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.
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The datasets generated and analyzed during the current study are not publicly available due the fact that they constitute and excerpt of research in progress but are available from the corresponding author on reasonable request.
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Acknowledgements
This work was supported by the Natural Science Foundation of Heilongjiang Province of China (grant number LH2021E060), National Key R&D Program of China (grant number 2021YFF0603200), National Natural Science Foundation major research projects of China (grant number 91960109), National Natural Science Foundation of China (grant number 52275525, 52175498, 52205560, 51975158), Fundamental Research Funds for the Central Universities (grant number 2022FRFK060025, XNAUEA5750302321).
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Wu, H., Sun, C., Lu, Q. et al. Unbalance prediction method of aero-engine saddle rotor based on deep belief networks and GA-BP intelligent learning. J Intell Manuf (2024). https://doi.org/10.1007/s10845-024-02392-5
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DOI: https://doi.org/10.1007/s10845-024-02392-5