Nuclear Engineering and Technology

  • 제52권7호
  • /
  • Pages.1436-1442
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  • 2020
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  • 1738-5733(pISSN)
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  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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Predictive maintenance architecture development for nuclear infrastructure using machine learning

  • Gohel, Hardik A. (Computer Science University of Houston) ;
  • Upadhyay, Himanshu (Applied Research Center, Florida International University) ;
  • Lagos, Leonel (Applied Research Center, Florida International University) ;
  • Cooper, Kevin (Indian River State College) ;
  • Sanzetenea, Andrew (Applied Research Center, Florida International University)
  • 투고 : 2019.10.25
  • 심사 : 2019.12.30
  • 발행 : 2020.07.25
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초록

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.

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