Servo systems are widely used in aerospace and other fields. To ensure the safety of the system operation, the fault detection of servo systems is very important. Servo system fault detection often uses comparative differential judgment method, which requires the establishment of a reliable model. On the one hand, servo system models are usually built using mathematical and physical methods by combining the transfer functions of the various components the actual servo system have into an algorithm that calculates the input–output relationship. On the other hand, machine learning algorithms can also be used to model the servo system. In this paper, we address this issue by developing a rocket data prediction model based on the LSTM algorithm using publicly available data provided by Shanghai Aerospace Control Technology Institute. After deriving the results, the results are evaluated by using R-squared metrics and some improvement outlooks are provided for the subsequent research.


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    Title :

    Hydraulic Servo System Fault Detection Based on Machine Learning


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Jing, Zhongliang (editor) / Strelets, Dmitry (editor) / Zhou, Shengjie (author) / Wang, Yue (author) / Yang, Rong (author) / Zhan, Xingqun (author) / Chen, Xiao (author) / Gao, Xiaoan (author)

    Conference:

    International Conference on Aerospace System Science and Engineering ; 2021 July 14, 2021 - July 16, 2021



    Publication date :

    2022-07-09


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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