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논문 상세정보

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


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.

참고문헌 (8)

  1. Haldar, A. and Mahadevan, S., 2000, Probability, Reliability, and Statistical Methods in Engineering Design, John Wiley & Sons, Inc., New York 
  2. Rahman, S. and Xu, H., 2004, 'A Univariate Dimension-Reduction Method for Multi-Dimensional Integration in Stochastic Mechanics,' Probabilistic Engineering Mechanics, Vol. 19, pp. 393-408 
  3. Won, J.H., Choi, C.H. and Choi, J.H., 2009, 'Improving the Dimension Reduction Method (DRM) in the Uncertainty Analysis and Application to the Reliability Based Design Optimization,' Journal of Mechanical Science and Technology, Vol. 23, no. 5, pp. 1249-1260 
  4. Gunawan, S. and Papalambros, P.Y., 2006, 'A Bayesian Approach to Reliability-Based Optimization With Incomplete Information,' ASME, Vol. 128, pp. 909-918 
  5. Cruse, T.A. and Brown, J.M., 2007, 'Confidence Interval Simulation for Systems of Random Variables,' Journal of Engineering for Gas Turbines and Power ASME, Vol. 129, pp.836-842 
  6. Eduard, H., Martina, K., Bernard, K.H., Jörg, P. and Martin, W., 2002, 'An Approximate Epistemic Uncertainty Analysis Approach in the Presence of Epistemic and Aleatory Uncertainties,' Reliability Engineering and System Safety, Vol. 77, pp. 229-238 
  7. Gelman, A., Carlim, J.B., Strern, H.S. and Rubin, D.B., 2003, Bayesian Data Analysis, CHAPMAN & HALL/CRC, Inc., New York 
  8. O'Hagan, A., 2006, 'Bayesian Analysis of Computer Code Outputs: A Tutorial,' Reliability Engineering and System Safety, Vol. 91, pp.1290-1300 

이 논문을 인용한 문헌 (1)

  1. Heo, Chan-Young ; An, Da-Wn ; Won, Jun-Ho ; Choi, Joo-Ho 2011. "Inverse Estimation of Fatigue Life Parameters of Springs Based on the Bayesian Approach" 大韓機械學會論文集. Transactions of the Korean Society of Mechanical Engineers. A. A, 35(4): 393~400 


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