Component uncertainty importance measure in complex multi-state system considering epistemic uncertainties

被引：24
作者：
Chen, Rentong [1 ,2 ]
Wang, Shaoping [1 ,4 ]
Zhang, Chao [1 ,3 ,4 ]
Dui, Hongyan [5 ]
Zhang, Yuwei [1 ]
Zhang, Yadong [1 ,2 ]
Li, Yang [3 ,4 ]
机构：
[1] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China
[2] Politecn Milan, Dept Energy, I-20156 Milan, Italy
[3] Beihang Univ, Res Inst Frontier Sci, Beijing 100191, Peoples R China
[4] Beihang Univ, Ningbo Inst Technol, Ningbo 315800, Peoples R China
[5] Zhengzhou Univ, Sch Management, Zhengzhou 450001, Peoples R China
来源：
CHINESE JOURNAL OF AERONAUTICS | 2024年 / 37卷 / 12期
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
Importance measure; Epistemic uncertainty; Multi-state system; Evidence theory; Markov hierarchal evidential network; COMPOSITE IMPORTANCE MEASURES; RELIABILITY-ANALYSIS; SAMPLE;
D O I：
10.1016/j.cja.2024.05.024
中图分类号：
V [航空、航天];
学科分类号：
08 ; 0825 ;
摘要：
Importance measures can be used to identify the vulnerable components in an aviation system at the early design stage. However, due to lack of knowledge or less available information on the component or system, the epistemic uncertainties may be one of the challenging issues in importance evaluation. In addition, the properties of the aircraft system, which are the fundamentals of the component importance measure, including the hierarchy, dependency, randomness, and uncertainty, should be taken into consideration. To solve these problems, this paper proposes the component Uncertainty Integrated Importance Measure (component UIIM) which considers multiple epistemic uncertainties in the complex multi-state systems. The degradation process for the components is described by a Markov model, and the system reliability model is developed using the Markov hierarchal evidential network. The concept of integrated importance measure is then extended into component UIIM to evaluate the component criticality rather than the component state change criticality, from the perspective of system performance. A case study on displacement compensation hydraulic system is presented to show the effectiveness of the proposed uncertainty importance measure. The results show that the component UIIM can be an effective method for evaluating the component criticality from system performance perspective at the system early design. (c) 2024 Production and hosting by Elsevier Ltd. on behalf of Chinese Society of Aeronautics and Astronautics. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
引用
收藏
页码：31 / 54
页数：24
相关论文
共 66 条
  • [1] Si S.B., Zhao J.B., Cai Z.Q., Et al., Recent advances in system reliability optimization driven by importance measures, Front Eng Manag, 7, 3, pp. 335-358, (2020)
  • [2] Ren B., Lu Z.Z., Zhou C.C., A novel method for importance measure analysis in the presence of epistemic and aleatory uncertainties, Chin J Aeronaut, 27, 3, pp. 568-576, (2014)
  • [3] Chen R.T., Zhang C., Wang S.P., Et al., Importance measures for critical components in complex system based on Copula Hierarchical Bayesian Network, Reliab Eng Syst Saf, 230, (2023)
  • [4] Guo J., Li Z.J., Keyser T., A Bayesian approach for integrating multilevel priors and data for aerospace system reliability assessment, Chin J Aeronaut, 31, 1, pp. 41-53, (2018)
  • [5] Wang S.P., Cui X.Y., Shi J., Et al., Modeling of reliability and performance assessment of a dissimilar redundancy actuation system with failure monitoring, Chin J Aeronaut, 29, 3, pp. 799-813, (2016)
  • [6] Levitin G., A universal generating function approach for the analysis of multi-state systems with dependent elements, Reliab Eng Syst Saf, 84, 3, pp. 285-292, (2004)
  • [7] Weber P., Medina-Oliva G., Simon C., Et al., Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas, Eng Appl Artif Intell, 25, 4, pp. 671-682, (2012)
  • [8] Zhou X., Sbarufatti C., Giglio M., Et al., Copula-based collaborative multistructure damage diagnosis and prognosis for fleet maintenance digital twins, AIAA J, 61, 10, pp. 4735-4740, (2023)
  • [9] Tang D.Y., Cao J.R., Yu J.S., Remaining useful life prediction for engineering systems under dynamic operational conditions: a semi-Markov decision process-based approach, Chin J Aeronaut, 32, 3, pp. 627-638, (2019)
  • [10] Sallak M., Schon W., Aguirre F., Reliability assessment for multi-state systems under uncertainties based on the Dempster-Shafer theory, IIE Trans, 45, 9, pp. 995-1007, (2013)
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