Entropy-based probabilistic fatigue damage prognosis and algorithmic performance comparison

Xuefei Guan, Yongming Liu, Abhinav Saxena, Jose Celaya, Kai Goebel

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

13 Scopus citations

Abstract

In this paper, a maximum entropy-based general framework for probabilistic fatigue damage prognosis is investigated. The proposed methodology is based on an underlying physics-based crack growth model. Various uncertainties from measurements, modeling, and parameter estimations are considered to describe the stochastic process of fatigue damage accumulation. A probabilistic prognosis updating procedure based on the maximum relative entropy concept is proposed to incorporate measurement data. Markov Chain Monte Carlo (MCMC) technique is used to provide the posterior samples for model updating in the maximum entropy approach. Experimental data are used to demonstrate the operation of the proposed probabilistic prognosis methodology. A set of prognostics-based metrics are employed to quantitatively evaluate the prognosis performance and compare the proposed method with the classical Bayesian updating algorithm. In particular, model accuracy, precision and convergence are rigorously evaluated in addition to the qualitative visual comparison.

Original languageEnglish (US)
Title of host publicationAnnual Conference of the Prognostics and Health Management Society, PHM 2009
PublisherPrognostics and Health Management Society
ISBN (Electronic)9781936263004
StatePublished - Jan 1 2009
Externally publishedYes
EventAnnual Conference of the Prognostics and Health Management Society, PHM 2009 - San Diego, United States
Duration: Sep 27 2009Oct 1 2009

Publication series

NameAnnual Conference of the Prognostics and Health Management Society, PHM 2009

Other

OtherAnnual Conference of the Prognostics and Health Management Society, PHM 2009
CountryUnited States
CitySan Diego
Period9/27/0910/1/09

ASJC Scopus subject areas

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

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