IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v15y2024i7d10.1007_s13198-024-02313-y.html
   My bibliography  Save this article

Retrospective and predictive analysis of human operator performance with event report data of a nuclear reactor

Author

Listed:
  • Vipul Garg

    (Bhabha Atomic Research Centre
    Homi Bhabha National Institute)

  • Gopika Vinod

    (Bhabha Atomic Research Centre
    Homi Bhabha National Institute)

  • Vivek Kant

    (Indian Institute of Technology Kanpur)

Abstract

Human Reliability Analysis (HRA) quantifies the likelihood of human operator's erroneous actions towards evaluation of risk emanating from complex systems, in terms of Human Error Probability. The main contribution of this article is in terms of the interactions between retrospective and predictive analysis of operator performance using insights from the recommendations of developing third-generation methods. This article highlights, (1) retrospective analysis lays the foundation of a good predictive analysis for HRA, and reciprocally, (2) a good predictive HRA method should be a replication of a robust retrospective analysis. In order to demonstrate this idea, we present a tool—APPROP (Application for Predictive and Retrospective analysis of Operator Performance). APPROP is a web tool and repository, based on features of Cognitive Reliability and Error Analysis Method and existing methods such as Standardized Plant Analysis Risk HRA (SPAR-H), to help practitioners in HRA data processing through debriefing. Using APPROP, a retrospective analysis was performed on event report data (2006–2020) from an operating nuclear reactor. The retrospective analysis scheme of APPROP enables HRA data processing from multiple sources and facilitates its classification into appropriate categories of context, error modes and error causes. A preliminary quantitative analysis with event report data gathered through APPROP was done using Logistic Regression (LR), Artificial Neural Networks (ANN) and Support Vector Machines based approaches. Preliminary predictive analysis results demonstrate that the LR and ANN-based models have the potential to perform predictive analysis and emulate the retrospective analysis.

Suggested Citation

  • Vipul Garg & Gopika Vinod & Vivek Kant, 2024. "Retrospective and predictive analysis of human operator performance with event report data of a nuclear reactor," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(7), pages 3039-3059, July.
  • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:7:d:10.1007_s13198-024-02313-y
    DOI: 10.1007/s13198-024-02313-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-024-02313-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s13198-024-02313-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ijsaem:v:15:y:2024:i:7:d:10.1007_s13198-024-02313-y. 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.