RESEARCH PAPER
Bayesian network approach for dynamic fault tree with common cause failures and interval uncertainty parameters
1
School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China, China
2
National Key Laboratory of Aircraft Configuration Design, Xi’an 710072, China
Submission date: 2024-02-12
Final revision date: 2024-04-13
Acceptance date: 2024-06-23
Online publication date: 2024-07-13
Publication date: 2024-07-13
Corresponding author
Shufang Song
School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China, China
School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China, China
Eksploatacja i Niezawodność – Maintenance and Reliability 2024;26(4):190379
HIGHLIGHTS
- Combine BWM and HFS to avoid the influence of expert subjectivity in β-factor model.
- The interval theory is introduced to deal with the uncertainty parameters.
- A framework for reliability evaluation of dynamic fault tree with CCF based on CTBN is proposed.
- The calculation time of proposed method is shorter than that of DTBN-based method.
KEYWORDS
fault tree analysis (FTA)continuous-time Bayesian network (CTBN)common cause failures (CCF)best-worst method (BWM)hesitant fuzzy set (HFS)interval theory
TOPICS
ABSTRACT
Traditional fault tree analysis often assumes that the basic events are independent and the failure parameters are known. Therefore, it is powerless to deal with the correlation among basic events and the uncertainty of failure parameters due to the small failure data. Therefore, a framework based on continuous-time Bayesian network is proposed to evaluate the reliability of fault tree with common cause failures (CCF) and uncertainty parameters. Firstly, the best-worst method (BWM) and hesitant fuzzy set (HFS) are introduced to address the issue of β-factor being influenced by experts’ subjectivity. Then, the interval theory is introduced to deal with the uncertainty parameters. Based on continuous-time Bayesian network, the conditional probability functions of logic gates (i.e. AND gate, OR gate, spare gate, priority AND gate) with CCF are derived, and the upper and lower bounds of failure probability of top event can be solved. Finally, the fault tree of CPU system is given to verify the effectiveness of the proposed framework.
ACKNOWLEDGEMENTS
This work was supported by the National Natural Science Foundation of China (No. 12272316) and the Foundation of National Key Laboratory of Science and Technology on Aerodynamic Design and Research (No. 61422010101).
REFERENCES (36)
1.
Che H, Zeng S, You Q, Song Y, Guo J. A fault tree-based approach for aviation risk analysis considering mental workload overload. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021; 23(4), https://doi.org/10.17531/ein.2....
2.
Zhang J. Reliability analysis of high voltage electric system of pure electric passenger car based on polymorphic fuzzy fault tree. Journal of Intelligent & Fuzzy Systems 2020; 38(4): 3747-3754, https://doi.org/10.3233/JIFS-1....
3.
Li C, Ding L, Zhong B. Highway planning and design in the Qinghai–Tibet Plateau of China: a cost–safety balance perspective. Engineering 2019; 5(2): 337-349, https://doi.org/10.1016/j.eng.....
4.
Matsuoka T. Procedure to solve mutually dependent Fault Trees (FT with loops). Reliability Engineering & System Safety 2021; 214: 107667, https://doi.org/10.1016/j.ress....
5.
Jung S, Yoo J, Lee Y. A software fault tree analysis technique for formal requirement specifications of nuclear reactor protection systems. Reliability Engineering & System Safety 2020; 203: 107064, https://doi.org/10.1016/j.ress....
6.
Dugan J, Bavuso S, Boyd M A. Dynamic fault-tree models for fault-tolerant computer systems. IEEE Transactions on Reliability 1992; 41(3): 363-377, https://doi.org/10.1109/24.159....
7.
Mi J, Lu N, Li Y, Huang H, Bai L. An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties. Reliability Engineering & System Safety 2022; 220: 108295, https://doi.org/10.1016/j.ress....
8.
Shao Q, Yang S, Bian C, Gou X. Formal analysis of repairable phased-mission systems with common cause failures. IEEE Transactions on Reliability 2020; 70(1): 416-427, https://doi.org/10.1109/TR.202....
9.
Cao Y, Liu S, Fang Z, Dong W. Reliability improvement allocation method considering common cause failures. IEEE Transactions on Reliability 2019; 69(2): 571-580, https://doi.org/10.1109/TR.201....
10.
Atwood C. The binomial failure rate common cause model. Technometrics 1986; 28(2): 139-148, https://doi.org/10.1080/004017....
11.
Hamza Z, Abdallah T. Mapping Fault Tree into Bayesian Network in safety analysis of process system. In: Proceedings of 2015 4th International Conference on Electrical Engineering; 2015 Dec 13-15; Boumerdes, Algeria; 2015, https://doi.org/10.1109/INTEE.....
12.
Gu Y, Zhang J, Shen Y, Fan C. Fault tree analysis method based on probabilistic model checking and discrete time Markov Chain. Journal of Industrial and Production Engineering 2019; 36(3): 146-153, https://doi.org/10.1080/216810....
13.
Zhou B, Cai Y, Zang T, Wu J, Sun B, et al. Reliability Assessment of Cyber–Physical Distribution Systems Considering Cyber Disturbances. Applied Sciences 2023; 13(6): 3452, https://doi.org/10.3390/app130....
14.
Boudali H, Dugan J. A new Bayesian network approach to solve dynamic fault trees. In: Proceedings of Annual Reliability and Maintainability Symposium; 2005 Jan 24-27; Alexandria, Virginia, USA; 2005, https://doi.org/10.1109/RAMS.2....
15.
