A traceable process to develop Bayesian networks from scarce data and expert judgment: A human reliability analysis application. (February 2023)

• Record Type: Journal Article
• Authors: Podofillini, Luca; Reer, Bernhard; Dang, Vinh N.
• Is Part Of: Reliability engineering & system safety. Volume 230(2023)
• Publication Date: 2023-02
• Subjects: Bayesian belief networks -- Human reliability analysis -- Event and accident analysis -- Errors of commission -- Expert judgement; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.108903
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: The BBN quantifies errors of commission for probabilistic safety assessment. We propose a novel process for the quantification of the BBN parameters. The process allows traceable integration of expert knowledge and (scarce) data. Use of to two data sets allows some intermediate validation. Abstract: The present paper develops a Bayesian Belief Network (BBN) for quantification of aggravating actions, as outcomes of inappropriate decisions, to be integrated in probabilistic safety assessment (PSA) models (i.e., the so-called errors of commission, EOCs). The BBN connects analyst ratings on influencing factors to the error forcing impact of a specific scenario, supporting the CESA-Q method (the Quantification module of the Commission Error Search and Assessment method). While contributing to the quantification of EOCs, this paper presents a novel process for the quantification of the BBN parameters (the Conditional Probability Distributions, CPDs), striving for traceable integration of expert knowledge and (scarce) data, in the form of retrospective analyses of operational events involving EOCs. The process combines the functional interpolation method for populating CPDs and Bayesian updates to adjust the BBN response to the available evidence. A first, prior BBN is developed, then sequentially updated to adjust to two data sets. This allows some intermediate validation and puts forwards the steps for future BBN updates as new EOC events (or new analyst assessments) become available.