Missile Health Status Prediction Method Based on PSO-RF

Introduction: To improve the maintenance efficiency and reliability of missile systems under complex combat conditions, this study proposes a novel modeling framework integrating swarm intelligence optimization and ensemble learning to address high-dimensional nonlinear prediction in missile health management. The approach improves prediction accuracy, reduces maintenance costs, and supports lifecycle management, offering significant engineering value and defense applications. Methods: This study proposes a hybrid prediction model that integrates Particle Swarm Optimization (PSO) with the Random Forest (RF) algorithm. PSO is used to optimize key hyperparameters of the RF model to enhance its generalization ability and prediction accuracy. Results: The performance of the proposed PSO-RF model is compared with Radial Basis Function (RBF) neural networks and Back Propagation (BP) neural networks. Experimental results demonstrate that the PSO-RF model outperforms the other models, achieving an average error percentage of 1.63% and a Root Mean Square Error (RMSE) of 0.0128. Discussion: The technical scheme has become a core part of a patent application and a software copyright registration, demonstrating its originality, software implementation capability, and potential for engineering application. Conclusion: The model effectively identifies potential failure risks, providing accurate and reliable decision support for the preventive maintenance of missile systems.