Unbalance prediction method of aero-engine saddle rotor based on deep belief networks and GA-BP intelligent learning

Abstract

Aiming at the problems of complex and time-consuming process of manual adjustment of eccentricity and tilt in the evaluation of machining error of aero-engine saddle rotor, and inaccurate measurement of unbalance after multi-stage rotor assembly, this paper proposes an unbalance prediction method based on Genetic Algorithm Back Propagation (GA-BP) neural network and deep belief networks (DBN). Firstly, according to the definition of single-stage rotor machining error, the influence source of saddle rotor machining error and the evaluation of machining error are analyzed. Secondly, GA-BP neural network is established to obtain the concentricity and flatness of saddle rotors at all stages as the error source of unbalance. Then, the output of the GA-BP neural network is used as the input of the DBN to establish the unbalance prediction network model. Finally, the experimental verification is carried out based on the experimental measurement data of an engine rotor unbalance. The results show that the mean value and root mean square error (RMSE) of the unbalance are 16.72 g·mm and 32.71 g·mm respectively, and R-squared (R²) determination coefficient is 0.96 when the 80 groups of samples are tested by the prediction method of DBN. Compared with the method based on the traditional error transfer model, the proposed method based on DBN and GA-BP reduces the average error and mean square error by 86.08% and 75.97% respectively, which greatly reduces the measurement error of rotor unbalance. Therefore, this method can provide technical guidance for the optimal assembly of multi-stage rotors, thereby improving the assembly quality of multi-stage rotors.