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

Khakzad, Nima; Khan, Faisal; Amyotte, Paul

doi:10.1016/j.ress.2011.03.012

Cited by 618 publications

(320 citation statements)

References 27 publications

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“…the data is rare). However, Khakzad et al (2011) summarize the simplified procedure which consists of graphical and numerical mapping FT into BN is shown in Fig. 3.…”

Section: Bn Model Constructionmentioning

confidence: 99%

“…In fact, many risk-based analysis methods for avoiding casualties and property losses have been developed, including pre-hazard analysis (PHA) (Khakzad et al, 2013;Savage, 2005), fault tree analysis (FTA) (Fink et al, 2014;Johnston, 2000), safety check list (Muttram, 2002), risk matrix analysis and risk probability analysis (Khakzad et al, 2011;An et al, 2011). In particular, The Bayesian network modeling method based on probability statistics is a powerful tool for dealing with the uncertainty and causality of the multiple influencing factors (Holický et al, 2013;Trucco et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy-Bayesian-network-based Safety Risk Analysis in Railway Passenger Transport

Sun

Jia

Yang

et al. 2018

Period. Polytech. Transp. Eng.

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“…the data is rare). However, Khakzad et al (2011) summarize the simplified procedure which consists of graphical and numerical mapping FT into BN is shown in Fig. 3.…”

Section: Bn Model Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy-Bayesian-network-based Safety Risk Analysis in Railway Passenger Transport

Sun

Jia

Yang

et al. 2018

Period. Polytech. Transp. Eng.

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show abstract

“…Compared to the traditional PRA-based methods, such as FTA, ETA and NN, the feature of the backward reasoning technique is unique and matchless in BN inference [16]. Diagnostic analysis aims to obtain the posterior probability distribution of each risk factor using the BN's backward reasoning technique when an accident or failure (T) occurs.…”

Section: Bn-based Risk Analysismentioning

confidence: 99%

Dynamic Risk Assessment in Construction Projects Using Bayesian Networks

Zhang¹,

Wu²,

Skibniewski

et al. 2014

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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“…[9], and the unique bidirectional inference mechanism which can be used either to predict the probability or to update the probability of known variables as well as diagnostic [8]. In recent years, BNs have become popular as a robust alternative to most classical methods such as FT [2,5], ET [10], Bow-tie(BT) [6] etc. In order to represent temporal dependencies, the time-dependency of some random variables that follows a Markov process can be integrated into a dynamic BN.…”

Section: Introduction Of Bn and Dbnmentioning

confidence: 99%

A Bayesian networks approach for event tree time-dependency analysis on phased-mission system

Tao

Jia

2015

Eksploatacja i Niezawodnosc - Maintenance and Reliability

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Article citation info: IntroductionAmong several techniques available to model sequence and quantify the failure probability in probabilistic risk assessment (PRA), event trees (ETs) are the most recognized methods that develop logical relationship among the events leading to the possible consequences, while fault trees (FTs) best represent the logic corresponding to pivotal events (PEs) and estimate the probabilities [16].Dependencies in event tree/ fault tree (E/FT) model are frequently encountered, and, if neglected, may result in an error estimation. Hosseini and Takahashi [4] classify dependencies into two categoriesimplicit and explicit. Explicit dependencies are due to shared basic events (SBEs) such as shared utilities or shared components which appear in more than one corresponding FTs, while the expression of implicit dependencies is a bit vague. Nývlt and Rausand [13] expanded the before-mentioned division to cover more types of dependencies such as common cause failures and cascading effect, and further classified the explicit dependencies with static and dynamic behaviour. However, in practice of aerospace PRA, such as lunar exploration which has the characteristics of the phased-mission system (PMS), ETs are typically used to portray progressions of phase mission over time, and the time interval between pivotal events (PEs) is not negligible, dependencies therefore become phase-dependency (as a subset of time-dependency in this context), and make the E/FT based reliability and risk analysis more difficult [1,13].In ET analysis, not so much work has been done with time-dependency analysis, and the papers cited above are mainly based on the hypothesis about static or time-independent behaviour [1,4,13,23]. PMS reliability attracts substantial attentions, and various techniques have been developed to deal with the phase-dependency. The analytical techniques for the PMS can be classified into two categories: combinatorial models (e.g., mini-components, sum of disjoint phase products, BDD) and state-space transition models (e.g., Markov models, Petri nets) [19,21]. The combinatorial method is based on the JiA M. A Bayesian networks approach for event tree time-dependency analysis on phased-mission system. Eksploatacja i Niezawodnosc -Maintenance and Reliability 2015; 17 (2): 273-281, http://dx.doi.org/10.17531

show abstract

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

Cited by 618 publications

References 27 publications

Fuzzy-Bayesian-network-based Safety Risk Analysis in Railway Passenger Transport

Fuzzy-Bayesian-network-based Safety Risk Analysis in Railway Passenger Transport

Dynamic Risk Assessment in Construction Projects Using Bayesian Networks

A Bayesian networks approach for event tree time-dependency analysis on phased-mission system

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