Journal ArticleDOI
Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches
TLDR
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.About:
This article is published in Reliability Engineering & System Safety.The article was published on 2011-08-01. It has received 573 citations till now. The article focuses on the topics: Fault tree analysis & System safety.read more
Citations
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Journal ArticleDOI
Pilot study of dynamic Bayesian networks approach for fault diagnostics and accident progression prediction in HTR-PM
TL;DR: In this article, a dynamic Bayesian network (DBN) is used to represent complex dynamic systems and taking full consideration of evidences obtained to perform diagnostics and prognostics.
Journal ArticleDOI
Reliability evaluation of a multi-state system based on interval-valued triangular fuzzy Bayesian networks
TL;DR: A reliability analysis method based on an interval-valued triangular fuzzy Bayesian network was proposed, and it was proved that the proposed method was feasible by comparing with T-S fuzzy importance analysis methods and fuzzyBayesian network analysis methods.
Journal ArticleDOI
Safety performance assessment among control room operators based on feature extraction and genetic fuzzy system in the process industry
Leila Omidi,Seyed Abolfazl Zakerian,Jebraeil Nasl Saraji,Esmaeil Hadavandi,Mir Saeed Yekaninejad +4 more
TL;DR: The results of genetic fuzzy system revealed that, among features with the highest merit values, safe work practices and permit–to–work systems, human-computer interface, and staff competence had major effects on safety performance of control room operators.
Book ChapterDOI
Quantitative assessment of risk caused by domino accidents
TL;DR: In this article, a procedure for the quantitative risk assessment of domino accidents, suitable for the calculation of individual and societal risk indexes, is outlined, and alternative approaches proposed for assessing risk posed by escalation events based on Bayesian techniques and Monte Carlo simulations are described.
Journal ArticleDOI
A Total Safety Management framework in case of a major hazards plant producing pesticides
TL;DR: In this paper, the authors present a framework for total safety management through its application to a major hazards chemical plant, namely a pesticides producing unit, and apply it to the storage area of a hazardous substance called dichloropropene, a flammable material used for pesticides production.
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.
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.
Book
Introduction to reliability engineering
TL;DR: Reliability and Rates of Failure, Loads, Capacity, and Reliability, and System Safety Analysis; Quality and Its Measures; and Answers to Odd--Numbered Exercises.
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.
Book
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Uffe Kjærulff,Anders L. Madsen +1 more
TL;DR: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks.