TY - GEN
T1 - A probabilistic multi-model Bayesian network for fatigue damage prognosis
AU - Guan, Xuefei
AU - Jha, Ratneshwar
AU - Liu, Yongming
N1 - Funding Information:
The research reported in this paper was supported by the NASA NNX09AY54A. The support is gratefully acknowledged.
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
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U2 - 10.2514/6.2011-1702
DO - 10.2514/6.2011-1702
M3 - Conference contribution
AN - SCOPUS:84872470883
SN - 9781600869518
T3 - Collection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
BT - 52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
T2 - 52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
Y2 - 4 April 2011 through 7 April 2011
ER -