An aircraft structural risk assessment method based on fatigue damage diagnosis and prognosis has been developed, considering fatigue crack propagation. The process is divided into three stages: initial crack diagnosis, crack diagnosis, and prediction, utilizing Monte Carlo simulation. Using 2024 aluminum alloy specimens with central holes, the study indicates that in the initial crack diagnosis stage, an inspection standard with a Single Flight Probability of Failure (SFPOF) less than 10-7 and a threshold method enhances structural fatigue crack diagnosis. In the crack diagnosis and prediction stages, iterative updates using Gaussian Process Regression (GPR) within a Dynamic Bayesian Network (DBN) improve crack propagation prediction and risk assessment accuracy. The diagnostic interval significantly impacts SFPOF, with an optimized interval balancing accuracy and computation time. Simplified and precise K value calculation methods enhance efficiency and accuracy. The method reduces costs and improves risk assessment accuracy, providing new insights for SPHM-based aircraft structural risk assessment.

中文翻译：

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一种基于疲劳损伤诊断与预后的考虑疲劳裂纹扩展的飞机结构风险评估方法


已经开发了一种基于疲劳损伤诊断和预测的飞机结构风险评估方法，考虑了疲劳裂纹扩展。该过程分为三个阶段：初始裂纹诊断、裂纹诊断和利用蒙特卡洛仿真进行预测。使用 2024 个带中心孔的铝合金试样，研究表明，在初始裂纹诊断阶段，单次飞行失效概率 （SFPOF） 小于 10-7 的检测标准和阈值法可以增强结构疲劳裂纹诊断。在裂纹诊断和预测阶段，在动态贝叶斯网络 （DBN） 中使用高斯过程回归 （GPR） 进行迭代更新可以提高裂纹扩展预测和风险评估准确性。诊断间隔对 SFPOF 有显著影响，优化的间隔平衡了准确性和计算时间。简化而精确的 K 值计算方法提高了效率和准确性。该方法降低了成本，提高了风险评估的准确性，为基于 SPHM 的飞机结构风险评估提供了新的见解。