Abstract
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.
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