An approach to constructing effective training data for a classification model to evaluate the reliability of a passive safety system

Kyungho Jin, Hyeonmin Kim, Seunghyoung Ryu, Seunggeun Kim and Jinkyun Park

Reliability Engineering and System Safety, 2022, vol. 222, issue C

Abstract: Although passive safety systems (PSSs) significantly contribute to the safety of nuclear power plants, evaluating the reliability of PSSs remains difficult due to a lack of data and insufficient understanding of a phenomenon on natural forces, which are driving forces of their safety functions. Within this context, approaches that evaluate functional failures of PSSs by combining Monte Carlo simulation with a thermal-hydraulic code have been proposed. In addition, several studies are trying to develop artificial intelligence models to reduce computational burdens in evaluating functional failures of PSSs. Despite such efforts, a large amount of training data is still required to train the model accurately: class imbalance between success/failure of PSSs exacerbates this problem. To address these issues, this paper proposes a method of obtaining effective training data by dealing with the data imbalance to reduce the number of data required. Case studies were performed to show the effectiveness of the proposed method and confirmed that classification models can be efficiently constructed through effective training data.

Keywords: Nuclear Power Plant; Passive safety system; Reliability evaluation; Functional Failure; Deep Learning; Classification; Class Imbalance (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:222:y:2022:i:c:s0951832022001119

DOI: 10.1016/j.ress.2022.108446

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