Probabilistic method for fatigue crack growth prediction with hybrid prior

Abstract

In practical engineering, there is usually multi-source prior knowledge which can be integrated for fatigue crack growth (FCG) prediction. This paper proposes a new probabilistic prediction method which enables the input of hybrid prior. This method comprises two inference steps. In the first inference step, a set of candidate priors are input. Then, the Monte Carlo integration is adopted in the calculation of posterior belief of each candidate prior. In the second inference step, the particle filter is extended to conduct Bayesian inference with hybrid prior. Numerical studies show integrating multiple priors can increase the robustness of FCG prediction.