A probabilistic multi-model Bayesian network for fatigue damage prognosis

Xuefei Guan, Ratneshwar Jha, Yongming Liu

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

Abstract

A general multi-model Bayesian network for fatigue damage prognosis is proposed in this paper. Uncertainties introduced by model choice, mechanism modeling, model parameter, and response measures are systematically included and hierarchically managed using a two-level Bayesian network. Additional relevant information is used to update the network state using the trans-dimensional Markov chain Monte Carlo (MCMC) simulations in the general multi-model state space. To improve the simulation efficiency, a new algorithm is developed to construct the proposal distributions in the trans-dimensional MCMC simulation. The model probabilities, parameter densities, Bayes factors, and the prognosis averaging are readily calculated based on the simulation results. A fatigue damage prognosis example incorporating three fatigue crack models is presented for methodology demonstration. Experimental data are used to validate the effectiveness of the method.

Original languageEnglish (US)
Title of host publication52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
DOIs
StatePublished - Dec 1 2011
Externally publishedYes
Event52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference - Denver, CO, United States
Duration: Apr 4 2011Apr 7 2011

Publication series

NameCollection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
ISSN (Print)0273-4508

Other

Other52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
CountryUnited States
CityDenver, CO
Period4/4/114/7/11

ASJC Scopus subject areas

  • Architecture
  • Materials Science(all)
  • Aerospace Engineering
  • Mechanics of Materials
  • Mechanical Engineering

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  • Cite this

    Guan, X., Jha, R., & Liu, Y. (2011). A probabilistic multi-model Bayesian network for fatigue damage prognosis. In 52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference [AIAA 2011-1702] (Collection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference). https://doi.org/10.2514/6.2011-1702