Trans-dimensional MCMC for fatigue prognosis model determination, updating, and averaging

Xuefei Guan, Ratneshwar Jha, Yongming Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

In this paper, a general Bayesian framework for fatigue model determination, updating and averaging using trans-dimensional Markov Chain Monte Carlo (MCMC) simulations is presented. Uncertainties introduced by model choice, mechanism modeling, model parameter, and response measures are systematically included. Additional response measures are used to update the model probabilities and the parameter distributions associated with each of the models simultaneously via one trans-dimensional MCMC simulation in the general state space. The averaging of model predictions can readily be performed using the simulation samples. The results of Bayes factors serve as a reference for model comparisons and determinations. To improve the simulation efficiency, we incorporate a new algorithm to construct the dimension matching densities and bijection functions. A fatigue crack growth example with experimental data is presented for methodology demonstration and validation.

Original languageEnglish (US)
Title of host publicationAnnual Conference of the Prognostics and Health Management Society, PHM 2010
PublisherPrognostics and Health Management Society
ISBN (Electronic)9781936263011
StatePublished - 2010
Externally publishedYes
EventAnnual Conference of the Prognostics and Health Management Society, PHM 2010 - Portland, United States
Duration: Oct 13 2010Oct 16 2010

Publication series

NameAnnual Conference of the Prognostics and Health Management Society, PHM 2010

Other

OtherAnnual Conference of the Prognostics and Health Management Society, PHM 2010
Country/TerritoryUnited States
CityPortland
Period10/13/1010/16/10

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Health Information Management
  • Computer Science Applications
  • Software

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