Bayesian model selection and parameter estimation for fatigue damage progression models in composites

Abstract

a b s t r a c tA Bayesian approach is presented for selecting the most probable model class among a set of damage mechanics models for fatigue damage progression in composites. Candidate models, that are first parameterized through a Global Sensitivity Analysis, are ranked based on estimated probabilities that measure the extent of agreement of their predictions with observed data. A case study is presented using multiscale fatigue damage data from a cross-ply carbon-epoxy laminate. The results show that, for this case, the most probable model class among the competing candidates is the one that involves the simplest damage mechanics. The principle of Ockham's razor seems to hold true for the composite materials investigated here since the data-fit of more complex models is penalized, as they extract more information from the data.

Residual life prediction for highly reliable products with prior accelerated degradation data

“…tions that were evaluated by the Bayesian method with conjugate prior distributions of random parameters, 3 RL denote the predictions that were evaluated by the method proposed in this paper, the residual life predictions and the confidence intervals of the predictions at several measuring time points were summarized in table 8. The confidence intervals were obtained with 95% confidence level by Bootstrap sampling method.…”

“…For an individual with high reliability, the degradation data observed under normal stress levels cannot show a distinct degradation trend, therefore it is difficult to precisely predict the residual life. In order to precisely predict the residual life for an individual with limited real-time degradation data, the prediction methods based on Bayesian inference have been popularly studied [3,7,8]. Gebraeel et al [5] developed an exponential degradation model with random parameters to model bearing degradation signals.…”

Predicting Fatigue Damage of Composites Using Strength Degradation and Cumulative Damage Model

“…These models can be classified in the following categories [1]: fatigue life concepts [2,3], phenomenological models with stiffness [4,5], strength degradation models [6,7], continuum damage mechanics (CDM) based models [8][9][10] and micromechanics models [11,12] and uncertainty and Bayesian based probabilistic models [13][14][15].…”

“…One of the new category of approaches to model fatigue in composites are models based on uncertainty and Bayesian based probabilistic framework [13][14][15]. Most of the probabilistic models are based on quantifying model uncertainties for different fatigue models.…”

“…They used a cumulative damage model based on the theory of Markov chains to infer a complete damage progression. Chiachío et al [14] presented one such model based on the uncertainty for empirical fatigue model. The authors did not account for any interlaminar failure for the Bayesian approach.…”