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Reliability Analysis Under Input Variable and Metamodel Uncertainty Using Simulation Method Based on Bayesian Approach

베이지안 접근법을 이용한 입력변수 및 근사모델 불확실성 하에 서의 신뢰성 분석

  • 안다운 (한국항공대학교, 항공우주 및 기계공학과) ;
  • 원준호 (한국항공대학교, 항공우주 및 기계공학과) ;
  • 김은정 (한국항공대학교, 항공우주 및 기계공학과) ;
  • 최주호 (한국항공대학교, 항공우주 및 기계공학부)
  • Published : 2009.10.01

Abstract

Reliability analysis is of great importance in the advanced product design, which is to evaluate reliability due to the associated uncertainties. There are three types of uncertainties: the first is the aleatory uncertainty which is related with inherent physical randomness that is completely described by a suitable probability model. The second is the epistemic uncertainty, which results from the lack of knowledge due to the insufficient data. These two uncertainties are encountered in the input variables such as dimensional tolerances, material properties and loading conditions. The third is the metamodel uncertainty which arises from the approximation of the response function. In this study, an integrated method for the reliability analysis is proposed that can address all these uncertainties in a single Bayesian framework. Markov Chain Monte Carlo (MCMC) method is employed to facilitate the simulation of the posterior distribution. Mathematical and engineering examples are used to demonstrate the proposed method.

Keywords

Reliability Analysis;Input Variable Uncertainty;Metamodel Uncertainty;Bayesian Approach;Markov Chain Monte Carlo;Reliability Based Design Optimization

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  1. Inverse Estimation of Fatigue Life Parameters of Springs Based on the Bayesian Approach vol.35, pp.4, 2011, https://doi.org/10.3795/KSME-A.2011.35.4.393