A Synthesis-Component-Based GO-FLOW Modeling Paradigm for Automated Time-Dependent and Phased-Mission Reliability Model Generation and Analysis

Automatic modeling is an efficient way to ensure the consistency and validity of system reliability and risk models. In this article, an innovative synthesis-component-based modeling paradigm is proposed for automated GO-FLOW model generation from piping and instrumentation diagrams. Within the automata modeling paradigm, a collection of componentized GO-FLOW models for general types and common failure modes of components is first developed to facilitate model transformation mapping. Then, an algorithmic procedure for automated GO-FLOW model generation is elaborated and extended for configuration risk management. Finally, the algorithmic paradigm is demonstrated and verified through comparisons among three different modeling strategies applying to a case study of a simplified safety injection system in pressurized water reactor designs. The comparison results show that the component-based GO-FLOW modeling paradigm is capable of efficient reliability modeling and analysis of time-dependent and phased-mission systems. The consistency, accuracy, and credibility of system reliability and risk models can be effectively guaranteed for configuration risk management with the unified and standardized component-based GO-FLOW modeling paradigm. The automated GO-FLOW model builder is also supportive for reliability-based system design optimization.

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Publication Details

Journal
IEEE Transactions on Reliability
Published
2024-10-24
DOI
https://doi.org/10.1109/tr.2024.3462451
Citations
1
Primary Topic
Software Reliability and Analysis Research
Type
article
Field-Weighted Citation Impact
0.49

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article

A Synthesis-Component-Based GO-FLOW Modeling Paradigm for Automated Time-Dependent and Phased-Mission Reliability Model Generation and Analysis

Jun Yang, Zhanyu He, Yongyue Chu
1 citations
IEEE Transactions on Reliability
Software Reliability and Analysis Research
0.49
article

A Synthesis-Component-Based GO-FLOW Modeling Paradigm for Automated Time-Dependent and Phased-Mission Reliability Model Generation and Analysis

Jun Yang, Zhanyu He, Yongyue Chu
article en
1 citations

Abstract

Automatic modeling is an efficient way to ensure the consistency and validity of system reliability and risk models. In this article, an innovative synthesis-component-based modeling paradigm is proposed for automated GO-FLOW model generation from piping and instrumentation diagrams. Within the automata modeling paradigm, a collection of componentized GO-FLOW models for general types and common failure modes of components is first developed to facilitate model transformation mapping. Then, an algorithmic procedure for automated GO-FLOW model generation is elaborated and extended for configuration risk management. Finally, the algorithmic paradigm is demonstrated and verified through comparisons among three different modeling strategies applying to a case study of a simplified safety injection system in pressurized water reactor designs. The comparison results show that the component-based GO-FLOW modeling paradigm is capable of efficient reliability modeling and analysis of time-dependent and phased-mission systems. The consistency, accuracy, and credibility of system reliability and risk models can be effectively guaranteed for configuration risk management with the unified and standardized component-based GO-FLOW modeling paradigm. The automated GO-FLOW model builder is also supportive for reliability-based system design optimization.

IEEE Transactions on ReliabilityVol. 74(3)
Nuclear and Radiation Safety Center (CN), South China University of Technology (CN)
National Natural Science Foundation of China, Soft Science Research Project of Guangdong Province
Openalex Percentile: Top 30%
Software Reliability and Analysis Research
0.49
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A Synthesis-Component-Based GO-FLOW Modeling Paradigm for Automated Time-Dependent and Phased-Mission Reliability Model Generation and Analysis — Jun Yang, Zhanyu He, et al. · IEEE Transactions on Reliability (2024) | TGRS Research Map | TGRS