To meet the predictive and condition-based maintenance requirements for the airframe structures of large passenger aircraft, a multi-source data fusion driven system for air route maintenance is developed. This system integrates engineering data from the model type development phase, including flight test measured loads, full-aircraft finite element internal forces, structural fatigue damage assessment, airworthiness limitation items, and their associated maintenance requirements. By employing methods such as data cleaning, sample reconstruction, data modeling, damage accumulation analysis, and maintenance customization optimization, the system enables comprehensive condition-based maintenance support. Through application to a specific domestic large passenger aircraft, it has resolved key challenges in data modeling accuracy and prediction efficiency for airframe structure maintenance. The system achieves rapid single-flight prediction of “flight parameters-loads-stress-damage” for critical wing regions. Based on comparison between theoretical damage per flight and actual damage values at 95% confidence level, preliminary estimates indicate that the repeat inspection interval for high-frequency eddy current inspections can be extended as follows: from 5 000 to 8 091 flight cycles for stringer hole edges at the skin-stringer splice detailed position of the lower wing panel, and from 5 500 to 8 900 flight cycles for skin hole edges.

The failure mechanisms of carbon-fiber-reinforced composites are complex, and experimental tests are costly. Traditional finite element methods, limited by current theoretical models, struggle to accurately simulate the entire failure process and exhibit significant cumulative errors, complicating precise modeling and uncertainty quantification. Machine learning approaches offer a promising alternative but generally require extensive datasets to achieve satisfactory performance. We present a multi-fidelity data-driven framework that blends a small set of high-fidelity test results with an extensive collection of low-fidelity simulation data to predict the distribution of mechanical properties in composite structures. The framework is validated through tensile-failure experiments on notched laminates. To improve the statistical representativeness of the limited experimental samples, we introduce a Bayesian data-augmentation scheme and derive the theoretical distribution of the inter-group coefficient of variation to confirm its soundness. Cross-validation shows that the proposed method attains a mean absolute error of 4.99% when predicting the 10th percentile of the failure-load distribution. The study mitigates the twin challenges of scarce experimental data and weak coupling between numerical models and physical tests.

Currently, experimental research on variable stiffness design mainly focuses on laminates. To ensure adaptability in practical application, it is imperative to conduct a systematic study on stiffened variable stiffness structures, including design, manufacture, experiment, and simulation. Based on the minimum curvature radius and process schemes, two types of T-stiffened panels were designed and manufactured. Uniaxial compression tests have been carried out and the results indicate that the buckling load of variable stiffness specimens is increased by 26.0%, while the failure load is decreased by 19.6%. The influence mechanism of variable stiffness design on the buckling and failure behavior of T-stiffened panels was explicated by numerical analysis. The primary reason for the reduced strength is the significantly increased load bearing ratio of stiffeners. As experimental investigations of stiffened variable stiffness structures are very rare, this study can be considered a reference for future work.