Predictive maintenance architecture development for nuclear infrastructure using machine learning

• Main Authors: Hardik A. Gohel, Himanshu Upadhyay, Leonel Lagos, Kevin Cooper, Andrew Sanzetenea
• Format: Article
• Language: English
• Published: Elsevier 2020-07-01
• Series: Nuclear Engineering and Technology
• Subjects: Predictive maintenance; Nuclear infrastructure; Machine learning; SVM; LR
• Online Access: http://www.sciencedirect.com/science/article/pii/S1738573319306783

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.