Approximate formulas for treating asymmetrical common cause failure events

Kang, Dong Yoon; Hwang, Mee Jeong; Han, Sang Hoon; Yang, Joon-Eon

doi:10.1016/j.nucengdes.2008.10.004

Cited by 7 publications

(5 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The system reliability is usually affected by the combination of three factors: human, machine, and environment, 19 as shown in Figure 1. Machines are directly affected by the environment or personnel during operation, 20 which may lead to independent or common-cause failures of components, [21][22][23][24] thus directly affecting the reliability of the system. Meanwhile, human as one of the critical factors affecting system reliability, 25 the environment and machine will affect the HEP by influencing the performance shaping factors of the personnel.…”

Section: System Reliabilitymentioning

confidence: 99%

“…Kančev et al 21 presented a new method based on beta factor model for explicit modeling of single machine failure event simultaneously within several different commoncause failure groups, and assessed the reliability of systems. Kang et al 22 used the basic parameter method to analyze the common-cause failure of emergency diesel generators. Rejc et al 23 extended the multiple Greek letter model to evaluate the reliability of systems with several redundant components.…”

Section: System Reliabilitymentioning

confidence: 99%

“…In considering the common‐cause failure of machines, many scholars have proposed different models and methods, such as beta factor model, 21 basic parameter model, 22 multiple Greek letter model, 23 and alpha factor model, 24 etc. These methods are widely used in the system reliability assessment.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reliability assessment of man‐machine systems subject to probabilistic common cause errors

Li,

Guo,

Zeng

et al. 2024

Quality & Reliability Eng

View full text Add to dashboard Cite

The occurrence of probabilistic common cause errors (PCCEs) in man‐machine systems can lead to multiple human errors being affected by common causes and will contribute greatly to the reliability of the system. In this paper, A reliability modeling method based on event tree (ET)‐fault tree (FT) model for man‐machine systems subjected to PCCEs is proposed. Under the influence of PCCEs, risk factors in the system are not independent, and human errors affected by the same common cause will occur with different probabilities. To describe the dependencies among risk factors, a PCCE gate is proposed to extend FTs in the ET‐FT model. To analyze the impact of common causes on the human error probabilities and quantify the extended FT, an explicit method and an implicit method based on the Cognitive Reliability and Error Analysis Method are proposed. The proposed quantification methods consider the different relationships among multiple common causes in a single model. Finally, the proposed methods are validated in the reliability assessment of the emergency response system under malfunction of the solar wing mechanism.

show abstract

Section: System Reliabilitymentioning

confidence: 99%

Section: System Reliabilitymentioning

confidence: 99%

See 1 more Smart Citation

Reliability assessment of man‐machine systems subject to probabilistic common cause errors

Li,

Guo,

Zeng

et al. 2024

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…The hybrid BN is used to analyse the system fault model, and an example is given to show that the a factor decomposition process can reduce the uncertainty in probabilistic risk assessment. However, as the parameter estimation models such as a factor and multiple Greek letter CCF model are based on a symmetry assumption that the probabilities of CCF events involving similar components are the same, the basic parameter model of an approximate formula for modelling asymmetric CCF model and estimating its parameters by decomposition method is derived by Kang et al (2009). The approximate formula obtained is applied to fault diagnosis of three emergency diesel generators.…”

Section: Introductionmentioning

confidence: 99%

Optimal fault diagnosis strategy for complex systems considering common cause failure under epistemic uncertainty

Duan

Huang

2021

View full text Add to dashboard Cite

Purpose This paper aims to deal with the problems such as epistemic uncertainty, common cause failure (CCF) and dynamic fault behaviours that arise in complex systems and develop an effective fault diagnosis method to rapidly locate the fault when these systems fail. Design/methodology/approach First, a dynamic fault tree model is established to capture the dynamic failure behaviours and linguistic term sets are used to obtain the failure rate of components in complex systems to deal with the epistemic uncertainty. Second, a β factor model is used to construct a dynamic evidence network model to handle CCF and some parameters obtained by reliability analysis are used to build the fault diagnosis decision table. Finally, an improved Vlsekriterijumska Optimizacija I Kompromisno Resenje algorithm is developed to obtain the optimal diagnosis sequence, which can locate the fault quickly, reduce the maintenance cost and improve the diagnosis efficiency. Findings In this paper, a new optimal fault diagnosis strategy of complex systems considering CCF under epistemic uncertainty is presented based on reliability analysis. Dynamic evidence network is easy to carry out the quantitative analysis of dynamic fault tree. The proposed diagnosis algorithm can determine the optimal fault diagnosis sequence of complex systems and prove that CCF should not be ignored in fault diagnosis. Originality/value The proposed method combines the reliability theory with multiple attribute decision-making methods to improve the diagnosis efficiency.

show abstract

“…This failure mode can be prevented easily by the application of small spreading angles or three rolls in the rolling process [11]. Yang et al [12] found that the use of an internal mandrel prevents axial metal flow, and, as a result, they were able to produce parts with the intended side wall thickness. By eliminating the failure modes characteristic of CWR processes for hollow parts, Ji et al [13] were able to design an innovative method for manufacturing hollow valves via cross-wedge rolling and die forging.…”

Section: Introductionmentioning

confidence: 99%

Cavity formation in cross-wedge rolling processes

Pater

Tomczak

Bulzak

2018

J. Iron Steel Res. Int.

View full text Add to dashboard Cite

The problem of end-face cavity formation in parts produced by cross-wedge rolling was studied in order to reduce material consumption. The cavity depth was measured by the displacement method. Twenty-one different cases of rolling were analysed by finite element method to determine the effects of process parameters such as the wedge tool angle, the temperature of material, the tool velocity and the reduction ratio on the depth of end-face cavities. Relationships between these parameters are examined in order to establish dependencies enabling quick and simple selection of a concavity allowance in order to remove the cavities. The equations for calculating the concavity allowance were verified in an experimental process for manufacturing ball pins with the use of flat tools. Rolling tests were performed using a billet with its length selected in compliance with the established dependencies. The experimental results demonstrate that the proposed solution is a viable method for end-face cavity removal.

show abstract

Approximate formulas for treating asymmetrical common cause failure events

Cited by 7 publications

References 2 publications

Reliability assessment of man‐machine systems subject to probabilistic common cause errors

Reliability assessment of man‐machine systems subject to probabilistic common cause errors

Optimal fault diagnosis strategy for complex systems considering common cause failure under epistemic uncertainty

Cavity formation in cross-wedge rolling processes

Contact Info

Product

Resources

About