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
More filters
Journal ArticleDOI
Quantitative Risk Analysis on Rail Transportation of Hazardous Materials
TL;DR: In this paper , the authors developed a risk assessment model by incorporating potential health risk factors and the obstacle circumstances, such as population density, route distance from residential areas, and the availability of sensitive third parties for health consequences.
Journal ArticleDOI
Fraud Detection of Bulk Cargo Theft in Port Using Bayesian Network Models
TL;DR: This work proposes a novel data-driven approach for formulating predictive models for detecting bulk cargo theft in ports, and shows that predictive models are effective, with both accuracy and recall values greater than 0.8.
Journal ArticleDOI
Risk assessment of an industrial wastewater treatment and reclamation plant using the bow-tie method
TL;DR: The bow-tie (BT) approach was applied to assess the risk of the WWTP of the Moorchekhort industrial complex (MIC), located in the central part of Iran, and showed a 41% risk of violation from the effluent standard limit in the MIC WWTP.
Journal ArticleDOI
Accident risk-based life cycle assessment methodology for green and safe fuel selection
TL;DR: In this paper, the authors developed a methodology for accident risk-based life cycle assessment (ARBLCA) of fossil fuels by considering both the voluntary and involuntary risks, and demonstrated the application of the developed methodology is demonstrated for liquefied natural gas (LNG) and heavy fuel oil (HFO) as fuels of a hypothetical power plant.
Journal ArticleDOI
Unascertained Measure-Set Pair Analysis Model of Collapse Risk Evaluation in Mountain Tunnels and Its Engineering Application
TL;DR: A novel integrated collapse risk evaluation method for mountain tunnels based on case-based reasoning, rough set theory, and unascertained measure-set pair analysis (UM-SPA) theory is proposed and provided to provide a new idea for collapse risk prediction while constructing mountain tunnels.
References
More filters
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.