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
Development and Implementation of a Direct Evaluation Solution for Fault Tree Analyses Competing With Traditional Minimal Cut Sets Methods
TL;DR: In this paper , the authors present several improvements applied to both the MCS and DE approaches in order to upgrade the computing performance of industrial fault tree analysis, which is relevant when considering the industrial, and more specifically the aeronautical, implementation and application of both techniques.
Journal ArticleDOI
Study of Bayesian Network-based Vegetable Traceability Model for Quality Security
Qingcong Zhao,Qingling Zhao +1 more
TL;DR: In order to solve the problem of vegetable product's Quality Security, a Vegetable Traceability Model for Quality Security based on Bayesian network is proposed and the experimental results have shown efficiency and rationality of this modeling.
Proceedings ArticleDOI
Machine learning protocol from ultrasound data for monitoring, predicting, and supporting the analysis of dam slopes
TL;DR: In this paper , a methodology based on machine learning and ultrasound for dam safety monitoring is presented, where a prototype dam was built to simulate different environmental conditions and various machine learning algorithms were applied to distinguish the different regions observed in the prototype dam.
Book ChapterDOI
Analysing the Fault Behavior of a Complex Mechanical System for Diagnosis: A Bond Graph-Based Approach
TL;DR: In this article , a bond graph-based approach is proposed to analyze the mechanical fault behavior for diagnosis, where the analytical model of an engineering system is firstly established via bond graph and the temporal causal graph is derived from the bond graph model to depict the analytic relationships system variables.
Journal ArticleDOI
A review of Bayesian Networks Applications for Electrical Systems
TL;DR: A bibliographic review about the use of Bayesian networks in the field of electric systems finds that reliability assessment and fault diagnosis are the most common fields and that China and USA are the highest active countries in this topic.
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.