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

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

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Component uncertainty importance measure in complex multi-state system considering epistemic uncertainties
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 impor-tance evaluation.In addition,the properties of the aircraft system,which are the fundamentals of the component importance measure,including the hierarchy,dependency,randomness,and uncer-tainty,should be taken into consideration.To solve these problems,this paper proposes the com-ponent Uncertainty Integrated Importance Measure(component UIIM)which considers multiple epistemic uncertainties in the complex multi-state systems.The degradation process for the compo-nents is described by a Markov model,and the system reliability model is developed using the Mar-kov 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.

Importance measureEpistemic uncertaintyMulti-state systemEvidence theoryMarkov hierarchal evidential network

Rentong CHEN、Shaoping WANG、Chao ZHANG、Hongyan DUI、Yuwei ZHANG、Yadong ZHANG、Yang LI

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School of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,China

Department of Energy,Politecnico di Milano,Milano 20156,Italy

Ningbo Institute of Technology,Beihang University,Ningbo 315800,China

Research Institute of Frontier Science,Beihang University,Beijing 100191,China

School of Management,Zhengzhou University,Zhengzhou 450001,China

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Importance measure Epistemic uncertainty Multi-state system Evidence theory Markov hierarchal evidential network

2024

中国航空学报(英文版)
中国航空学会

中国航空学报(英文版)

CSTPCDEI
影响因子:0.847
ISSN:1000-9361
年,卷(期):2024.37(12)