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
Data Mining and Equi-Accident Zones for US Pipeline Accidents
Dayakar L. Naik,Ravi Kiran +1 more
TL;DR: Data mining is performed on the last 21 years of United States pipeline accident data to illustrate the trends in different pipeline accident types and their consequences, namely financial consequences of pipeline accidents.
Journal ArticleDOI
Human and organizational failures analysis in process industries using FBN-HFACS model: Learning from a toxic gas leakage accident
Fakhradin Ghasemi,Kamran Gholamizadeh,Amirhasan Farjadnia,Alireza Sedighizadeh,Omid Kalatpour +4 more
TL;DR: In this paper , a hybrid technique of Fuzzy sets theory (FST), Bayesian network (BN), and Human Factors Analysis and Classification System (HFACS) was used to investigate a toxic gas leakage accident quantitatively.
Journal ArticleDOI
Modeling and Testing of Temporal Dependency in the Failure of a Process System
TL;DR: This work demonstrates the suitability and applicability of process-accident models in capturing temporal dependence using process data and investigates their competitive advantages as well as their limitations.
Journal ArticleDOI
A modelling approach based on Bayesian networks for dam risk analysis: Integration of machine learning algorithm and domain knowledge
TL;DR: In this article , a Bayesian Network (BN) is used to predict earthen dams in the USA, which are subsequently modified using domain knowledge (DK) to establish BN models.
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.