A pragmatic approach to estimate alpha factors for common cause failure analysis

Hassija, Varun; Kumar, Chandan; Velusamy, K.

doi:10.1016/j.anucene.2013.07.053

Cited by 13 publications

(5 citation statements)

References 6 publications

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“…When converting the fault trees into a PMS‐BDD, the phase algebra should be adopted to deal with the correlation among phases, it is necessary to consider the relationship between the ordering of variables and the ordering of phases 23 . Assuming B i and B j are the variables of component B in phases i and j respectively, where i < j, the “ite” structures of B i and B j can be expressed as G and F as follows 24,25 :

\begin{equation}{\rm{G = ite(}}{{\rm{B}}_{\rm{i}}}{\rm{,}}{{\rm{G}}_{{B_{{\rm{i}} = {\rm{1}}}}}}{\rm{,}}{{\rm{G}}_{{B_{{\rm{i}} = {\rm{0}}}}}}{\rm{) = ite(}}{{\rm{B}}_{\rm{i}}}{\rm{,}}{{\rm{G}}_{\rm{1}}}{\rm{,}}{{\rm{G}}_{\rm{0}}}{\rm{)}}\end{equation}

\begin{equation}{\rm{F = ite(}}{{\rm{B}}_{\rm{j}}}{\rm{,}}{{\rm{F}}_{{B_{{\rm{j}} = {\rm{1}}}}}}{\rm{,}}{{\rm{F}}_{{B_{{\rm{j}} = {\rm{0}}}}}}{\rm{) = ite(}}{{\rm{B}}_{\rm{j}}}{\rm{,}}{{\rm{F}}_{\rm{1}}}{\rm{,}}{{\rm{F}}_{\rm{0}}}{\rm{)}}\end{equation}

…”

Section: The Modeling Methodsmentioning

confidence: 99%

“…When converting the fault trees into a PMS-BDD, the phase algebra should be adopted to deal with the correlation among phases, it is necessary to consider the relationship between the ordering of variables and the ordering of phases. 23 Assuming B i and B j are the variables of component B in phases i and j respectively, where i < j, the "ite" structures of B i and B j can be expressed as G and F as follows 24,25 :…”

Section: The Construction Of Pms-bddmentioning

confidence: 99%

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A reliability evaluation method for phased‐mission system considering multi‐mechanism coupling within phases

Dong

Chen

et al. 2022

Quality & Reliability Eng

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Due to the difference of environmental stress and system configuration, as well as the coupling between phases, the phased-mission system (PMS) has significant disparity in the failure modes and mechanisms of components in different phases. From the perspective of failure mechanism, an initial independent one will affect or be affected by other failure mechanisms during a task, and eventually leads to the failure of system. Therefore, the coupling of failure mechanisms in PMS is considered in this paper, and the recursive quantitative analysis of coupling from failure mechanism level to system level and phase level are accomplished by means of the basic rules of reliability physics. Furthermore, taking a core circuit from an aircraft electronic system as an example, the reliability modeling and analysis are performed. The results show that after considering the coupling relationship between failure mechanisms within each phase, the reliability and lifetime of components are reduced compared with the mechanismindependence situation, however, the cumulative failure probability of the system is increased. It reveals that it is very important to involve the coupling relationship between failure mechanisms in the reliability analysis of PMS, which can improve the accuracy of the prediction.

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\begin{equation}{\rm{G = ite(}}{{\rm{B}}_{\rm{i}}}{\rm{,}}{{\rm{G}}_{{B_{{\rm{i}} = {\rm{1}}}}}}{\rm{,}}{{\rm{G}}_{{B_{{\rm{i}} = {\rm{0}}}}}}{\rm{) = ite(}}{{\rm{B}}_{\rm{i}}}{\rm{,}}{{\rm{G}}_{\rm{1}}}{\rm{,}}{{\rm{G}}_{\rm{0}}}{\rm{)}}\end{equation}

\begin{equation}{\rm{F = ite(}}{{\rm{B}}_{\rm{j}}}{\rm{,}}{{\rm{F}}_{{B_{{\rm{j}} = {\rm{1}}}}}}{\rm{,}}{{\rm{F}}_{{B_{{\rm{j}} = {\rm{0}}}}}}{\rm{) = ite(}}{{\rm{B}}_{\rm{j}}}{\rm{,}}{{\rm{F}}_{\rm{1}}}{\rm{,}}{{\rm{F}}_{\rm{0}}}{\rm{)}}\end{equation}

