An effective hybrid method for analysis the large-scale reliability block diagram model

• Autorzy: Ping Yashi , Ren Yi , Li Zhifeng , Yang Dezhen , Yang Chao

Identyfikatory

DOI

10.17531/ein/169408

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

reliability block diagram; structure identification; plus structure; binary decision diagram

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2023
• Tom: Vol. 25, no. 3
• Strony: art. no. 169408
• Opis fizyczny: Bibliogr. 26 poz., rys., tab., wykr.

Twórcy

autor

Ping Yashi

• School of Reliability and Systems Engineering, Beihang University, China

autor

Ren Yi

• School of Reliability and Systems Engineering, Beihang University, China

autor

Li Zhifeng

• School of Reliability and Systems Engineering, Beihang University, China

• https://orcid.org/0009-0006-8991-7115

autor

Yang Dezhen

• School of Reliability and Systems Engineering, Beihang University, China

autor

Yang Chao

• School of Aeronautical Science and Engineering, Beihang University, China

Bibliografia

• 1. Bobbio A, Portinale L, Minichino M, et al. Improving the analysis of dependable systems by mapping fault trees into Bayesian networks. Reliability Engineering & System Safety 2001; 71(3): 249-260, https://doi.org/10.1016/S0951-8320(00)00077-6.
• 2. Bhattacharjya D, Deleris L. From reliability block diagrams to fault tree circuits. Decision Analysis 2012; 9(2): 128-137, https://doi.org/10.1287/deca.1120.0231.
• 3. Byun J, Zwirglmaier K, Straub D, et al. Matrix-based Bayesian network for efficient memory storage and flexible inference. Reliability Engineering & System Safety 2019; 185: 533-545, https://doi.org/10.1016/j.ress.2019.01.007.
• 4. Carnevali L, Ciani L, Fantechi A, et al. An efficient library for reliability block diagram evaluation. Applied Sciences 2021; 11(9): 4026, https://doi.org/10.3390/app11094026.
• 5. Dugan J. Automated analysis of phased-mission reliability. IEEE Transactions on Reliability 1991; 40: 45-52, https://doi.org/10.1109/24.75332.
• 6. Guo H, Yang X. A simple reliability block diagram method for safety integrity verification. Reliability Engineering & System Safety 2007; 92(9): 1267-1273, https://doi.org/10.1016/j.ress.2006.08.002.
• 7. Jung W, Han S, Ha J. Development of an efficient BDD algorithm to solve large fault trees. Spitzer, C, Schmocker, U, Dang, V.N. (eds) Probabilistic Safety Assessment and Management. Springer, London 2004, https://doi.org/10.1007/978-0-85729-410-4_539.
• 8. Jia X, Cui L, Xing L. New insights into reliability problems for supply chains management based on conventional reliability model. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2018; 20 (3): 465–470, http://dx.doi.org/10.17531/ein.2018.3.16.
• 9. Karimi S, Zadeh M, Suul J A, et al. Reliability analysis of shore-to-ship fast charging systems. 2021 IEEE Transportation Electrification Conference & Expo (ITEC), Chicago, IL, USA, 2021; 539-545, https://doi.org/10.1109/ITEC51675.2021.9490146.
• 10. Lee C. Representation of switching circuits by binary-decision programs. The Bell System Technical Journal 1959; 38: 985–999, https://doi.org/10.1002/j.1538-7305.1959.tb01585.x
• 11. Lu, J.M., et al., FLEXIBLE TRUNCATION METHOD FOR THE RELIABILITY ASSESSMENT OF PHASED MISSION SYSTEMS WITH REPAIRABLE COMPONENTS. Eksploatacja I Niezawodnosc-Maintenance and Reliability, 2016. 18(2): p. 229-236, https://dx.doi.org/10.17531/ein.2015.3.8.
