Measurement method of aero-engine rotor concentricity and perpendicularity based on deep belief neural network.

Abstract

When implementing the traditional assembly method, the rotor is affected by machining errors. The morphology of the rotor is complex, and the machining error of the rotors at all levels are transmitted step by step through the stop mating surface, which affects the performance and service life of the aero-engine. The evaluation of machining error of single-stage rotor is the basis of assembly quality of multi-stage rotor. In order to improve the current situation of complicated and time-consuming rotor machining error evaluation, this paper proposes to establish a deep belief neural network (DBNN) to replace the traditional procedure of depolarization. The network takes the relative evaluation error of the rotor profile data without depolarization as the input and takes the machining error of the rotors obtained after depolarization as the output. First, the evaluation mechanism of the rotor's machining error is analyzed, and the corresponding machining error influence source is selected as the input source of the deep belief neural network. Second, as DBNN is trained, and the appropriate weight initialization method and the optimization algorithm of the prediction network are selected to ensure the optimization of the whole network for feature mapping extraction of the training set. Finally, the assembly of multi-stage rotors is simulated and analyzed. It is shown in the experiments that after the iteration, the prediction network, with good training effects, has converged, and its prediction results tend to be consistent with the real values. The mean prediction error of the concentricity is 0.09 µm while the mean difference of angle of concentricity error value is 0.77°, and the mean difference of perpendicularity error value is 0.21 µm while the mean difference of angle of perpendicularity error value is 1.4°, the corresponding R2 determination coefficients were 0.99, 0.98, 0.91, and 0.94, respectively. It meets the requirements of field assembly and fully embodies the effectiveness of the procedure of depolarization based on deep confidence neural network.