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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.
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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.

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Citations
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Proceedings ArticleDOI

Error Diagnosis of Cloud Application Operation Using Bayesian Networks and Online Optimisation

TL;DR: This paper proposes an error diagnosis approach using Bayesian Networks where each node in the network captures the potential (root) causes of operational errors and its probability under different operational contexts.
Journal ArticleDOI

A comprehensive review on dynamic risk analysis methodologies

TL;DR: Dynamic risk analysis is an advanced continuous real-time approach that deals with rapidly changing circumstances to update and integrate the changing risk levels into overall risk profile as discussed by the authors , which is a solution against the restrictions on conventional static hazard identification and risk estimation techniques.
Journal ArticleDOI

Towards a Systematic Description of Fault Tree Analysis Studies Using Informetric Mapping

TL;DR: This study can help scholars better master the current research status and frontiers of FTA to improve system reliability and safety.
Journal ArticleDOI

An Integrated Risk and Resilience Assessment of Sea Ice Disasters on Port Operation

TL;DR: This study shows that the Gumbel Copula method, with consideration of ice thickness level and area of ice cover, provides the most robust risk assessment outcome, and a grid chart system with an overlap of a joint return period and port throughput change provides a straightforward understanding of resilience performance under different risk levels.
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

Elmer E Lewis
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

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.
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