Predictive maintenance architecture development for nuclear infrastructure using machine learning

Gohel Hardik A.; Upadhyay Himanshu; Lagos Leonel; Cooper Kevin; Sanzetenea Andrew

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자료유형: 학술저널

저자정보: Gohel Hardik A. Upadhyay Himanshu Lagos Leonel Cooper Kevin Sanzetenea Andrew

저널정보: 한국원자력학회 Nuclear Engineering and Technology Nuclear Engineering and Technology 제52권 제7호

발행연도: 2020.1

수록면: 1,436 - 1,442 (7page)

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Nuclear infrastructure systems play an important role in national security. The functions and missions of nuclear infrastructure systems are vital to government, businesses, society and citizen's lives. It is crucial to design nuclear infrastructure for scalability, reliability and robustness. To do this, we can use machine learning, which is a state of the art technology used in various fields ranging from voice recognition, Internet of Things (IoT) device management and autonomous vehicles. In this paper, we propose to design and develop a machine learning algorithm to perform predictive maintenance of nuclear infrastructure. Support vector machine and logistic regression algorithms will be used to perform the prediction. These machine learning techniques have been used to explore and compare rare events that could occur in nuclear infrastructure. As per our literature review, support vector machines provide better performance metrics. In this paper, we have performed parameter optimization for both algorithms mentioned. Existing research has been done in conditions with a great volume of data, but this paper presents a novel approach to correlate nuclear infrastructure data samples where the density of probability is very low. This paper also identifies the respective motivations and distinguishes between benefits and drawbacks of the selected machine learning algorithms.

참고문헌 (29)

참고문헌 신청

Ching-Han Chen / 2014 / Big data collection gateway for vision-based smart meter reading network / Proceedings of Big Data, BigData Congress

X. Jin / 2011 / Anomaly detection in nuclear power plants via symbolic dynamic filtering / IEEE Trans. Nucl. Sci 58(1):277~288

H.M. Hashemian / 2011 / Wireless sensors for equipment health and condition monitoring in nuclear power plants / Proc. Future Instrum. Int. Workshop (FIIW) :83~86

H. Gohel / 2018 / Developing Security Intelligence in Big Data Knowledge Computing and its Applications, vol. 1 / Springer Publications

노승국 / 2017 / Big Data Analysis of Public Acceptance of Nuclear Power in Korea / Nuclear Engineering and Technology 49(4):850~854

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