With the deepening adoption of whole-lifecycle management paradigms in aviation equipment, accurate prediction of remaining useful life has emerged as a pivotal technical enabler for ensuring flight safety and optimizing maintenance resource allocation. This study systematically addresses limitations in existing data-driven remaining useful life prediction methodologies through comprehensive optimizations spanning data preprocessing protocols and model architectural enhancements. To mitigate the local optimum entrapment inherent in deep learning hyperparameter optimization, an improved Gray Wolf Optimizer incorporating dynamic perturbation factors is proposed. This algorithm is subsequently deployed to optimize hyperparameters within the redesigned predictive architecture. Comparative analyses reveal that the proposed framework achieves superior prediction accuracy compared to mainstream optimization-driven models and state-of-the-art approaches. The results substantiate the capability of dynamic perturbation strategies to enhance both hyperparameter optimization quality and prediction stability, ultimately delivering an efficient solution for aero-engine remaining useful life estimation. The experiments on the C-MAPSS Dataset verify the effectiveness of these improvements.

To solve the problem of insufficient accuracy in tool wear process modeling and Remaining Useful Life (RUL) estimation, this study proposes a two-stage prediction method. Firstly, a linear prediction benchmark model is constructed: Support Vector Regression (SVR) is used to preliminarily model the tool wear process, obtaining initial prediction results and their error distribution. Building on this foundation, an Autoencoder (AE) is introduced to establish a nonlinear mapping relationship for the errors, achieving effective compensation of the SVR prediction results and establishing the SVR–AE prediction model. To further enhance model performance, the Ant Colony Optimization (ACO) algorithm is utilized to optimize three key parameters: the number of training epochs, batch size, and hidden layer dimensions, ultimately establishing the ACO–SVR–AE optimization model. Experimental validation demonstrates that on the PHM2010 dataset, compared to the Support Vector Regression (SVR) and Autoencoder (AE) models, the proposed method achieves average reductions of 26.1% in Mean Squared Error (MSE) and 14.5% in Mean Absolute Error (MAE). Compared to traditional random forest and neural network models, the MSE and MAE show average reductions of 32.3% and 25.3%. By combining linear modeling with nonlinear error compensation, this method provides an integrated optimization approach to prediction tasks in complex industrial scenarios.

The thermal data of the hypersonic wind tunnel field accurately reflect the aerodynamic performance and key parameters of the aircraft model. However, the prediction of the temperature in hypersonic wind tunnels has problems such as a large delay, nonlinearity and multivariable coupling. In order to reduce the influence brought by temperature changes and improve the accuracy of temperature prediction in the field control of hypersonic wind tunnels, this paper first combines kernel principal component analysis (KPCA) with phase space reconstruction to preprocess the temperature data set of wind tunnel tests, and the processed data set is used as the input of the temperature-prediction model. Secondly, support vector regression is applied to the construction of the temperature prediction model for the hypersonic wind-tunnel temperature field. Meanwhile, aiming at the problem of difficult parameter-combination selection in support vector regression machines, an Improved African Vulture Optimization Algorithm (IAVOA) based on adaptive chaotic mapping and local search enhancement is proposed to conduct combination optimization of parameters in support vector regression. The improved African Vulture Optimization Algorithm (AVOA) proposed in this paper was compared and analyzed with the traditional AVOA, PSO (Particle Swarm Optimization Algorithm) and GWO (Grey Wolf Optimizer) algorithms through 10 basic test functions, and the superiority of the improved AVOA algorithm proposed in this paper in optimizing the parameters of the support vector regression machine was verified in the actual temperature data in wind-tunnel field control.