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
A network based approach to envisage potential accidents in offshore process facilities
TL;DR: The proposed method is able to differentiate the consequence of specific events and predict probabilities for such events along with continual updating of consequence probabilities of fire and explosion scenarios taking into account.
Journal ArticleDOI
Assessing seismic vulnerability of urban road networks by a Bayesian network approach
Yun Tang,Shuping Huang +1 more
TL;DR: A Bayesian network (BN) method for seismic vulnerability assessment of an urban road network considering spatial seismic hazard with different levels of ground motion intensities, vulnerability of the components and effect of structural damage of components within the road network to the network functionality is proposed.
Journal ArticleDOI
Process system failure evaluation method based on a Noisy-OR gate intuitionistic fuzzy Bayesian network in an uncertain environment
TL;DR: A Noisy-OR gate Bayesian network method based on intuitionistic fuzzy theory is proposed in cases of imprecise and insufficient historical data, which can provide a more suitable result in an uncertain environment and the weak links of the crude oil tank system are identified through Bayesian reasoning and sensitivity analysis.
Journal ArticleDOI
Analyzing the critical risk factors associated with oil and gas pipeline projects in Iraq
TL;DR: This paper focuses on identifying and analyzing the risks caused by TDP in order to develop a holistic Risk Management Model (RMM), and reveals that terrorism, sabotage, oleum product transportation and theft are the most critical safety risks, official corruption and lawlessness the most influential factors for regulatory risks.
Journal ArticleDOI
Resilience Analysis of a Remote Offshore Oil and Gas Facility for a Potential Hydrocarbon Release.
TL;DR: This study attempts to relate the resilience capacity of a system to the system's absorptive, adaptive, and restorative capacities to influence predisaster and postdisaster strategies that can be mapped to enhance the resilience of the system.
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.