Journal ArticleDOI
Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network
TLDR
This paper introduces the application of probability adapting in dynamic safety analysis rather than probability updating, and illustrates how Bayesian network (BN) helps to overcome limitations in BT.About:
This article is published in Process Safety and Environmental Protection.The article was published on 2013-01-01. It has received 440 citations till now. The article focuses on the topics: Event tree & Fault tree analysis.read more
Citations
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Journal ArticleDOI
Methods and models in process safety and risk management: Past, present and future
TL;DR: In this paper, the authors reviewed past progress in the development of methods and models for process safety and risk management and highlighted the present research trends; also it outlines the opinions of the authors regarding the future research direction in the field.
Journal ArticleDOI
Quantitative risk analysis of offshore drilling operations: A Bayesian approach
TL;DR: The Bayesian network method provides greater value than the bow-tie model since it can consider common cause failures and conditional dependencies along with performing probability updating and sequential learning using accident precursors.
Journal ArticleDOI
Dynamic risk analysis using bow-tie approach
TL;DR: This work is focused on using bow-tie model approach in a dynamic environment in which the occurrence probability of accident consequences changes, and uses Bayes’ theorem to estimate the posterior probability of the consequences which results in an updated risk profile.
Journal ArticleDOI
Domino effect analysis using Bayesian networks.
TL;DR: This study shows how probability updating helps to update the domino effect model either qualitatively or quantitatively, and accentuates the effectiveness of Bayesian network in modeling domino effects in processing facility.
Journal ArticleDOI
Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures
TL;DR: A methodology based on dynamic Bayesian network is proposed to model both the spatial and temporal evolutions of domino effects and also to quantify the most probable sequence of accidents in a potential domino effect.
References
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Book
Bayesian networks and decision graphs
TL;DR: The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams, and presents a thorough introduction to state-of-the-art solution and analysis algorithms.
Book
Chemical Process Safety: Fundamentals with Applications
Daniel A. Crowl,Joseph F. Louvar +1 more
TL;DR: In this article, the authors present a review of the safety of industrial hygienic products, including Relief Sizing, and a case history of cases of fire and explosion.
Journal ArticleDOI
Improving the analysis of dependable systems by mapping fault trees into Bayesian networks
TL;DR: It is shown that any FT can be directly mapped into a BN and that basic inference techniques on the latter may be used to obtain classical parameters computed from the former, i.e. reliability of the Top Event or of any sub-system, criticality of components, etc.
Journal ArticleDOI
Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas
TL;DR: A bibliographical review over the last decade is presented on the application of Bayesian networks to dependability, risk analysis and maintenance and an increasing trend of the literature related to these domains is shown.
Journal ArticleDOI
Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches
TL;DR: The paper concludes that BN is a superior technique in safety analysis because of its flexible structure, allowing it to fit a wide variety of accident scenarios.