Machine Learning for Analysis of Real Nuclear Plant Data in the Frequency Domain

Kollias, Stefanos, Yu, Miao and Wingate, James (2022) Machine Learning for Analysis of Real Nuclear Plant Data in the Frequency Domain. Annals of Nuclear Energy, 177 (109293). ISSN 0306-4549

Full content URL: https://doi.org/10.1016/j.anucene.2022.109293

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Machine Learning for Analysis of Real Nuclear Plant Data in the Frequency Domain
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Abstract

Machine Learning is used in this paper for detecting anomalies in nuclear plant reactor cores. The proposed approach first generates large amounts of simulated data with different types of perturbations occurring at various locations in the core. This is achieved using the CORE SIM+ modelling framework, which generates these data in the frequency domain. State-of-the-art machine and deep learning models are then extended and used to successfully perform semantic segmentation of the core, classification and localisation of perturbations. Actual plant data are then considered, provided by two different reactors, including no labels about perturbation existence. A domain adaptation methodology is then developed, which uses self-supervised, or unsupervised learning, so as to align the simulated data with the actual plant data and detect perturbations, whilst classifying their type and estimating their location. Experimental studies illustrate the successful performance of the developed approach and extensions are described that indicate a great potential for further research.

Keywords:machine learning, core monitoring, nuclear plant data, self supervised learning, unsupervised learning
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:College of Science > School of Computer Science
ID Code:50244
Deposited On:25 Jul 2022 10:15

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