A Bayesian Inference Method and its Application in Fatigue Crack Life Prediction

In practical engineering, the design data are uncertain. The data will deviate from the true value due to technical reasons such as measurement. It would result in the inaccuracy of crack fatigue life prediction. To deal with those problems, a regression model for crack fatigue life prediction is established in this study based on the conditional Bayesian theory. We use the prior distribution instead of posterior distribution in the iteration. The Monte Carlo sampling method is utilized to obtain the likelihood function and the prior distribution of the model. Then, the corresponding posterior distribution of the crack life can be obtained by likelihood and prior in iterative calculation. An engineering example of structural fatigue life of robotic is given to illustrate the application of proposed model. The effects of different parameter uncertainties on the posterior distribution of the model are compared. Also, the results in the least squares method are provided as reference.

Abstract licence: CC BY-NC-ND

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.