Bayesian Networks based approach to enhance GO methodology for reliability modeling of multi-state consecutive-k-out-of-n: F system. (January 2023)

• Record Type: Journal Article
• Authors: Tian-yuan, Ye; Lin-lin, Liu; He-wei, Pang; Yuan-zi, Zhou
• Is Part Of: Reliability engineering & system safety. Volume 229(2023)
• Publication Date: 2023-01
• Subjects: GO methodology -- Multi-state consecutive-k-out-of-n: F system -- Bayesian network -- Reliability analysis -- Failure diagnosis; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.108828
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Definition of new type 18 operator to enhance the model ability of GO methodology for MC( k, n ) system. Optimization of BN structure corresponding to large-scale MC( k, n ) system. Mapping procedure transforming GO model of MC( k, n ) system to corresponding BN. Fault diagnosis and improvement guidance of MC( k, n ) system based on extended GO model and BN. Abstract: As a success-oriented system reliability and safety analysis technique, GO methodology is widely used in the modeling and analysis scenarios of critical safety engineering systems with signal flow, such as nuclear, chemical industry, electric power transportation, wireless network and microwave communication network. Consecutive- k -out-of- n systems are also widely applied in these fields, but due to their relatively complex logic, there are no corresponding graphical modeling and calculation methods. In this paper, the basic GO methodology is extended to support the modeling and analysis of multi-state linear and circular consecutive- k -out-of- n : F system (MLC( k, n ) &MCC( k, n )) models. Two new operators are defined in graphical modeling to represent the two types of complex system logic. The multi-state logic semantics of the new operators and mapping rules to Bayesian networks (BN) are given. And the programmable process is presented to transform the extended GO model into BN, and the reliability of the MLC( k, n )/MCC( k, n ) is calculated. Finally, three cases are used to verify i) the correctness of the method supporting multi-state model, ii) the efficiency and ability of the algorithm, and iii) the forward calculation and reverse diagnosis of the MLC( k, n ) and MCC( k, n ) systems with external shared components.