Aeroengine performance prediction based on fuzzy information granulation and Optimized SVM

The aeroengine is the heart of the aircraft, which is a complex aero-thermodynamic system integrating mechanical, electrical, pneumatic and hydraulic technologies. The working process is complex and changeable, and the operating conditions are severe. The problems of safety and maintenance are very prominent. Monitoring and forecasting engine performance is the core content of improving its safety and reliability. In this paper, an aeroengine performance prediction scheme based on fuzzy information granulation and optimized support vector machine is proposed to achieve accurate prediction of aeroengine performance range. The application of fuzzy information granulation can realize interval prediction by using the established fuzzy rules to eliminate the adverse effects caused by random errors. SVM is an efficient regression prediction model, which can be well applied in the regression prediction of aeroengine performance parameters. The simulation results show that the prediction scheme can track the performance of aeroengine effectively and predict it accurately.