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      Prior Robustness for Bayesian Implementation of the Fault Tree Analysis

      Joshi, Chaitanya; Ruggeri, Fabrizio; Wilson, Simon P.
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      Joshi_Ruggeri_Wilson_IEEETR_Final version.pdf
      Accepted version, 547.0Kb
      DOI
       10.1109/TR.2017.2778241
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      Joshi, C., Ruggeri, F., & Wilson, S. P. (2018). Prior Robustness for Bayesian Implementation of the Fault Tree Analysis. IEEE Transactions on Reliability, 67(1), 170–183. https://doi.org/10.1109/TR.2017.2778241
      Permanent Research Commons link: https://hdl.handle.net/10289/12595
      Abstract
      We propose a prior robustness approach for the Bayesian implementation of the fault tree analysis (FTA). FTA is often used to evaluate risk in large, safety critical systems but has limitations due to its static structure. Bayesian approaches have been proposed as a superior alternative to it, however, this involves prior elicitation, which is not straightforward. We show that minor misspecification of priors for elementary events can result in a significant prior misspecification for the top event. A large amount of data is required to correctly update a misspecified prior and such data may not be available for many complex, safety critical systems. In such cases, prior misspecification equals posterior misspecification. Therefore, there is a need to develop a robustness approach for FTA, which can quantify the effects of prior misspecification on the posterior analysis. Here, we propose the first prior robustness approach specifically developed for FTA. We not only prove a few important mathematical properties of this approach, but also develop easy to use Monte Carlo sampling algorithms to implement this approach on any given fault tree with and and/or or gates. We then implement this Bayesian robustness approach on two real-life examples: a spacecraft re-entry example and a feeding control system example. We also provide a step-by-step illustration of how this approach can be applied to a real-life problem.
      Date
      2018
      Type
      Journal Article
      Publisher
      IEEE
      Rights
      This is the author's accepted version. © 2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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      • Computing and Mathematical Sciences Papers [1457]
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