EconPapers    
Economics at your fingertips  
 

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

Vipul Garg (), Gopika Vinod and Vivek Kant
Additional contact information
Vipul Garg: Bhabha Atomic Research Centre
Gopika Vinod: Bhabha Atomic Research Centre
Vivek Kant: Indian Institute of Technology Kanpur

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 7, No 16, 3039-3059

Abstract: 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.

Keywords: Human reliability analysis; Retrospective analysis; Predictive analysis; Probabilistic safety assessment; Machine learning (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-024-02313-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:15:y:2024:i:7:d:10.1007_s13198-024-02313-y

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-024-02313-y

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-04-20
Handle: RePEc:spr:ijsaem:v:15:y:2024:i:7:d:10.1007_s13198-024-02313-y