Bayesian Approach for the Lethargy Coefficient Estimation in the Probabilistic Creep-Fatigue Life Model
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
Prognostics and Health Management (PHM), Creep-fatigue life, Lethargy coefficient, Zhurkov model, Markov Chain Monte Carlo (MCMC), Bayesian framework
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