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Fault diagnosis of control moment gyroscope based on a new CNN scheme using attention-enhanced convolutional block

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Abstract

Control moment gyroscope (CMG) is a typical attitude control system component for satellites and mobile robots, and the online fault diagnosis of CMG is crucial because it determines the stability and accuracy of the attitude control system. This paper develops a data-driven CMG fault diagnosis scheme based on a new CNN method. In this design, seven types of fault signals are converted into spectrum datasets through short-time Fourier transformation (STFT), and a new CNN network scheme called AECB-CNN is proposed based on attention-enhanced convolutional blocks (AECB). AECB-CNN can achieve high training accuracy for the CMG fault diagnosis datasets under different sliding window parameters. Finally, simulation results indicate that the proposed fault diagnosis method can achieve an accuracy of nearly 95% in 1.28 s and 100% in 2.56 s, respectively.

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Authors and Affiliations

  1. Research Center of Satellite Technology, School of Astronautics, Harbin Institute of Technology, Harbin, 150001, China

    HaoTian Zhao, Ming Liu & XiBin Cao

  2. School of Aerospace Engineering, Beijing Institute of Technology, Beijing, 100081, China

    YiYong Sun

  3. Department of Automation, Tsinghua University, Beijing, 100084, China

    YiYong Sun & Zhang Chen

  4. Control Theory and Guidance Technology, School of Astronautics, Harbin Institute of Technology, Harbin, 150001, China

    GuangRen Duan

Authors
  1. HaoTian Zhao
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  2. Ming Liu
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  3. YiYong Sun
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  4. Zhang Chen
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  5. GuangRen Duan
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  6. XiBin Cao
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Corresponding author

Correspondence to Ming Liu.

Additional information

This work was supported by the Science Center Program of the National Natural Science Foundation of China (Grant No. 62188101), the National Natural Science Foundation of China (Grant Nos. 61833009, 61690212, 51875119, 61903219, and 62073183), the Heilongjiang Touyan Team, and the Guangdong Major Project of Basic and Applied Basic Research (Grant No. 2019B030302001). In particular, the authors would like to thank Prof. Bin Liang from the Department of Automation, Tsinghua University, Beijing, China.

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Zhao, H., Liu, M., Sun, Y. et al. Fault diagnosis of control moment gyroscope based on a new CNN scheme using attention-enhanced convolutional block. Sci. China Technol. Sci. 65, 2605–2616 (2022). https://doi.org/10.1007/s11431-022-2141-9

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  • DOI: https://doi.org/10.1007/s11431-022-2141-9

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