• Journal of Korean Society for Quality Management
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• v.46 no.2
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• pp.327-338
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• 2018

Purpose: A risk evaluation procedure is proposed for common failure causes in FMEA(Failure Mode and Effects Analysis). The conventional FMEA does not provide a proper means to compare common failure causes with other failure causes. This research aims to develop a risk evaluation procedure in FMEA where common failure causes and other failure causes exist together. Methods: For each common failure cause, the effect of each combination of its resulting failures is recommended to be reevaluated considering their interactive worsening effect. And the probability that each combination of failures is incurred by the same common cause is also considered. Based on these two factors, the severity of each common cause is determined. Other procedures are similar to the conventional method. Results: The proposed procedure enables to compare and prioritize every failure cause. Thus, the common causes, each of which incurring two or more failures, and other causes, each of which is corresponding to one failure, can be fairly compared. Conclusion: A fair and proper way of comparing the common failure causes and other causes is provided. The procedure is somewhat complicated and requires more works to do. But it is worth to do.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.255-262
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• 2008

The railway is required to be highly reliable, which carries a lot of passenger and baggage. Presently, the reliability prediction method is based on independent failure. If the common cause failure affecting many components simultaneously in a system occurs, the system has seriously an aptitude to be broken out. Therefore, for raising the reliability of the railway power system, it is introduced that the analysis is conducted to use the common cause failure. The common cause failure is modeled and is combined with independent failure. Furthermore in order to examine the method, it is applied to the railway power substation. If this method is used to the power system, the reliability of the railway power system will be highly improved.

• IE interfaces
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• v.25 no.4
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• pp.382-392
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• 2012

Reactor protection system to keep nuclear safety and operational economy of plants requires high reliability. Such a high reliability of the system can be achieved through the redundant design of components. However, common cause failures of components reduce the benefits of redundant design. Thus, the common cause failure analysis, to accurately calculate the reliability of the reactor protection system, is carried out using alpha-factor model. Analysis results to 24 operating months are that 1) the system reliability satisfies the reliability goal of EPRI-URD and 2) the common cause failure contributes 90% of the system unreliability. The uncertainty analysis using alpha factor parameters of 0.05 and 0.95 quantile values shows significantly large difference in the system unreliability.

• International Journal of Reliability and Applications
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• v.11 no.2
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• pp.123-138
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• 2010

This paper investigates a mathematical model of a system composed of two non-identical unit parallel system with common-cause failure, critical human error, non-critical human error, preventive maintenance and two type of repair, i.e. cheaper and costlier. This system goes for preventive maintenance at random epochs. We assume that the failure, repair and maintenance times are independent random variables. The failure rates, repair rates and preventive maintenance rate are constant for each unit. The system is analyzed by using the graphical evaluation and review technique (GERT) to obtain various related measures and we study the effect of the preventive maintenance preventive maintenance on the system performance. Certain important results have been derived as special cases. The plots for the mean time to system failure and the steady-state availability A(${\infty}$) of the system are drawn for different parametric values.

• Nuclear Engineering and Technology
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• v.24 no.1
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• pp.14-29
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• 1992

COMCAF, a computer code for the common-cause failure analysis, is developed to treat the common-cause failures in nuclear power plants. In the treatment of common-cause failures, the minimal cut sets of the system are obtained first without changing the fault-tree structure. The occurrence probabilities of the minimal cut sets are then calculated accounting for the common-cause failures among components in the same minimal cut set or in different minimal cut sets. The basic parameter model is used to model the common-cause failures between similar or identical components. For dissimilar components, the assumption of symmetry used in the basic parameter model is applied to the basic events affecting two or more components. The top event probability is evaluated using the inclusion-exclusion method. In addition to the common-cause failures of components in the same minimal cut sets, failures of components in the different minimal cut sets are also easily accounted for by this method. This study applied this common-cause failure analysis to the PWR auxiliary feedwater system. The results in the top event probability for the system are compared with those of no common-cause failures.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.2
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• pp.63-71
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• 2005

In the field of nuclear reactor safety study, common cause failures (CCFs) became significant contributors to system failure probability and core damage frequency in most Probabilistic risk assessments. However, it is hard to estimate the reliability of such a system, because of the dependency of components caused by CCFs. In order to analyze the system, we propose an analytic method that can find the parameters with lack of raw data. This study adopts the shock model in which the failure probability increases as the shock is cumulated. We use two-step Expectation and Maximization (EM) algorithm to find the unknown parameters. In order to verify the analysis result, we perform the simulation under same environment. This approach might be helpful to build the defensive strategy for the CCFs.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.324-327
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• 2001

This paper shows an example of the Design Review and Common-Cause Failure (CCF) Modeling of mechanical Parts. Reliability should be continuously monitored during the entire period of design. Design Review is the procedure to improve the reliability for the product. We proposed the reliability assessment and design review method. CCF Model is the general dependent model considering the failure mode effects several component simultaneously. This study considers the computation of the network with dependent components. It is important that CCF model is applied for mechanical pars.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.9
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• pp.62-68
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• 2011

Power system needs to sustain high reliability due to its complexity and security. The reliability prediction method is usually based on independent failure. However, in practice, the Common Cause Failures(CCF) and Cascading failure occur to the facilities in power system as well as independent failures in many cases. The CCF and Cascading failure turn out the system collapse seriously in a wide range. Therefore to improve the reliability of the power system practically, it is required that the analysis is conducted by using the CCF and Cascading failure. This paper describes the CCF and Cascading failure modeling combined with independent failure. The incorporated model of independent failure, CCF and cascading failure is proposed and analyzed, and it is applied to the distribution power system in order to examine this method.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.357-367
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• 2021

In general, common cause failures (CCFs) have been modeled with the assumption that components within the same group are symmetric. This assumption reduces the number of parameters required for the CCF probability estimation and allows us to use a parametric model, such as the alpha factor model. Although there are various asymmetric conditions in nuclear power plants (NPPs) to be addressed, the traditional CCF models are limited to symmetric conditions. Therefore, this paper proposes the copulabased CCF model to deal with asymmetric as well as symmetric CCFs. Once a joint distribution between the components is constructed using copulas, the proposed model is able to provide the probability of common cause basic events (CCBEs) by formulating a system of equations without symmetry assumptions. In addition, Bayesian inferences for the parameters of the marginal and copula distributions are introduced and Markov Chain Monte Carlo (MCMC) algorithms are employed to sample from the posterior distribution. Three example cases using simulated data, including asymmetry conditions in total failure probabilities and/or dependencies, are illustrated. Consequently, the copula-based CCF model provides appropriate estimates of CCFs for asymmetric conditions. This paper also discusses the limitations and notes on the proposed method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.4
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• pp.373-378
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• 2012

The objective of this research is to examine the probabilistic approach to evaluating turbine ejection frequency considering common-cause failure. This paper identifies basic turbine ejection mechanisms under high and low speeds and presents a detailed probabilistic methodology (fault tree) for assessing ejection frequency. The alpha factor methodology is applied to common-cause failure evaluations. The frequencies under different test schemes are compared and the propagation of uncertainty through the fault tree model is evaluated. The following conclusions were reached: (1) the turbine blade ejection frequency due to ductile failure under high speed is around 8.005E-7/yr; (2) if common-cause failure is considered, the frequency will be increased by 11% and 33% depending on the test scheme; and (3) if the parameter uncertainties are considered, the frequency is estimated to be in the range of 9.35E-7 to 1.13E 6, with 90% confidence.