基于动态贝叶斯网络的变幅载荷下疲劳裂纹扩展预测方法

针对当前疲劳裂纹扩展预测研究中较少考虑不确定因素而导致预测结果偏差大的问题，提出基于动态贝叶斯网络（DBN）的疲劳裂纹扩展预测方法. 以变幅载荷作用下的疲劳裂纹扩展为具体对象，利用统一疲劳寿命预测（UFLP）模型构建疲劳裂纹扩展的物理状态方程；分析疲劳裂纹扩展过程典型不确定因素之间的联系，基于动态贝叶斯网络建立疲劳裂纹扩展动态性能退化模型；采用粒子滤波（PF）推断算法，向动态性能退化模型输入裂纹观测数据，修正预测结果，降低不确定因素的影响. 根据已有的裂纹扩展实验数据，给出具有不确定因素疲劳裂纹扩展预测的仿真算例，结果表明，所提出的基于动态贝叶斯网络的变幅载荷作下疲劳裂纹扩展预测方法较现有方法能够取得更好的预测精度.

Fatigue crack growth prediction method under variable amplitude load based on dynamic Bayesian network

A method of fatigue crack growth prediction based on dynamic Bayesian network (DBN) was proposed to address the problem of large deviation of prediction results due to less consideration of uncertainties in the current study of fatigue crack growth prediction. The physical state equation of fatigue crack expansion was constructed using the unified fatigue life prediction (UFLP) model using the fatigue crack expansion under variable amplitude load as the specific object. The link between typical uncertainties in the fatigue crack growth process was analyzed, and the degradation model of fatigue crack growth was built based on the DBN. Finally, the particle filter (PF) algorithm was used to input the crack observation data into the dynamic degradation model, and the prediction results were corrected to reduce the influence of uncertainties. A simulation example with uncertainty factor fatigue crack expansion prediction was given based on the existing experimental data on crack growth, and results show that the DBN-based method for fatigue crack growth prediction can achieve better prediction accuracy than existing methods.