TY - JOUR
T1 - An aircraft structural risk assessment method considering fatigue crack propagation based on fatigue damage diagnosis and prognosis
AU - Han, Liang
AU - He, Xiaofan
AU - Ning, Yu
AU - Zhang, Yanjun
AU - Zhou, Yan
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2025/1
Y1 - 2025/1
N2 - 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.
AB - 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.
KW - Crack propagation
KW - Fatigue damage diagnosis and prognosis
KW - Risk assessment
KW - Single Flight Probability of Failure
KW - Structural prognostics and health management
UR - https://www.scopus.com/pages/publications/85206481147
U2 - 10.1016/j.ijfatigue.2024.108650
DO - 10.1016/j.ijfatigue.2024.108650
M3 - 文章
AN - SCOPUS:85206481147
SN - 0142-1123
VL - 190
JO - International Journal of Fatigue
JF - International Journal of Fatigue
M1 - 108650
ER -