Predicting potential risks associated with the fatigue of key structural components is crucial in engineering design. However, fatigue often involves entangled complexities of material microstructures and service conditions, making diagnosis and prognosis of fatigue damage challenging. We report a statistical learning framework to predict the growth of fatigue cracks and the life-to-failure of the components under loading conditions with uncertainties. Digital libraries of fatigue crack patterns and the remaining life are constructed by high-fidelity physical simulations. Dimensionality reduction and neural network architectures are then used to learn the history dependence and nonlinearity of fatigue crack growth. Path-slicing and re-weighting techniques are introduced to handle the statistical noises and rare events. The predicted fatigue crack patterns are self-updated and self-corrected by the evolving crack patterns. The end-to-end approach is validated by representative examples with fatigue cracks in plates, which showcase the digital-twin scenario in real-time structural health monitoring and fatigue life prediction for maintenance management decision-making.

翻译：在工程设计中，预测关键结构部件疲劳相关的潜在风险至关重要。然而，疲劳过程往往涉及材料微观组织与服役工况的复杂耦合，使得疲劳损伤的诊断与预测极具挑战性。我们提出了一种统计学习框架，用于预测载荷条件存在不确定性时疲劳裂纹的扩展以及部件的失效寿命。通过高保真物理仿真构建了疲劳裂纹模式与剩余寿命的数字数据库。随后采用降维技术与神经网络架构来学习疲劳裂纹扩展的历史依赖性与非线性特征。引入路径切片与重加权技术以处理统计噪声与稀有事件。预测得到的疲劳裂纹模式可随裂纹演化过程实现自我更新与校正。基于含疲劳裂纹板的代表性算例对该端到端方法进行了验证，展现了其在实时结构健康监测与疲劳寿命预测（用于维护管理决策）中的数字孪生场景。