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

Author

Listed:
  • Huilin Wu

    (Harbin Institute of Technology
    Ministry of Industry and Information Technology)

  • Chuanzhi Sun

    (Harbin Institute of Technology
    Ministry of Industry and Information Technology)

  • Qing Lu

    (Harbin Institute of Technology
    Ministry of Industry and Information Technology
    Beijing Power Machinery Institute)

  • Yinchu Wang

    (Harbin Institute of Technology
    Ministry of Industry and Information Technology
    Southeast University)

  • Yongmeng Liu

    (Harbin Institute of Technology
    Ministry of Industry and Information Technology)

  • Limin Zou

    (Harbin Institute of Technology
    Ministry of Industry and Information Technology)

  • Jiubin Tan

    (Harbin Institute of Technology
    Ministry of Industry and Information Technology)

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.

Suggested Citation

  • Huilin Wu & Chuanzhi Sun & Qing Lu & Yinchu Wang & Yongmeng Liu & Limin Zou & Jiubin Tan, 2025. "Unbalance prediction method of aero-engine saddle rotor based on deep belief networks and GA-BP intelligent learning," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2829-2840, April.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:4:d:10.1007_s10845-024-02392-5
    DOI: 10.1007/s10845-024-02392-5
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    References listed on IDEAS

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    1. Xinhua Yao & Di Wang & Tao Yu & Congcong Luan & Jianzhong Fu, 2023. "A machining feature recognition approach based on hierarchical neural network for multi-feature point cloud models," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2599-2610, August.
    2. Zhe Li & Yi Wang & Kesheng Wang, 2020. "A data-driven method based on deep belief networks for backlash error prediction in machining centers," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1693-1705, October.
    3. Yuhang Pan & Yonghao Wang & Ping Zhou & Ying Yan & Dongming Guo, 2020. "Activation functions selection for BP neural network model of ground surface roughness," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1825-1836, December.
    4. Chenglin Li & Baohai Wu & Zhao Zhang & Ying Zhang, 2023. "A novel process planning method of 3 + 2-axis additive manufacturing for aero-engine blade based on machine learning," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 2027-2042, April.
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