Dynamic Bayesian inference method for structural fatigue crack propagation based on particle filter

Abstract

Accurately predicting the fatigue crack propagation process of aircraft structure is the basis for conducting life monitoring and residual life estimation of individual aircraft. In this paper, a prediction method of structural fatigue crack propagation based on dynamic Bayesian network is proposed, which combines the prior knowledge and the posterior knowledge of fatigue crack propagation to accurately infer the crack length. The influence of different particle numbers on the inference accuracy of the dynamic Bayesian network was studied. Through the study of crack propagation of a single hole plate and a lug under random load spectrum, it is shown that the dynamic Bayesian network method can accurately predict the fatigue crack propagation, and the prediction accuracy is more than 50% higher than that of the traditional method.

Keywords

• fatigue crack
• structural health monitoring
• data-driven
• dynamic bayesian networks
• walker model