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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Proceedings ArticleDOI
Dynamic Risk Assessment in Construction Projects Using Bayesian Networks
TL;DR: In this article, a systemic Bayesian network (BN) based approach for dynamic risk assessment for adjacent buildings in tunnel construction is presented, which consists of four steps in detail, namely, hazard analysis, BN learning and BN-based risk analysis.
Матрично-векторные алгоритмы нормировки для локального апостериорного вывода в алгебраических байесовских сетях
A. A. Zolotin,Russian Federation +1 more
TL;DR: A task of local posteriori inference description by means of matrix-vector equations in algebraical Bayesian networks that represent a class of probabilistic graphical models is considered.
Journal ArticleDOI
Parallel Processing for Large-scale Fault Tree in Wireless Sensor Networks
Xinyan Wang,Ruixin Zhang +1 more
TL;DR: A novel strategy which is based on communication delay between sensors, based on fault tree analysis, to solve the problem of self-organizing in the topology and show that net-tree has strong ability in self- organising and extensible.
Journal ArticleDOI
Quantitative risk assessment of college campus considering risk interactions
Xinan Wang,Xiaofeng Hu +1 more
TL;DR: In this article , an integrated model for assessing comprehensive risks on the campus is proposed to put forward risk reduction strategies, and the most sensitive cause is alcohol use in the case of the four sensitive causes simultaneously occurring, the probability of high campus risk will increase from 21.9% to 39.4%.
Journal ArticleDOI
Classification and pattern extraction of incidents: a deep learning-based approach
TL;DR: In this paper , an adaptive moment estimation (ADAME) based DNN was proposed for incident prediction in structured and unstructured text data, where the key terms are extracted from the unstructures using LDA-based topic modeling and then added with the predictor categories to form the feature vector, which is further processed for noise reduction and fed to the AME for classification.
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.