基于粒子滤波的结构疲劳裂纹扩展动态贝叶斯推断方法

准确预测结构的疲劳裂纹扩展过程是开展飞机单机寿命监控与剩余寿命估算的基础。提出一种基于动态贝叶斯网络的结构疲劳裂纹扩展预测方法，结合疲劳裂纹扩展的先验知识与后验知识来准确地推断裂纹长度；研究粒子滤波算法中不同粒子数对动态贝叶斯网络推断精度的影响规律；通过对单孔板结构与耳片连接结构件在随机载荷谱下进行裂纹扩展研究。结果表明：动态贝叶斯网络方法可以对复杂结构的疲劳裂纹扩展进行准确预测，预测精度相对于传统方法提高50% 以上。

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

Accurate prediction of fatigue crack growth serves as the cornerstone for aircraft component lifespan monitoring and residual life estimation. In this paper, a prediction method of structural 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 the single hole plate and the lug under random load spectrum, it is shown that the dynamic Bayesian network method can accurately predict the crack growth of structures, and the prediction accuracy is more than 50% higher than that of the traditional method.