IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-030-41862-5_135.html

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

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • 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)

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.

Suggested Citation

  • S. Sharanya & Revathi Venkataraman, 2020. "Empirical Analysis of Machine Learning Algorithms in Fault Diagnosis of Coolant Tower in Nuclear Power Plants," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 1325-1332, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_135
    DOI: 10.1007/978-3-030-41862-5_135
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-030-41862-5_135. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.