• 12. Lu, J.M., et al., TWO-TERMINAL RELIABILITY ANALYSIS FOR MULTI-PHASE COMMUNICATION NETWORKS. Eksploatacja I Niezawodnosc-Maintenance and Reliability, 2016. 18(3): p. 418-427, https://dx.doi.org/10.17531/ein.2017.3.20.
• 13. Montani S, Portinale L, Bobbio A, et al. Radyban: A tool for reliability analysis of dynamic fault trees through conversion into dynamic Bayesian networks. Reliability Engineering & System Safety 2008; 93(7): 922-932, https://doi.org/10.1016/j.ress.2007.03.013.
• 14. Mi J, Li Y, Beer M, Broggi M, Cheng Y. Importance measure of probabilistic common cause failures under system hybrid uncertainty based on Bayesian network. Eksploatacja i Niezawodność – Maintenance and Reliability 2020; 22(1): 111-120. https://doi.org/10.17531/ein.2020.1.13.
• 15. Rauzy A. New algorithms for fault trees analysis. Reliability Engineering & System Safety 1993; 40: 203-211, https://doi.org/10.1016/0951-8320(93)90060-C.
• 16. Rauzy A. Toward an efficient implementation of the MOCUS algorithm. IEEE Transactions on Reliability 2003; 52(2): 175-180, https://doi.org/10.1109/TR.2003.813160.
• 17. Rodrigues A, Bhering F, et al. Bayesian estimation of component reliability in coherent systems. IEEE Access 2018; 6: 18520-18535, https://doi.org/10.1109/ACCESS.2018.2821102.
• 18. Torres-Toledano J, Sucar L. Bayesian networks for reliability analysis of complex systems. Progress in Artificial Intelligence-IBERAMIA 98 1998; 195-206, https://doi.org/10.1007/3-540-49795-1_17.
• 19. Tchangani A. Reliability analysis using Bayesian networks. Studies in Informatics and Control 2001; 10(3): 181-188.
• 20. Tu, J.L., R.F. Cheng, and Q.X. Tao, RELIABILITY ANALYSIS METHOD OF SAFETY-CRITICAL AVIONICS SYSTEM BASED ON DYNAMIC FAULT TREE UNDER FUZZY UNCERTAINTY. Eksploatacja I Niezawodnosc-Maintenance and Reliability, 2015. 17(1): p. 156-163, https://dx.doi.org/10.17531/ein.2017.2.12.
• 21. Trivedi K. Probability and statistics with reliability, queuing, and computer science applications. John Wiley & Sons, Inc 2016, https://doi.org/10.1002/9781119285441.
• 22. Wu Z, Yao Y, Wang D. The reliability modeling of marine power station. Applied Mechanics and Materials 2013；427-429, 404-407, https://doi.org/10.4028/www.scientific.net/AMM.427-429.404.
• 23. Wu, X.Y. and X.Y. Wu, MISSION RELIABILITY MODELING AND EVALUATION OF MULTI-MISSION PHASED MISSION SYSTEM BASED ON EXTENDED OBJECT-ORIENTED PETRI NET. Eksploatacja I Niezawodnosc-Maintenance and Reliability, 2017. 19(2): p. 244-253m https://dx.doi.org/10.17531/ein.2015.1.20.
• 24. Zhang L, Guo H. Introduction to Bayesian networks. Science Press 2006.
• 25. Zaitseva, E., V. Levashenko, and J. Kostolny, APPLICATION OF LOGICAL DIFFERENTIAL CALCULUS AND BINARY DECISION DIAGRAMIN IMPORTANCE ANALYSIS. Eksploatacja I Niezawodnosc-Maintenance and Reliability, 2015. 17(3): p. 379-388, https://dx.doi.org/10.17531/ein.2016.3.14.
• 26. Zhang, S., et al., A DECISION DIAGRAM BASED RELIABILITY EVALUATION METHOD FOR MULTIPLE PHASED-MISSION SYSTEMS. Eksploatacja I Niezawodnosc-Maintenance and Reliability, 2017. 19(3): p. 485-492, https://dx.doi.org/10.17531/ein.2016.2.10.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).