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
Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network
TL;DR: A resilience perspective to understand the safety issues associated with the NSR is provided, which can be seen as the main innovation of this work.
Journal ArticleDOI
A Probabilistic Framework to Evaluate Seismic Resilience of Hospital Buildings Using Bayesian Networks
Jin Liu,Changhai Zhai,Peng Yu +2 more
TL;DR: In this article , the authors proposed a comprehensive framework to evaluate the seismic resilience of hospital buildings, considering the interdependencies on nonstructural components, including critical departments and rooms in the hospital.
Journal ArticleDOI
Embedded Bayesian Network Contribution for a Safe Mission Planning of Autonomous Vehicles
TL;DR: This paper proposes some automatic generation techniques from failure mode and effects analysis (FMEA)-like tables using the pattern design approach and shows how these BN modules can be incorporated into the decision-making model for the mission planning of unmanned aerial vehicles (UAVs).
Journal ArticleDOI
Causation mechanism analysis of excess emission of flue gas pollutants from municipal solid waste incineration power plants by employing the Fault Tree combined with Bayesian Network: A case study in Dongguan
TL;DR: In this article, an exploratory classical Fault Tree and Bayesian Network model is developed for the first time for the excess emission of flue gas (FG) pollutants of MSWI power plants.
Journal ArticleDOI
Exposition and Comparison of Two Kinds of a Posteriori Analysis of Fault Trees@@@استعراض ومقارنة نوعين من التحليل اللاحق لأشجار الأخطاء
TL;DR: It is demonstrated here that in many cases this analysis is still possible via elementary faulttree manipulations that use the concept of a Boolean quotient to effectively implement Bayes’ Theorem in the Boolean domain.
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.