Empirical Analysis of Machine Learning Algorithms in Fault Diagnosis of Coolant Tower in Nuclear Power Plants

S. Sharanya () and Revathi Venkataraman ()
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S. Sharanya: SRM Institute of Science and Technology, Department of Computer Science and Engineering
Revathi Venkataraman: SRM Institute of Science and Technology, Department of Computer Science and Engineering

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 1325-1332 from Springer

Abstract: Abstract Nuclear power is one of the promising power sources in developing countries. Because of the disasters that has occurred in Nuclear Power Plants (NPPs), it has become a primary concern for the plant engineers to ensure the safe operation of the plant. The coolant towers are a subsystem of the NPP which is directly linked with the water sources. Faulty operation in coolant tower will degrade the ecosystem and environment around them. Deployment of artificial intelligence, machine learning and anomaly detection algorithms would reduce the human intervention in the plant and also eases the process of condition monitoring. This work gives a detailed empirical analysis of common machine learning classification algorithms with various performance metrics. This work would be of immense help to the plant engineers and the machine learning experts to share their knowledge, which would benefit each other.

Keywords: Nuclear power plant (NPP); Condition monitoring; Coolant towers; Machine learning (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_135

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DOI: 10.1007/978-3-030-41862-5_135

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