Sensitivity analysis on the effect of software-induced common cause failure probability in the computer-based reactor trip system unavailability

In quantitative analysis of common cause failures (CCFs), importance measures for ranking the importance of the CCF events will be significant to limit the CCF analysis framework, find out efficient defence strategies against CCF, and make sensitivity analysis for them. In this paper, three importance measures are defined for different types of common cause failure events: Structure Importance, Probability Importance, and β-Importance. Simplified algorithms for calculating these importance measures are developed.

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Reliability and Sensitivity Analysis Method for a Multistate System with Common Cause Failure

Complexity ◽

10.1155/2019/6535726 ◽

2019 ◽

Vol 2019 ◽

pp. 1-8 ◽

Cited By ~ 6

Author(s):

Jinlei Qin ◽

Zheng Li

Keyword(s):

Sensitivity Analysis ◽

System Reliability ◽

Analysis Method ◽

Common Cause ◽

Common Cause Failure ◽

Resource Limitations ◽

Multistate System ◽

Failure State ◽

Sensitivity Analysis Method ◽

Reliability Assessments

With the increasing complexity of industrial products and systems, some intermediate states, other than the traditional two states, are often encountered during reliability assessments. A system with more than two states is called a multistate system (MSS) which has already become a general phenomenon in the components and/or systems. Moreover, common cause failure (CCF) often plays a very important role in the assessment of system reliability. A method is proposed to assess the reliability and sensitivity of an MSS with CCF. Some components are not only in a failure state that can cause failure itself, but also in a state that can cause the failure of other components with a certain probability. The components that are affected by one type of CCF make up some sets which can overlap on some components. Using the technology of a universal generating function (UGF), the CCF of a component can be incorporated in the expression of its UGF. Consequently, indices of reliability can be calculated based on the UGF expression of an MSS. Sensitivity analysis can help engineers to judge which type of CCF should be eliminated first under various resource limitations. Examples illustrate and validate this method.

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Data Mapping and the Prediction of Common Cause Failure Probability

IEEE Transactions on Reliability ◽

10.1109/tr.2005.847244 ◽

2005 ◽

Vol 54 (2) ◽

pp. 291-296 ◽

Cited By ~ 19

Author(s):

L. Xie ◽

J. Zhou ◽

X. Wang

Keyword(s):

Failure Probability ◽

Data Mapping ◽

Common Cause ◽

Common Cause Failure

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Reliability Evaluation of NPP’s Power Supply System Based on Improved GO-FLOW Method

Science and Technology of Nuclear Installations ◽

10.1155/2016/1245387 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Jie Zhao ◽

Tian Liu ◽

Yu Zhao ◽

Dichen Liu ◽

Xiaodong Yang ◽

...

Keyword(s):

Failure Probability ◽

Power Supply ◽

Power Supply System ◽

Flow Model ◽

Probability Model ◽

Fault Tree Analysis ◽

Supply System ◽

Common Cause ◽

Flow Method ◽

Common Cause Failure

NPP’s power supply system is repairable and there is common cause failure between the components. The repair rate is introduced and total signaling is considered in the improved GO-FLOW method, aimed at reliability analysis for NPP’s power supply system. Traditional GO-FLOW operators’ algorithms are improved. Comprehensively considering the effect of total signaling flow in the power supply system, the equivalent reliability parameter model and common cause failure probability model of multimodal repairable components are constructed. The improved GO-FLOW model of NPP’s power supply system is set up. Based on the proposed model, components’ reliability parameters are computed. The failure probability time-varying trend in thirty years, respectively, of NPP’s offsite power source and power supply system, is simulated and analyzed. Compared with calculation results of dynamic fault tree analysis method, the validity and the simplicity of the improved GO-FLOW method are verified. The effectiveness and applicability of the improved GO-FLOW model for NPP’s power supply system are proved by simulation examples.

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