Reliability Analysis of Common Cause Failure Multistate System Based on CUGF

This paper addresses the problem of mixed uncertainty in the reliability analysis of multistate systems under common cause failure conditions. Combining the cloud model theory, universal generation function (UGF) method, and common cause failure theory, the universal generation function method is ex...

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Bibliographic Details
Published in:Mathematical problems in engineering 2020, Vol.2020 (2020), p.1-14
Main Authors: Yang, Zhi-guo, Jia, Peng, Li, Zhuang, Jinzhang, Jia
Format: Article
Language:English
Subjects:
Common cause failures
Engineering
Expected values
Failure
Failure analysis
Fuzzy sets
Fuzzy systems
Methods
Reliability analysis
Reliability aspects
Statistical analysis
Stochastic models
System reliability
Systems analysis
Theory
Uncertainty
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Summary:This paper addresses the problem of mixed uncertainty in the reliability analysis of multistate systems under common cause failure conditions. Combining the cloud model theory, universal generation function (UGF) method, and common cause failure theory, the universal generation function method is extended based on a probabilistic cloud model, i.e., the cloud universal generation function (CUGF) analysis method. The cloud model represents the random and cognitive uncertainty of the state probability, i.e., mixed uncertainty. Next, through CUGF, according to the calculation rules of cloud operators, we provide steps to obtain the reliability of a multistate system under independent failure and common cause failure conditions and obtain cloud digital features for reliability. The accuracy and feasibility of the method are verified by a numerical example. This paper solves the problem of reliability analysis of multistate systems with mixed uncertainty in unit state probability information under common cause failure conditions. We integrate system multistate, information uncertainty, and common cause failure for reliability analysis to avoid large errors, more in line with a project’s actual situation. We propose new ideas and methods to process randomness and fuzzy information or data in multistate system reliability analysis.
ISSN:1024-123X
1563-5147
DOI:10.1155/2020/4608124