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An effective hybrid method for analysis the large-scale reliability block diagram model
Yi Ren 1
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School of Reliability and Systems Engineering, Beihang University, China
School of Aeronautical Science and Engineering, Beihang University, China
Submission date: 2023-03-22
Final revision date: 2023-06-25
Acceptance date: 2023-07-08
Online publication date: 2023-07-12
Publication date: 2023-07-12
Corresponding author
Zhifeng Li   

School of Reliability and Systems Engineering, Beihang University, Xueyuan RD., 100191, Beijing, China
Eksploatacja i Niezawodność – Maintenance and Reliability 2023;25(3):169408
  • This paper proposes extended diagrams (e.g., plus and multi-functional structures).
  • A structure identification method is proposed for large-scale RBD.
  • An analysis method based on BDD is proposed to enhance the efficiency of RBD.
The reliability block diagram (RBD) is a graphical tool used for reliability modeling and analysis in various industries, including shipbuilding, aviation, and aerospace. Typically, RBDs are transformed into Bayesian networks for quantitative analysis of systems. Bayesian networks are probabilistic graphical models that can capture the uncertainties and causal relationships in complex systems. They can provide various reliability metrics such as failure probability, mean time to failure, availability, etc. However, these techniques have several drawbacks, especially for large-scale models, such as being extremely time and memory-consuming. To address these issues, we propose a hybrid method for quantitative analysis of large-scale RBDs based on the structure identification approach and binary decision diagrams. Theoretical analysis and case verification demonstrate that the proposed method is significantly more efficient than the current one.
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