Research on aircraft engine maintenance strategy considering mixture distribution of random failures under special operating conditions

Considering the coupled failure risks of aero-turbofan engines under special operating conditions such as adverse meteorological conditions, bird strikes, and foreign object damage (FOD), a hybrid-distribution maintenance optimization model integrating degradation failures and sudden failures was developed. At the model construction stage, the Weibull distribution was employed to characterize the statistical behavior of sudden failures, while a joint probability distribution framework for multivariate degradation failures was established by integrating the Gamma process and the Clayton Copula function based on shared latent risk factors. Subsequently, a multi-objective optimization model was formulated with objectives of minimizing maintenance cost, maximizing degradation failure reliability, and maximizing sudden failure reliability. The NSGA-Ⅲ algorithm was then adopted to obtain the Pareto optimal solution set. Validation results based on the NASA turbofan engine degradation simulation dataset demonstrated that, under the optimal maintenance strategy, the total expected maintenance cost was CYN 15 749.6, while the overall system reliability reached 0.950 6. The NSGA-Ⅲ algorithm achieved an IGD (inverted generational distance) value of 5.8×10⁻⁴, representing performance and convergence improvements of 13.4% and 29.3%, respectively, compared with the NSGA-Ⅱ algorithm. In Addition, the LS-SVM (least squares support vector machine) prediction model yielded a mean relative error of 6.676%. The hybrid distribution model can accurately quantify compound failure risks and achieve optimal maintenance cost control while ensuring system reliability, thereby providing theoretical support for intelligent maintenance decision-making of aero-engines.