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
More filters
Journal ArticleDOI
Dynamic Bayesian network-based reliability and safety assessment of the subsea Christmas tree
TL;DR: Results show that the annulus loop has the lowest reliability and is the most likely to fail, and corresponding control measures are proposed that can significantly reduce the failure risk of the tree.
Journal ArticleDOI
A dynamic domino effect risk analysis model for rail transport of hazardous material
TL;DR: In this article, a dynamic risk analysis model for analyzing domino effects in RTHM based on Dynamic Bayesian Network is proposed, which can simulate the domino-driven effects in terms of both consequences and probability escalation and dealing with the parameter and model uncertainties.
Journal ArticleDOI
Bayesian framework for reliability prediction of subsea processing systems accounting for influencing factors uncertainty
TL;DR: In this article, a Bayesian network probabilistic framework is suggested for reliability prediction of two conceptual subsea processing systems and a suitable reliability prediction method for mechanical equipment is selected and illustrated stepwise to estimate the total failure rate of the main subsea equipment from reliability data available for similar offshore topside equipment.
Journal ArticleDOI
Risk analysis and maintenance decision making of natural gas pipelines with external corrosion based on Bayesian network
TL;DR: A risk analysis and maintenance decision-making model for natural gas pipelines with external corrosion is proposed based on a Bayesian network that can be used to provide effective maintenance schemes and to reduce the possible losses caused by external corrosion.
Journal ArticleDOI
Risk analysis for consumer-level utility gas and liquefied petroleum gas incidents using probabilistic network modeling: A case study of gas incidents in Japan
Chi Yung Lam,A.M. Cruz +1 more
TL;DR: A probabilistic network modeling approach is proposed in which the inherent characteristics of risk factors for consumer-level gas incidents are considered and these risk factors can be reduced, which may reduce the occurrence of serious gas incidents at the consumer level.
References
More filters
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.