Parametric probabilistic approach for cumulative fatigue damage using double linear damage rule considering limited data

João Paulo Dias, Stephen Ekwaro-Osire, Americo Cunha, Shweta Dabetwar, Abraham Nispel, Fisseha M. Alemayehu, Haileyesus B. Endeshaw

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

This work proposes a parametric probabilistic approach to model damage accumulation using the double linear damage rule (DLDR) considering the existence of limited experimental fatigue data. A probabilistic version of DLDR is developed in which the joint distribution of the knee-point coordinates is obtained as a function of the joint distribution of the DLDR model input parameters. Considering information extracted from experiments containing a limited number of data points, an uncertainty quantification framework based on the Maximum Entropy Principle and Monte Carlo simulations is proposed to determine the distribution of fatigue life. The proposed approach is validated using fatigue life experiments available in the literature.

Original languageEnglish
Pages (from-to)246-258
Number of pages13
JournalInternational Journal of Fatigue
Volume127
DOIs
StatePublished - Oct 2019

Keywords

  • Cumulative fatigue damage
  • Double linear damage rule
  • Limited data experiments
  • Maximum Entropy Principle
  • Uncertainty quantification

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