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
Accident and pollution risk assessment for hazardous cargo in a port environment.
TL;DR: In this paper, the authors focused on the assessment of multifactor risks associated with the dealing of hazardous cargos inside a port and found that under normal circumstances, the probability of an accident with considerable consequences is 59.8.
Journal ArticleDOI
Research on Risk Assessment based on Bayesian Network
Journal ArticleDOI
Fuzzy-Bayesian-network-based safety risk analysis in railway passenger transport
TL;DR: A fuzzy Bayesian network (FBN) method is presented to analyze the influence on the safety risk of railway passenger transport applying different risk control strategies and can reasonably predict the probability of rail passenger safety risk.
Journal ArticleDOI
A review on the realization methods of dynamic fault tree
Chengyuan Zhu,Tianyuan Zhang +1 more
TL;DR: The research status of different realization methods/analysis techniques of DFT is comprehensively reviewed and the future development direction and challenges of D FT are prospected.
Dissertation
Human factor risk assessment of a maintenance operation in offshore process system
TL;DR: In this article, the authors developed comprehensive methodologies to estimate the human error probability (HEP) in pre and post-maintenance procedures of process facilities and developed a risk-based methodology to investigate the reliability of human performance in harsh and cold environments.
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.