Coupling mechanical model and failure of aeroengine ceramic matrix composite based on genetic algorithm

Abstract

In order to understand the coupling mechanical model of aeroengine ceramic matrix composite, the author proposed a coupling mechanical model and failure study of aeroengine ceramic matrix composite based on genetic algorithm. The author first analyzed that the real-time model of an aircraft engine and the matching accuracy of the engine directly affect the accuracy of aircraft engine fault diagnosis, a least squares support vector regression (AGA LSSVR) method based on adaptive genetic algorithm was proposed to modify the real-time model of an aircraft engine, effectively improving the matching accuracy of the model. Secondly, the impact of parameter selection in least squares support vector machines on model correction was analyzed, and an adaptive genetic algorithm was used to search for the optimal parameters in the parameter selection space. Finally, compare the correction effects of methods such as (BP) neural network, support vector regression machine, AGA LSSVR in airborne models. The results indicate that: The proposed AGA LSSVR has good correction accuracy, which verifies the effectiveness of the correction model.