A Hybrid Uncertainty Propagation Method for System Risk Assessment

This paper proposes a hybrid uncertainty propagation approach for system risk assessment. In view of hybrid uncertainty propagation with parameters dependency of variables in risk assessment, a two level hybrid uncertainty propagation framework was proposed, in which inner and outer parameters are characterized with probability and possibility respectively, the numerical values are calculated by Monte Carlo (MC) simulation and fuzzy extension principle. In consideration of the dependency of epidemic uncertainty parameters, an epidemic uncertainty parameter dependency model is designed and a dependency coefficient is proposed. Then the proposed method was compared with the conventional two level MC. Finally, taking the safety of one hydrogen and oxygen co-bottom tank an example, the effectiveness and feasibility of the proposed method was validated.

[1]  Guo Shu,et al.  A non-probabilistic model of structural reliability based on interval analysis , 2001 .

[2]  M. Hanss,et al.  Review: Non-probabilistic finite element analysis for parametric uncertainty treatment in applied mechanics: Recent advances , 2011 .

[3]  E. Zio,et al.  A Combined Monte Carlo and Possibilistic Approach to Uncertainty Propagation in Event Tree Analysis , 2008, Risk analysis : an official publication of the Society for Risk Analysis.

[4]  Z. Qiu,et al.  Comparison between non‐probabilistic interval analysis method and probabilistic approach in static response problem of structures with uncertain‐but‐bounded parameters , 2004 .

[5]  Enrico Zio,et al.  Empirical Comparison of Methods for the Hierarchical Propagation of Hybrid Uncertainty in Risk Assessment, in Presence of Dependences , 2012, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[6]  H. Carter.  Fuzzy Sets and Systems — Theory and Applications , 1982 .

[7]  J. C. Helton,et al.  Uncertainty and sensitivity analysis in the presence of stochastic and subjective uncertainty , 1997 .

[8]  Serhat Hosder,et al.  A Mixed Uncertainty Quantification Approach with Evidence Theory and Stochastic Expansions , 2014 .

[9]  Hung T. Nguyen,et al.  Possibility Theory, Probability and Fuzzy Sets Misunderstandings, Bridges and Gaps , 2000 .

[10]  Didier Dubois,et al.  Hybrid approach for addressing uncertainty in risk assessments , 2003 .

[11]  John E. Renaud,et al.  Uncertainty quantification using evidence theory in multidisciplinary design optimization , 2004, Reliab. Eng. Syst. Saf..

[12]  Jon C. Helton,et al.  Representation of analysis results involving aleatory and epistemic uncertainty , 2010, Int. J. Gen. Syst..

[13]  Timothy J. Ross,et al.  Fuzzy Logic with Engineering Applications: Ross/Fuzzy Logic with Engineering Applications , 2010 .

[14]  Cédric Baudrit,et al.  The use of the possibility theory to investigate the epistemic uncertainties within scenario-based earthquake risk assessments , 2011 .

[15]  Enrico Zio,et al.  Handling epistemic uncertainties in fault tree analysis by probabilistic and possibilistic approaches , 2009 .

[16]  Didier Dubois,et al.  Joint Propagation and Exploitation of Probabilistic and Possibilistic Information in Risk Assessment , 2006, IEEE Transactions on Fuzzy Systems.

[17]  Didier Dubois,et al.  Possibility theory and statistical reasoning , 2006, Comput. Stat. Data Anal..