Bayesian fatigue damage and reliability analysis using Laplace approximation and inverse reliability method

Xuefei Guan, Jingjing He, Ratneshwar Jha, Yongming Liu

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

Abstract

This paper presents an efficient analytical Bayesian method for reliability and system response estimate and update. The method includes additional data such as measurements to reduce estimation uncertainties. Laplace approximation is proposed to evaluate Bayesian posterior distributions analytically. An efficient algorithm based on inverse first-order reliability method is developed to evaluate system responses given a reliability level. Since the proposed method involves no simulations such as Monte Carlo or Markov chain Monte Carlo simulations, the overall computational efficiency improves significantly, particularly for problems with complicated performance functions. A numerical example and a practical fatigue crack propagation problem with experimental data are presented for methodology demonstration. The accuracy and computational efficiency of the proposed method is compared with simulation-based methods.

Original languageEnglish (US)
Title of host publicationProceedings of the Annual Conference of the Prognostics and Health Management Society 2011, PHM 2011
EditorsAbhinav Saxena, Sankalita Saha, Jose R. Celaya
PublisherPrognostics and Health Management Society
Pages94-103
Number of pages10
ISBN (Electronic)9781936263035
StatePublished - 2014
Externally publishedYes
Event2011 Annual Conference of the Prognostics and Health Management Society, PHM 2011 - Montreal, Canada
Duration: Sep 25 2011Sep 29 2011

Publication series

NameProceedings of the Annual Conference of the Prognostics and Health Management Society 2011, PHM 2011

Other

Other2011 Annual Conference of the Prognostics and Health Management Society, PHM 2011
Country/TerritoryCanada
CityMontreal
Period9/25/119/29/11

ASJC Scopus subject areas

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

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