An RL-Guided Hybrid Forecasting Framework for Aircraft Engine RUL and Performance Emission Prediction

In this paper, a new hybrid prediction method is proposed for estimating remaining useful life, emissions, and performance parameters using experimental data obtained from a micro-turbojet engine. Experiments were conducted under various rotational speed conditions, yielding a total of 342 measurement points. Turbine speed, exhaust gas temperature, fuel flow rate, and thrust were considered as input variables in the study. Thermal efficiency, total power, CO2, and NO2 were considered as output variables. The experimental findings showed that thermal efficiency varied between 0.49% and 7.1%, total power between 0.266 and 13.94 kW, and CO2 emissions by volume between 0.317% and 2.183%. The proposed RL-MH-LR-CBR approach combines the advantages of multiple methods. In this method, the interpretable formulation of linear regression serves as the foundation. Additionally, in the adaptive meta-heuristic optimization process, a hyper-heuristic selection mechanism based on the UCB1-based multi-arm bandit approach is used to select the optimal algorithm from among the meta-heuristic methods. Finally, the CatBoost-based residual error learning component aims to capture non-linear patterns that cannot be explained by the linear model. The method was compared with 14 different methods on both the NASA C-MAPSS FD001 dataset and real engine data. The results demonstrate that the proposed framework exhibits more balanced, stable, and higher generalization capabilities compared to classical regression models and powerful AI methods, particularly in non-linear, noisy, and heterogeneous outputs. In the real engine dataset, the proposed method produced R2 values of 0.968 for CO2 and 0.936 for NO2, while the predictive performance was even stronger for thermal efficiency and total power, with corresponding R2 values of 0.998 and 0.995, respectively. Additionally, the method demonstrated a clear advantage in hard-to-model outputs by reducing the error level to 0.061 in NO2 predictions. These findings demonstrate that the proposed approach is not limited to micro-turbojet-engines. The developed method provides a robust decision support framework that is applicable, scalable, and generalizable to predictive maintenance, emissions monitoring, energy systems, aviation analytics, and other highly dynamic engineering problems.