• Login
    View Item 
    •   eScholar Home
    • Faculty of Science
    • Master Theses & Projects
    • View Item
    •   eScholar Home
    • Faculty of Science
    • Master Theses & Projects
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Predictive analytics for maintenance activities in nuclear power plants: a feasibility study

    Thumbnail
    View/Open
    Khurmi_Rajinder.pdf (7.842Mb)
    Date
    2021-12-01
    Author
    Khurmi, Rajinder
    Metadata
    Show full item record
    Abstract
    Nuclear power plants are known for their use of legacy systems and processes. As plants age, the amount of maintenance increases while resources remain finite, leading to unwanted delays, affecting the health of assets and increasing costs. To aid in the modernization and digitization of nuclear power plants, this work explores data driven methods, including statistical and machine learning techniques to predict target variables. Representative Naval Propulsion Plant data with variables similar to that in the nuclear industry are used as nuclear data is not available in the public domain. Experimental results confirm target variables can be predicted with relatively high accuracy, with Deep Learning methods harbouring the lowest relative error. Two frameworks are developed based on results to showcase how predictive analytics can be used in nuclear power plant maintenance. This work is a proof of concept informing stakeholders that data driven approaches are viable in reducing maintenance delays.
    URI
    https://hdl.handle.net/10155/1415
    Collections
    • Electronic Theses and Dissertations [1369]
    • Master Theses & Projects [302]

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV
     

     

    Browse

    All of eScholarCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV