Retrieval methodology for similar NPP LCO cases based on domain specific NLP

Autor: No Kyu Seong, Jae Hee Lee, Jong Beom Lee, Poong Hyun Seong
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Nuclear Engineering and Technology, Vol 55, Iss 2, Pp 421-431 (2023)
Druh dokumentu: article
ISSN: 1738-5733
DOI: 10.1016/j.net.2022.09.028
Popis: Nuclear power plants (NPPs) have technical specifications (Tech Specs) to ensure that the equipment and key operating parameters necessary for the safe operation of the power plant are maintained within limiting conditions for operation (LCO) determined by a safety analysis. The LCO of Tech Specs that identify the lowest functional capability of equipment required for safe operation for a facility must be complied for the safe operation of NPP.There have been previous studies to aid in compliance with LCO relevant to rule-based expert systems; however, there is an obvious limit to expert systems for implementing the rules for many situations related to LCO. Therefore, in this study, we present a retrieval methodology for similar LCO cases in determining whether LCO is met or not met. To reflect the natural language processing of NPP features, a domain dictionary was built, and the optimal term frequency-inverse document frequency variant was selected. The retrieval performance was improved by adding a Boolean retrieval model based on terms related to the LCO in addition to the vector space model. The developed domain dictionary and retrieval methodology are expected to be exceedingly useful in determining whether LCO is met.
Databáze: Directory of Open Access Journals