The flow rate of aircraft hydraulic system is one of the important parameters to measure the performance of air-craft. The stability of flow rate of hydraulic pipeline is related to the safety of hydraulic system and even aircraft flight. Aiming at the problem that it is difficult to measure the load flow of aircraft hydraulic system, a prediction method based on ensemble learning for the load flow of Engine Driven Pump (EDP) is proposed. According to the different working conditions of EDP, the QPSO-LightGBM intelligent integrated learning algorithm model is established. Through Quantum Particle Swarm Optimization (QPSO) algorithm optimizes the super parameters of Light Gradient Boosting Machine (LightGBM) algorithm, and realizes the regression prediction model of load flow by three factors: system oil pressure, temperature and high pressure oil filtration pressure difference. Finally, by com-paring multiple linear regression, random forest and XGBoost, the Root Mean Square Error(RMSE) of the proposed method is 1.0152 and the goodness of fit R2 has reached to 0.9988. The effectiveness of QPSO-LightGBM method is verified. The method can provide technical support for the fault diagnosis of aeronautical hydraulic system.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Flow Prediction of Aero Engine Driven Pump Based on QPSO-LightGBM


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Zhao, Yangyang (author) / Niu, Wei (author) / Wang, Meinan (author)


    Publication date :

    2021-11-02


    Size :

    7 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Flow Prediction of Aero Engine Driven Pump Based on QPSO-LightGBM

    Zhao, Yangyang / Niu, Wei / Wang, Meinan | British Library Conference Proceedings | 2022


    Flow Prediction of Aero Engine Driven Pump Based on QPSO-LightGBM

    Zhao, Yangyang / Niu, Wei / Wang, Meinan | TIBKAT | 2022


    Modeling method of variable cycle engine based on QPSO hybrid algorithm

    Xiao, Hongliang / Li, Huacong / Li, Jia et al. | British Library Online Contents | 2018


    Flight delay prediction based on LightGBM

    Tao, Jiang / Man, Hua / Yanling, Li | IEEE | 2021