Sensitivity analysis with mixture of epistemic and aleatory uncertainties

作     者：Guo, Jia Du, Xiaoping 

作者机构：Univ Missouri Dept Mech & Aerosp Engn Rolla MO 65409 USA 

出 版 物：《AIAA JOURNAL》 (美国航空航天学会志)

年 卷 期：2007年第45卷第9期

页      面：2337-2349页

核心收录：

学科分类：08[工学] 0825[工学-航空宇航科学与技术] 

基　　金：U.S. National Science Foundation, (CMMI-040081) University of Missouri, MU 

主　　题：RISK assessment ACCELERATED life testing DECISION making STRUCTURAL optimization SENSITIVITY theory (Mathematics) KOLMOGOROV complexity 

摘      要：The study on epistemic uncertainty due to the lack of knowledge has received increasing attention in risk assessment, reliability analysis, decision making, and design optimization. Different theories have been applied to model and quantify epistemic uncertainty. Research on sensitivity analysis for epistemic uncertainty has also been initialized. Sensitivity analysis can identify the contributions of individual input variables with epistemic uncertainty to the model output. It then helps guide the collection of more information to reduce the effect of epistemic uncertainty. In this paper, an effective sensitivity analysis method for epistemic uncertainty is proposed when both epistemic and aleatory uncertainties exist in model inputs. This method employs the unified uncertainty analysis framework to calculate the plausibility and belief measures. The gap between belief and plausibility measures is used as an indicator of the effect of epistemic uncertainty on the model output. The Kolmogorov-Smirnov distance between the two measures is used to quantify the main effect and the total effect of each independent variable with epistemic uncertainty. By the Kolmogorov-Smirnov distance, the importance of each variable is ranked. The feasibility and effectiveness of the proposed method is demonstrated with two engineering examples.