…”

Section: The Modeling Methodsmentioning

confidence: 99%

Section: The Construction Of Pms-bddmentioning

confidence: 99%

A reliability evaluation method for phased‐mission system considering multi‐mechanism coupling within phases

Dong

Chen

et al. 2022

Quality & Reliability Eng

View full text Add to dashboard Cite

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“…Furthermore, Zhou et al [33] studied the fault diagnosis of the wind turbine by combining ontology and FMECA and realized knowledge sharing among different stakeholders. Hassija et al [34] adopted the alpha factor model to assess common cause failure in two nuclear power plants. According to their finding, the contribution of common cause failure to failure probability was very sensitive to the change in the criterion.…”

Section: Failure Analysismentioning

confidence: 99%

Analysis of Failures and Influence Factors of Critical Infrastructures: A Case of Metro

Deng

Song

Zhou

et al. 2020

Advances in Civil Engineering

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Metro plays an indispensable role in promoting social and economic development of the big cities. However, the safety operation of the metro system is believed to be an arduous work for its operation being susceptible to various factors, especially equipment failure, which could bring about delay, interruption, and even accidents. In consideration of the seriousness of metro accident, it is becoming more and more crucial to conduct proactive, targeted, and effective risk management to reduce failures. Firstly, based on the FMECA and influence diagram theory, this paper proposes a new analytical framework to study metro equipment failure in accordance with system decomposition. Furthermore, a case study is implemented to verify the feasibility and availability of this framework in evaluating criticality of failure modes and assessing its influence factors based on the collected data from metro operation company. As indicated by the research results, wheel damage is the most serious failure mode in the bogie system, and its most paramount influence factor is the shortage of professional skills. Finally, a discussion about the research methods and results is carried out, and several recommendations are put forward to improve safety management on the basis of research findings. This study has the potential to offer suggestions in regard to metro equipment design, operation, and maintenance for raising the safety level in metro operation.

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“…Guidelines on modeling CCFs [2][3][4][5] in safety and reliability studies and assessing CCF rates 5 have been developed and applied in most engineering probabilistic safety assessments (PSAs). There is a rich literature of a number of specific aspects of CCF analysis, for instance, modeling of one component assigned to several CCF groups, 1,[6][7][8] parameter estimation, [9][10][11] uncertainties' assessment, [12][13][14] safety analysis for special system (e.g. voting system) including CCFs in condition of system reconfiguration, 15 and human and organization factors that influence evaluation.…”

Section: Introductionmentioning

confidence: 99%

Common cause failure model updating for risk monitoring in nuclear power plants based on alpha factor model

Zhang

Mosleh

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part O:

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Common cause failure model updating (both qualitatively and quantitatively) is a key factor in risk monitoring for nuclear power plants when configuration changes (e.g. components become unavailable) occur among a redundant configuration. This research focuses on the common cause failure updating based on the alpha factor model method, which is commonly used in the living probabilistic safety assessment models for nuclear power plant risk monitoring. This article first discusses the common cause failure model updating in an ideal condition, which evaluates the common cause failure model parameters for the configurationally changed system in different ways, based on the causes of the detected failures. Then, two alternative updating processes are proposed considering the difficulty to identify failure causes immediately during plant operation: one is to update the common cause failure models with the assumption that the failures detected are independent failures and the other is to update the common cause failure models with the parameters as expectations of the values for all possible failure causes. Finally, a case study is given to illustrate the common cause failure updating process and to compare these two alternative processes. The results show that (1) common cause failures can be reevaluated automatically by the methods proposed in this article and (2) the second process is more conservative and reasonable but with more data requirements compared with the first approach. Considering limitations in accessibility of the data, the first strategy is suggested currently. More future work on data acquisition is demanded for better assessment of common cause failures during nuclear power plant risk monitoring.

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A pragmatic approach to estimate alpha factors for common cause failure analysis

Cited by 13 publications

References 6 publications

A reliability evaluation method for phased‐mission system considering multi‐mechanism coupling within phases

A reliability evaluation method for phased‐mission system considering multi‐mechanism coupling within phases

Analysis of Failures and Influence Factors of Critical Infrastructures: A Case of Metro

Common cause failure model updating for risk monitoring in nuclear power plants based on alpha factor model

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