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
Reliability assessment of marine floating structures using Bayesian network
TL;DR: A novel methodology to conduct reliability analysis of moored floating structures using Bayesian network (BN) is proposed and can be employed to mitigate associated risk with marine structures brought about by stochastic hydrodynamic loads.
Journal ArticleDOI
Understanding industrial safety: Comparing Fault tree, Bayesian network, and FRAM approaches
TL;DR: In this article, three approaches to safety are examined: fault trees (FT), Bayesian networks (BN), and the Functional Resonance Analysis Method (FRAM), and a case study of a propane feed control system is used to apply these methods.
Journal ArticleDOI
Developing a risk-based maintenance model for a Natural Gas Regulating and Metering Station using Bayesian Network
TL;DR: An advanced Risk-based Maintenance (RBM) methodology to optimize maintenance time schedule is presented and results prove that the most critical components are the calculator and pilots, while the most reliable one is the odorization.
Journal ArticleDOI
Validation of risk analysis for ship collision in narrow waters by using fuzzy Bayesian networks approach
TL;DR: The proposed Bayesian Network method is applied for the risk modelling of ship collision in narrow waters and is expected to help safety researchers validate their results.
Journal ArticleDOI
Evolution of Safety and Security Risk Assessment methodologies towards the use of Bayesian Networks in Process Industries
TL;DR: A review of evolution of safety risk assessment and security risk assessment methodologies towards Bayesian Networks and its applications in process industries is provided to bring out the strengths and weaknesses of Bayesian networks as opposed to traditional and competing methods and to provide directions for future research.
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.