Automatic detection system for aircraft engine turbine blade faults based on improved PSO

Introduction With the increase of human travel, aviation activities become more frequent, and flight safety gains more attention. Faults in aircraft engine turbine blades seriously threaten flight safety, while traditional detection methods face problems of low accuracy and efficiency, making them insufficient for complex operating conditions. Methods This study raises an automatic detection system for aircraft engine turbine blade faults based on improved Particle Swarm Optimization. The system integrates the advantages of Grey Wolf Optimization, Genetic Algorithm, and Particle Swarm Optimization, and builds an adaptive feature selection mechanism. It combines Relevance Vector Machine classification to train the fault detection model, completing automatic detection of turbine blade faults. Experimental results show that after 80 iterations, the system error decreases to 0.08 × 10 −3 , and the fault prediction accuracy reaches 98.33% improving by 9.16% compared with the reference system. With increased data volume, the prediction accuracy and response time reach 0.99 and 0.5 s respectively, demonstrating significantly better performance than the comparison system. Results and Discussion These results indicate that the proposed system achieves efficient and accurate detection of turbine blade faults. It provides an intelligent detection solution for aircraft engine maintenance, improves fault identification accuracy and efficiency, reduces flight safety risks, and promotes the development of intelligent diagnosis technology for aviation equipment, showing engineering application and promotion value.