Jun H, Kim D. A Bayesian network-based approach for fault analysis. Expert Systems with Applications 2017; 81: 332-348, https://doi.org/10.1016/j.eswa....
16.
Guo Y, Zhong M, Gao C, Wang H, Liang X. A discrete-time Bayesian network approach for reliability analysis of dynamic systems with common cause failures. Reliability Engineering & System Safety 2021; 216: 108028, https://doi.org/10.1016/j.ress....
17.
Zhang Y, Liang L, Niu W, Song X. Reliability Evaluation of phase-mission Systems Based on discrete-time Bayesian network. In: Proceedings of 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering; 2021 March 26-28; Changsha, China; 2021, https://doi.org/10.1109/AEMCSE....
18.
Boudali H, Dugan J B. A continuous-time Bayesian network reliability modeling, and analysis framework. IEEE Transactions on Reliability 2006; 55(1): 86-97, https://doi.org/10.1109/TR.200....
19.
Yang S, Zeng Y, Li X, Li Y, Huang H. Reliability analysis for wireless communication networks via dynamic Bayesian network. Journal of Systems Engineering and Electronics 2023; 34(5): 1368-1374, https://doi.org/10.23919/JSEE.....
20.
Wang X, Li Y, Li A, Mi J, Huang H. Reliability Modeling and Evaluation for Rectifier Feedback System Based on Continuous Time Bayesian Networks Under Fuzzy Numbers. Journal of Mechanical Engineering 2015; 51(14): 167-174, https://doi.org/10.3901/JME.20....
21.
Dui H, Song J, Zhang Y. Reliability and Service Life Analysis of Airbag Systems. Mathematics 2023; 11(2): 434, https://doi.org/ 10.3390/math11020434.
22.
Sturlaugson L, Sheppard J W. Sensitivity analysis of continuous time Bayesian network reliability models. SIAM/ASA Journal on Uncertainty Quantification 2015; 3(1): 346-369, https://doi.org/10.1137/140953....
23.
Liu M, Hommersom A, van der Heijden M, Lucas PJF. Hybrid time Bayesian networks. International Journal of Approximate Reasoning 2017; 80: 460-474, https://doi.org/ 10.1016/j.ijar.2016.02.009.
24.
Schupbach J, Pryor E, Webster K, Sheppard J. A Risk-Based Approach to Prognostics and Health Management Combining Bayesian Networks and Continuous-Time Bayesian Networks. IEEE Instrumentation & Measurement Magazine 2023; 26(5): 3-11, https://doi.org/10.1109/MIM.20....
25.
Perreault L, Thornton M, Strasser S, Sheppard J. Deriving prognostic continuous time Bayesian networks from D-matrices. In: Proceedings of 2015 IEEE AUTOTESTCON; 2015 November 02-05; National Harbor, MD, USA; 2015, https://doi.org/10.1109/AUTEST....
26.
Bai X, Zan Y, Luo X, Huang K, Guo R. Position Loss Risk Analysis of Dynamic Positioning Systems of Semi-submersible Drilling Units by Considering Time-varying Failure. Journal of Coastal Research 2020; 104(SI): 160-165, https://doi.org/10.2112/JCR-SI....
27.
Li Y, Mi J, Liu Y, Yang Y, Huang H. Dynamic fault tree analysis based on continuous-time Bayesian networks under fuzzy numbers. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2015; 229(6): 530-541, https://doi.org/10.1177/174800....
28.
Sturlaugson L, Sheppard J W. Uncertain and negative evidence in continuous time Bayesian networks. International journal of approximate reasoning 2016; 70: 99-122, https://doi.org/10.1016/j.ijar....
29.
Yu H, Zhao Y, Mo L. Fuzzy reliability assessment of safety instrumented systems accounting for common cause failure. IEEE Access 2020; 8: 135371-135382, https://doi.org/10.1109/ACCESS....
30.
Rezaei J. Best-worst multi-criteria decision-making method. Omega 2015; 53: 49-57, https://doi.org/10.1016/j.omeg....
31.
Xia M, Xu Z. Hesitant fuzzy information aggregation in decision making. International Journal of Approximate Reasoning 2011; 52(3): 395-407, https://doi.org/10.1016/j.ijar....
32.
Nakahara Y, Sasaki M, Gen M. On the linear programming problems with interval coefficients. Computers & Industrial Engineering 1992; 23(1-4): 301-304, https://doi.org/10.1016/0360-8....
33.
Wang Y, Yang J, Xu D. A two-stage logarithmic goal programming method for generating weights from interval comparison matrices. Fuzzy Sets and Systems 2005; 152(3): 475-498, https://doi.org/10.1016/j.fss.....
34.
Rezaei J. Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega 2016; 64: 126-130, https://doi.org/10.1016/j.omeg....
35.
Moore R E. Methods and applications of interval analysis. Society for Industrial and Applied Mathematics 1979. https://doi.org/10.1137/1.9781....
36.
Guo J, Qi J, Li X. Reliability Analysis of EMUs Braking Systems with Fuzzy Dynamic Fault Tree. China Mechanical Engineering 2019; 30(13):6, https://doi.org/10.3969/j.issn....
CITATIONS (1):
1.
Time-Varying Reliability Assessment of Urban Traffic Network Based on Dynamic Bayesian Network
Sihui Dong, Ni Jia, Shiqun Li, Yazhuo Zou
Sustainability
Sihui Dong, Ni Jia, Shiqun Li, Yazhuo Zou
Sustainability
| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
