Dynamic Bayesian network modeling of reliability of subsea blowout preventer stack in presence of common cause failures

Liu, Zengkai; Y, Liu; Cai, Baoping; Zhang, Dawei; Zheng, Chao

doi:10.1016/j.jlp.2015.09.001

Cited by 70 publications

(23 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[ 28 ] introduced an algorithm based on a DBN for a repairable model to evaluate the reliability and security of complex systems. Cai & Liu [ 29 , 30 ] developed a reliability model of subsea blowout preventers to perform a common cause failure analysis based on a DBN. To improve the benefit of combined maintenance, Wang et al .…”

Section: Introductionmentioning

confidence: 99%

Reliability modelling and analysis of a multi-state element based on a dynamic Bayesian network

Li¹,

Xu²,

Gu³

et al. 2018

R. Soc. open sci.

View full text Add to dashboard Cite

This paper presents a quantitative reliability modelling and analysis method for multi-state elements based on a combination of the Markov process and a dynamic Bayesian network (DBN), taking perfect repair, imperfect repair and condition-based maintenance (CBM) into consideration. The Markov models of elements without repair and under CBM are established, and an absorbing set is introduced to determine the reliability of the repairable element. According to the state-transition relations between the states determined by the Markov process, a DBN model is built. In addition, its parameters for series and parallel systems, namely, conditional probability tables, can be calculated by referring to the conditional degradation probabilities. Finally, the power of a control unit in a failure model is used as an example. A dynamic fault tree (DFT) is translated into a Bayesian network model, and subsequently extended to a DBN. The results show the state probabilities of an element and the system without repair, with perfect and imperfect repair, and under CBM, with an absorbing set plotted by differential equations and verified. Through referring forward, the reliability value of the control unit is determined in different kinds of modes. Finally, weak nodes are noted in the control unit.

show abstract

Section: Introductionmentioning

confidence: 99%

Reliability modelling and analysis of a multi-state element based on a dynamic Bayesian network

Li¹,

Xu²,

Gu³

et al. 2018

R. Soc. open sci.

View full text Add to dashboard Cite

show abstract

“…Common cause failure and cascading failure are two typical examples of failure dependency. BN modeling methods considering common cause failure have been extensively researched [25,101]. A cascading failure is a failure in an interconnected system, in which the failure of a part can trigger the failure of successive parts.…”

Section: A Bn Modeling Methods Considering Cascading Failuresmentioning

confidence: 99%

Application of Bayesian Networks in Reliability Evaluation

Cai

Kong

et al. 2019

IEEE Trans. Ind. Inf.

Self Cite

193

View full text Add to dashboard Cite

The Bayesian network (BN) is a powerful model for probabilistic knowledge representation and inference and is increasingly used in the field of reliability evaluation. This paper presents a bibliographic review of BNs that have been proposed for reliability evaluation in the last decades. Studies are classified from the perspective of the objects of reliability evaluation, i.e., hardware, structures, software, and humans. For each classification, the construction and validation of a BN-based reliability model are emphasized. The general procedural steps for BN-based reliability evaluation, including BN structure modeling, BN parameter modeling, BN inference, and model verification and validation, are investigated. Current gaps and challenges in reliability evaluation with BNs are explored, and a few upcoming research directions that are of interest to reliability researchers are identified.

show abstract

“…Cai et al (2012) have shown how a BN can be used for reliability evaluation of subsea blow-out preventer control system. Recently, Liu et al (2015) proposed a method for developing a dynamic Bayesian network model for reliability analysis of subsea blowout preventer stack in the presence of common cause failures. In (Hänninen et al, 2014a;Eleye-Datubo et al, 2008) BNs have been used for safety and reliability analysis in the maritime and offshore industries.…”

Section: Applications As Standalone Approachesmentioning

confidence: 99%

Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review

2019

View full text Add to dashboard Cite

System safety, reliability and risk analysis are important tasks that are performed throughout the system lifecycle to ensure the dependability of safety-critical systems. Probabilistic risk assessment (PRA) approaches are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to, Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). Growing complexity of modern systems and their capability of behaving dynamically make it challenging for classical PRA techniques to analyse such systems accurately. For a comprehensive and accurate analysis of complex systems, different characteristics such as functional dependencies among components, temporal behaviour of systems, multiple failure modes/states for components/systems, and uncertainty in system behaviour and failure data are needed to be considered. Unfortunately, classical approaches are not capable of accounting for these aspects. Bayesian networks (BNs) have gained popularity in risk assessment applications due to their flexible structure and capability of incorporating most of the above mentioned aspects during analysis. Furthermore, BNs have the ability to perform diagnostic analysis. Petri Nets are another formal graphical and mathematical tool capable of modelling and analysing dynamic behaviour of systems. They are also increasingly used for system safety, reliability and risk evaluation. This paper presents a review of the applications of Bayesian networks and Petri nets in system safety, reliability and risk assessments. The review highlights the potential usefulness of the BN and PN based approaches over other classical approaches, and relative strengths and weaknesses in different practical application scenarios.

show abstract

Dynamic Bayesian network modeling of reliability of subsea blowout preventer stack in presence of common cause failures

Cited by 70 publications

References 26 publications

Reliability modelling and analysis of a multi-state element based on a dynamic Bayesian network

Reliability modelling and analysis of a multi-state element based on a dynamic Bayesian network

Application of Bayesian Networks in Reliability Evaluation

Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review

Contact Info

Product

Resources

About