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http://dx.doi.org/10.3795/KSME-A.2011.35.4.393

Inverse Estimation of Fatigue Life Parameters of Springs Based on the Bayesian Approach  

Heo, Chan-Young (Dept. of Aerospace & Mechanical Engineering, Korea Aerospace Univ.)
An, Da-Wn (Dept. of Aerospace & Mechanical Engineering, Korea Aerospace Univ.)
Won, Jun-Ho (Dept. of Aerospace & Mechanical Engineering, Korea Aerospace Univ.)
Choi, Joo-Ho (Dept. of Aerospace & Mechanical Engineering, Korea Aerospace Univ.)
Publication Information
Transactions of the Korean Society of Mechanical Engineers A / v.35, no.4, 2011 , pp. 393-400 More about this Journal
Abstract
In this study, a procedure for the inverse estimation of the fatigue life parameters of springs which utilize the field fatigue life test data is proposed to replace real test with the FEA on fatigue life prediction. The Bayesian approach is employed, in which the posterior distributions of the parameters are determined conditional on the accumulated life data that are routinely obtained from the regular tests. In order to obtain the accurate samples from the distributions, the Markov chain Monte Carlo (MCMC) technique is employed. The distributions of the parameters are used in the FEA for predicting the fatigue life in the form of a predictive interval. The results show that the actual fatigue life data are found well within the posterior predictive distributions.
Keywords
Fatigue Life Parameters; Inverse Estimation; Bayesian Approach; Markov Chain Monte Carlo;
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Times Cited By KSCI : 4  (Citation Analysis)
Times Cited By SCOPUS : 0
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1 Gunawan, S. and Papalambros, P. Y., 2006, “ABayesian Approach to Reliability-Based OptimizationWith Incomplete Information,” Journal of MechanicalDesign, Vol. 128, No. 4, pp. 909-918.   DOI   ScienceOn
2 Lee, S., B., Park, T., W. and Yim, H., J., 2000, “AStudy on Computational Method for Fatigue LifePrediction of Vehicle Structure,” Journal of KSNVE,Vol. 10, No. 4, pp. 686-691.
3 Rice, R. C., 1997, SAE Fatigue Design Handbook,3rd ed., SAE, Warrendale.
4 Gelman, A., Carlim, J. B., Strern, H. S. and Rubin,D. B., 2004, Bayesian Data Analysis, CHAPMAN &HALL/CRC, Inc., New York.
5 Yim, H., J. and Lee, S., B., 1996, "An IntegratedCAE System for Dynamic Stress and Fatigue LifePrediction of Mechanical Systems," KSMEInternational Journal, Vol. 10, No. 2, pp. 158-168.   DOI
6 Budynas & Nisbett. 2007, Shigley's MechanicalEngineering Design, McGraw-Hill, New York.
7 An, D. W., Won, J. H., Kim, E. J. and Choi, J. H.,2009, “Reliability Analysis Under Input Variable andMetamodel Uncertainty Using Simulation MethodBased on Bayesian Approach,” Trans. of the KSME(A),Vol. 33, No. 10, pp. 1163-1170.   과학기술학회마을   DOI   ScienceOn
8 Cruse, T.A. and Brown, J.M., 2007, "ConfidenceInterval Simulation for Systems of RandomVariables," Journal of Engineering for Gas Turbinesand Power ASME, Vol. 129, pp.836-842.   DOI
9 Andrieu, C., de Freitas, N., Doucet, A. and Jordan,M., 2003, "An Introduction to MCMC for MachineLearning," Machine Learning, Vol. 50, No. 1-2, pp.5-43.   DOI
10 Lin, J. and Pan, J., 1998, “A New Method forSelection of Population Distribution and ParameterEstimation,” Reliability Engineering & System Safety,Vol. 60, No. 3, pp. 247-255.   DOI   ScienceOn
11 Kim, D. S. and Kim, J. K., 1994, “The Prediction ofFatigue Life According to the Determination of theParameter in Residual Strength Degradation Model,”Trans. of the KSME, Vol. 18, No. 8, pp. 2053-2061.   과학기술학회마을
12 Hu, Q. and Xu, H., 1995, “Two-parameters nominalstress approach,” International journal of fatigue, Vol.17, No. 5, pp. 339-341.   DOI   ScienceOn
13 Yoon, H. Y. and Zhang, J., 2008, “Evaluation forProbabilistic Distributions of Fatigue Life of MarinePropeller Materials by using a Monte CarloSimulation,” Trans. of the KSME(A), Vol. 32, No. 12,pp. 1055-1062.   과학기술학회마을   DOI   ScienceOn
14 Song, Y. C., Yoh, E. G. and Lee, Y. S., 1999, “AStudy on the Prediction of Fatigue Life in Die,”Transactions of the Korean Society of Machine ToolEngineers, Vol. 8, No. 4, pp. 87-92.   과학기술학회마을