The Lethargy Coefficient Estimation of the Probabilistic Fatigue Life Model Using the Markov Chain Monte Carlo
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Recently, the researchers of prognostics and health management (PHM) have been developed to the field of engineering. In this study, probabilistic fatigue life which based on Zhurkov model is suggested using stochastically and statistically estimated lethargy coefficient. The fatigue life model was derived using Zhurkov life model and it was deterministically validated with the reference of fatigue life data. For this process, firstly, lethargy coefficient which is relative to the failure of materials has to be obtained with rupture time and stress from quasi-static tensile test. These experiments are performed using HS40R steel. However, lethargy coefficient has uncertainties due to inherent uncertainty and the variation of material properties in the experiments. Bayesian approach was employed for estimating the lethargy coefficient of the fatigue life model using Markov Chain Monte Carlo (MCMC) sampling method and considering its uncertainties. Once the samples are obtained, one can proceed to the posterior predictive inference on the fatigue life. This life model is reasonable through comparing with experimental fatigue life data. As a result, predicted fatigue life was observed that it was significantly decreased in accordance with increasing stress conditions relatively. This life model is reasonable through comparing with experimental fatigue life data.
##plugins.themes.bootstrap3.article.details##
Prognostics and health management (PHM), Fatigue life, Lethargy coefficient, Zhurkov model, Markov Chain Monte Carlo (MCMC), Bayesian approach
J.-H. Choi, D. An, J. Gang, J. Joo & N. H. Kim (2011). Bayesian Approach for Parameter Estimation in the Structural Analysis and Prognosis. Annual Conference of the Prognostics and Health Management Society. September 25-29 Montreal, Quebec, Canada.
F. Ibisoglu & M. Modarres (2015). Probabilistic Life Models for Steel Structures Subject to Creep-Fatigue Damege. International Journal of Prognostics and Health Managements, vol. 6, pp. 1-12.
S. N. Zhurkov, (1965). Kinetic Concept of The Strength of Solid. International Journal of Fracture Mechanics, vol.1, no. 4, pp. 311-322
S. R. Sin, S. M. Yang, H. S. Yu, C. W. Kim & H. Y. Kang (2007). Fatigue Analysis of Multi-Lap Spot Welding of High Strength Steel by Quasi Static Tensile-Shear Test. Engineering Material, vol. 345-346, pp. 251-254.
J. E. Park, S. M. Yang, J. H. Han & H. S. Yu (2011). Creep-Fatigue Design with Various Stress and Temperature Conditions on the Basis of Lethargy Coefficient. Korean Society of Mechanical Engineers, vol.3, pp. 157-162. Korean Industrial Standard, Seoul. Republic Of Korea.
S. M. Yang, H. Y. Kang, J. H. Song, S. J. Kwon, H. S. Kim, (1997). Failure life prediction by simple tensile test under dynamic load. International Conference on Fracture 9. November Sydney, Australia.
J. H. Song, H. G. Noh, H. S. Yu, H. Y. Kang, & S. M. Yang (2004). Estimation of fatigue Life by lethargy coefficient using molecular dynamic simulation.
International journal of automotive technology, vol.5(3), pp.215-219
S. H. Leem, D. An, S. Ko, & J.-H. Choi (2011). A Study on the parameter estimation for crack growth prediction under variable amplitude loading. Annual Conference of the Prognostics and Health Management Society. September 25-29 Montreal, Quebec, Canada.