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
Probability analysis of offshore fire by incorporating human and organizational factor
TL;DR: The model clearly shows that the model integrates the power of FT for modeling deterministic causal paths with the flexibility of BN for modeling non-deterministic HOF relationships.
Journal ArticleDOI
A holistic approach to control process safety risks: Possible ways forward
TL;DR: Various complexities when applying a holistic control of safety to a process plant in general will be described, and it will more specifically focus on safeguarding measures such as barriers and other controls with some examples.
Journal ArticleDOI
Risk-based optimal safety measure allocation for dust explosions
TL;DR: In this paper, the authors proposed a risk-based optimal allocation of safety measures while considering both available budget and acceptable residual risk, and applied the methodology to the aluminum dust explosion that occurred at Hayes Lemmerz International, Huntington, Indiana, US in October 2003.
Journal ArticleDOI
Network based approach for predictive accident modelling
TL;DR: In this article, a Bayesian network (BN) approach is used to predict the probability of adverse events in real-time industrial data from a liquefied natural gas (LNG) process.
Journal ArticleDOI
Algorithms for Bayesian Network Modeling, Inference, and Reliability Assessment for Multistate Flow Networks
Yanjie Tong,Iris Tien +1 more
TL;DR: A Bayesian network is a useful tool for the modeling and reliability assessment of civil infrastructure systems and for a system comprising many interconnected components, it captures the state of the art in many respects.
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.