencial umetne inteligence v jedrski industriji je velik, od zagotavljanja zanesljive proizvodnje do vzdrževanja. Tehniki, kot sta nadzorovano in nenadzorovano učenje, sta izpostavljeni v članku, saj sta ključno orodje za zaznavanje napak, vzorcev in razvoja preventivnih modelov. Pri nadzorovanem učenju algoritem učimo z znanimi podatki, ki so klasificirani. Pri nenadzorovanem učenju algoritem učimo z veliko količino neklasificiranih podatkov, iz katerih model izlušči vzorce in zaznava odstopanje. Za integracijo umetne inteligence v jedrske elektrarne pa ostaja še veliko izzivov, med drugim tudi pomanjkanje standardnih pristopov. Ne glede na ponujene izzive pa orodja z uporabo umetne inteligence, globokega učenja in hibridnimi modeli obetajo pozitivne rezultate na področju zaznavanja napak in napovedovanja v jedrskih elektrarnah. Takšni napredki izboljšujejo varnost in omogočajo zanesljivo obratovanje. Čeprav umetna inteligenca še ni bila temeljno vpeljana v jedrsko industrijo, prikazuje pozitivne napredke za njeno prihodnost. Njena implementacija povečuje zaznavanje napak, zmanjšuje obratovalna tveganja ter zagotavlja stabilno in zanesljivo proizvodnjo električne energijeTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaUniverza v Mariboru, Fakulteta za energetiko