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Journal ArticleDOI

Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches

01 Aug 2011-Reliability Engineering & System Safety (Elsevier)-Vol. 96, Iss: 8, pp 925-932
TL;DR: 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.
Citations
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Journal ArticleDOI
TL;DR: In this paper, the main existing safety and reliability challenges in hydrogen systems are reviewed, and the current state-of-the-art in safety analysis for hydrogen storage and delivery technologies is discussed, and recommendations are mentioned to help providing a foundation for future risk and reliability analysis to support safe, reliable operation.

513 citations

Journal ArticleDOI
TL;DR: This paper introduces the application of probability adapting in dynamic safety analysis rather than probability updating, and illustrates how Bayesian network (BN) helps to overcome limitations in BT.

440 citations

Journal ArticleDOI
TL;DR: In this paper, the authors reviewed past progress in the development of methods and models for process safety and risk management and highlighted the present research trends; also it outlines the opinions of the authors regarding the future research direction in the field.

361 citations


Cites background from "Safety analysis in process faciliti..."

  • ...This mapping algorithm has certain limitations when incorporating the dependent failures, and functional uncertainty which is associated with deciding the logical gate, and expert opinion (Khakzad et al., 2011)....

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Journal ArticleDOI
TL;DR: The Bayesian network method provides greater value than the bow-tie model since it can consider common cause failures and conditional dependencies along with performing probability updating and sequential learning using accident precursors.

330 citations

Journal ArticleDOI
TL;DR: This work is focused on using bow-tie model approach in a dynamic environment in which the occurrence probability of accident consequences changes, and uses Bayes’ theorem to estimate the posterior probability of the consequences which results in an updated risk profile.

300 citations

References
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Book
01 Jan 2001
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.
Abstract: Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis. The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams. The reader is introduced to the two types of frameworks through examples and exercises, which also instruct the reader on how to build these models. The book is a new edition of Bayesian Networks and Decision Graphs by Finn V. Jensen. The new edition is structured into two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems. The authors also provide a well-founded practical introduction to Bayesian networks, object-oriented Bayesian networks, decision trees, influence diagrams (and variants hereof), and Markov decision processes. give practical advice on the construction of Bayesian networks, decision trees, and influence diagrams from domain knowledge. give several examples and exercises exploiting computer systems for dealing with Bayesian networks and decision graphs. present a thorough introduction to state-of-the-art solution and analysis algorithms. The book is intended as a textbook, but it can also be used for self-study and as a reference book.

4,566 citations

Journal ArticleDOI
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.

819 citations

Book
01 Jan 1970
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.
Abstract: Probability and Sampling. Continuous Random Variables. Quality and Its Measures. Data and Distributions. Reliability and Rates of Failure. Loads, Capacity, and Reliability. Reliability Testing. Redundancy. Maintained Systems. Failure Interactions. System Safety Analysis. Appendices. Answers to Odd--Numbered Exercises. Index.

727 citations


"Safety analysis in process faciliti..." refers background in this paper

  • ...Knowing the minimal cut-sets, the following considerations would be of great help [31]:...

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  • ...The top event usually represents a major accident causing safety hazards or economic loss [31]....

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  • ...For comprehensive accident scenario analysis and effective safety decision-making, it is necessary to determine the critical primary events and also minimal cut-sets leading to the top event occurrence [31]....

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Journal ArticleDOI
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.

635 citations


"Safety analysis in process faciliti..." refers methods in this paper

  • ...[30] gave an exhaustive statistical review of BN application in different areas such as risk and maintenance analysis, in which BN was qualitatively compared with other methods such as FTs, Markov chains, and Petri nets....

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Book
29 Nov 2012
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.
Abstract: Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification, troubleshooting, and data mining under uncertainty 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 Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his/her level of understanding The techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide

522 citations


"Safety analysis in process faciliti..." refers background in this paper

  • ...While BN reduces the uncertainty of prior beliefs through probability updating, there are other modeling techniques that help to capture some types of uncertainty [37]....

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