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
Fault Diagnosis of Train Network Control Management System Based on Dynamic Fault Tree and Bayesian Network
TL;DR: Based on the historical fault data of the TCMS accumulated during their online service, the working principles, failure modes, and effects analysis of TCMS are researched and the dynamic fault tree (DFT) model of TC MS failure is built as mentioned in this paper.
Journal ArticleDOI
Quantitative risk analysis of offshore well blowout using bayesian network
TL;DR: This method provides greater value than the previous models since it can consider the complicated characteristics of geological condition, the whole offshore drilling, completion and workover technologies and operations, surface and subsea BOP common cause failures.
Journal ArticleDOI
Dynamic risk assessment in healthcare based on Bayesian approach
TL;DR: In this model, a static fault tree is established to show risk scenarios, and Dynamic Bayesian network and Bayesian inference are introduced to analyze the operations of medical devices, in consideration of their failures, repairs, and human errors over time.
Journal ArticleDOI
Selecting strategies to reduce high-risk unsafe work behaviors using the safety behavior sampling technique and bayesian network analysis
TL;DR: By holding high quality safety training courses, companies would be able to reduce the rate of HRUBs significantly, and safety training was the most important factor influencing employees' behavior at the workplace.
Journal ArticleDOI
Bayesian Networks Application in Multi-State System Reliability Analysis
Sheng Zhai,Shu Zhong Lin +1 more
TL;DR: This paper will discuss how to establish and construct a multi-state system model based on Bayesian network, and how to apply the prior probability and posterior probability to do the bidirectional inference analysis, and directly calculate the reliability indices of the system by means of prior probabilities and Conditional Probability Table (CPT).
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.