Dynamic Bayesian networks method of safety analysis based on reliability block diagram

Chen, Guobing; Yang, Zichun; Zhao, Jia-Yu; Fei, Zhifang

doi:10.1109/icrms.2014.7107363

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Liu et al 11 studied the subsea blowout preventer (BOP) stack with CCF and evaluated its reliability via a dynamic BN. Chen et al 12 also proposed a dynamic BN‐based method to model the system with CCF configurations. Based on the BN, some methods were proposed to deal with reliability modeling and assessment with epistemic uncertainty 13,14 …”

Section: Introductionmentioning

confidence: 99%

Reliability assessment for systems suffering common cause failure based on Bayesian networks and proportional hazards model

Liu

Huang

et al. 2020

Quality & Reliability Eng

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The Bayesian network (BN) is an efficient tool for probabilistic modeling and causal inference, and it has gained considerable attentions in the field of reliability assessment. The common cause failure (CCF) is simultaneous failure of multiple elements in a system under a common cause, and it is a common phenomenon in engineering systems with dependent elements. Several models and methods have been proposed for modeling and assessment of complex systems with CCF. In this paper, a new reliability assessment method is proposed for the systems suffering from CCF in a dynamic environment. The CCF among components is characterized by a BN, which allows for bidirectional reasoning. A proportional hazards model is applied to capture the dynamic working environment of components and then the reliability function of the system is obtained. The proposed method is validated through an illustrative example, and some comparative studies are also presented.

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Section: Introductionmentioning

confidence: 99%

Reliability assessment for systems suffering common cause failure based on Bayesian networks and proportional hazards model

Liu

Huang

et al. 2020

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…The analytical methods mainly include reliability block diagram method [2,6], failure mode consequence analysis method FMEA [5], fault tree analysis method FTA [8,19] and so on. The model checking based approaches mainly use counterexamples in model checking [14,15].…”

Section: Introductionmentioning

confidence: 99%

Predicting Accidents in Interlocking Systems: An SHA Model-Based Approach

Wang¹

2017

IJPE

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In recent days, rail transit accidents happen from time to time, but the causes are difficult to be found. According to the stochastic and real-time characteristics of equipment faults, three layer models based on stochastic hybrid automata (SHA) are proposed for interlocking systems. The three layer models consist of a system model, a monitoring model and a fault prediction model. The accidents caused by the equipment faults are predicted by simulating these models together on UPPAAL-SMC platform. The main contributions of this paper include: (1) extracting model patterns for interlocking systems (2) presenting a pattern-based system model generation process and an automatic generation method of monitoring model based on time constraints and (3) defining the accidents prediction model of collision accidents to predict the accidents and monitoring accident causes through model simulation.

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Iterative techniques for assessment of electrical power system structural reliability

Oboskalov

Sergey

2016

2016 2nd International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

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Dynamic Bayesian networks method of safety analysis based on reliability block diagram

Cited by 3 publications

References 7 publications

Reliability assessment for systems suffering common cause failure based on Bayesian networks and proportional hazards model

Reliability assessment for systems suffering common cause failure based on Bayesian networks and proportional hazards model

Predicting Accidents in Interlocking Systems: An SHA Model-Based Approach

Iterative techniques for assessment of electrical power system structural reliability

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