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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Tackling uncertainty in security assessment of critical infrastructures: Dempster-Shafer Theory vs. Credal Sets Theory
TL;DR: Results demonstrate the substantial equivalence of the two methodologies in prognostic analysis and an approximate updating procedure of Evidential network through equivalent Credal network has been proposed, to overcome the lack of possibility to compute updating in the context of Dempster-Shafer Theory.
Journal ArticleDOI
Human reliability assessment for complex physical operations in harsh operating conditions
Nima Golestani,Rouzbeh Abbassi,Rouzbeh Abbassi,Rouzbeh Abbassi,Vikram Garaniya,Mohsen Asadnia,Faisal Khan +6 more
TL;DR: A methodology for quantifying the effect of harsh environmental conditions on the reliability of human actions in performing complex physical operations in extreme environments is presented, based on a hierarchical Bayesian network accounting for causal dependencies among environmental factors, human error modes, and scenario-based activities.
Journal ArticleDOI
Integrated offshore power operation resilience assessment using Object Oriented Bayesian network
TL;DR: This paper identifies the main requirements for an improved resilience of an offshore power management scheme and adopts the object-oriented Bayesian network format to model resilience as a function of anticipated reactions, system adaptability, absorptive capability and restoration.
Journal ArticleDOI
A novel approach for reliability assessment of residual heat removal system for HPR1000 based on failure mode and effect analysis, fault tree analysis, and fuzzy Bayesian network methods
TL;DR: The proposed approach included identifying the events containing failure modes, failure causes, and resulted failures for the RHRS by FMEA, establishing the FT for theRHRS by FTA, and establishing the FBN model for reliability assessment of the RH RS by FBN according to the transformation process from FT toFBN model.
Journal ArticleDOI
An integrated approach for dynamic economic risk assessment of process systems
TL;DR: The Bayesian Tree Augmented Naive Bayes (TAN) algorithm is applied to model the precise and concise probabilistic dependencies that exist among key operational process variables to detect faults and predict the time dependent probability of system deviation.
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.