Bayesian Approach for the Lethargy Coefficient Estimation in the Probabilistic Creep-Fatigue Life Model

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Published Oct 3, 2016
Jaehyeok Doh Junhwan Byun Jongsoo Lee

Abstract

The researches of Prognostics and Health Management (PHM) have been important in the field of engineering. The crack is propagated by high temperature and stress in power plants, vehicle engines and etc. The defect and damage are also accumulated. Therefore, it is necessary for design of creep-fatigue life about various structures and etc. In this study, probabilistic life design based on Zhurkov life model was performed using the lethargy coefficient under the variety of temperatures and stress conditions. For this work, the integration life equation was derived using Zhurkov life model. The deterministic lethargy coefficient is calculated to using the reference of the Small Punch (SP)-Creep test and tensile-shear test data about steel material (rupture stress and rupture time). Markov Chain Monte Carlo (MCMC) sampling method based on Bayesian framework is employed for estimating the lethargy coefficient and considering its uncertainties. As a result, predicted creep-fatigue life was observed that it was considerably decreased in accordance with increasing temperature and stress conditions relatively. This life model is reasonable through comparing with conventional creep-fatigue life data.

How to Cite

Doh, J., Byun, J., & Lee, J. (2016). Bayesian Approach for the Lethargy Coefficient Estimation in the Probabilistic Creep-Fatigue Life Model. Annual Conference of the PHM Society, 8(1). https://doi.org/10.36001/phmconf.2016.v8i1.2578
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Keywords

Prognostics and Health Management (PHM), Creep-fatigue life, Lethargy coefficient, Zhurkov model, Markov Chain Monte Carlo (MCMC), Bayesian framework

References
C. Andrieu, N. D. Freitas, A. Doucet & M. Jordan (2003). An introduction to MCMC for Machine Learning, Machine Learning. vol. 50, pp. 5-43.
X. Guan, R. Jha, & Y. Liu (2010). Trans-dimensional MCMC for fatigue prognosis model determination updating and averaging. Annual Conference of the Prognostics and Health Management Society. October 10-16, Portland, Oregon.
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.
X. Guan, Y. Liu, R. Jha, A. Saxena, J. Celaya, & K. Geobel (2011). Comparison of Two Probabilistic Fatigue Damage Assessment Approaches Using Prognostic Performance Metrics. International Journal of Prognostics and Health Management, vol.2, pp.1-11.
S. Sankararaman, Y. Ling, C. Shantz & S, Mahadevan (2011). Uncertainty Quantification in Fatigue Crack Growth Prognosis. International Journal of Prognostics and Health Management, vol.2, pp. 1-15.
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. 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. 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.
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. INSTRON 8516, Instron Corporation, Norwood, MA.
KS B 0850, (2011). Korean Standards Information Center, Korean Industrial Standard, Seoul. Republic Of Korea.
J. M. Park, J. H. Song, H. Y. Kang, S. M. Yang (1998). Prediction of life of SAPH45 steel with measured fracture time and strength. Korean Society of Manufacturing Technology Engineers, pp. 269~273
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.
Section
Technical Research Papers