Probabilistic fatigue damage prognosis using maximum entropy approach

Xuefei Guan, Ratneshwar Jha, Yongming Liu

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

A general framework for probabilistic fatigue damage prognosis using maximum entropy concept is proposed and developed in this paper. The fatigue damage is calculated using a physics-based crack growth model. Due to the stochastic nature of fatigue crack propagation process, uncertainties arising from the underlying physical model, parameters of the model and the response variable measurement noise are considered and integrated into this framework. Incorporating all those uncertainties, a maximum relative entropy (MRE) approach is proposed to update the statistical description of model parameters and narrow down the prognosis deviations. A Markov Chain Monte Carlo (MCMC) simulation is then employed to generate samples from updated posterior probability distributions and provide statistical information for the maximum relative entropy updating procedure. A numerical toy problem is given to demonstrate the proposed MRE prognosis methodology. Experimental data for aluminum alloys are used to validate model predictions under uncertainty. Following this, a detailed comparison between the proposed MRE approach and the classical Bayesian updating method is performed to illustrate advantages of the proposed prognosis framework.

Original languageEnglish (US)
Pages (from-to)163-171
Number of pages9
JournalJournal of Intelligent Manufacturing
Volume23
Issue number2
DOIs
StatePublished - Apr 2012

Keywords

  • Bayesian method
  • Fatigue crack growth
  • Markov Chain Monte Carlo
  • Maximum relative entropy
  • Model updating
  • Uncertainty

ASJC Scopus subject areas

  • Software
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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