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Sequence-to-Sequence Remaining Useful Life Prediction of the Highly Maneuverable Unmanned Aerial Vehicle: A Multilevel Fusion Transformer Network Solution

Author

Listed:
  • Shaojie Ai

    (School of Astronautics, Beihang University, Beijing 100191, China
    Aerospace Crafts Technology Institute, Beihang University, Beijing 100191, China)

  • Jia Song

    (School of Astronautics, Beihang University, Beijing 100191, China
    Aerospace Crafts Technology Institute, Beihang University, Beijing 100191, China)

  • Guobiao Cai

    (School of Astronautics, Beihang University, Beijing 100191, China
    Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies of Ministry of Education, Beihang University, Beijing 100191, China)

Abstract

The remaining useful life (RUL) of the unmanned aerial vehicle (UAV) is primarily determined by the discharge state of the lithium-polymer battery and the expected flight maneuver. It needs to be accurately predicted to measure the UAV’s capacity to perform future missions. However, the existing works usually provide a one-step prediction based on a single feature, which cannot meet the reliability requirements. This paper provides a multilevel fusion transformer-network-based sequence-to-sequence model to predict the RUL of the highly maneuverable UAV. The end-to-end method is improved by introducing the external factor attention and multi-scale feature mining mechanism. Simulation experiments are conducted based on a high-fidelity quad-rotor UAV electric propulsion model. The proposed method can rapidly predict more precisely than the state-of-the-art. It can predict the future RUL sequence by four-times the observation length (32 s) with a precision of 83% within 60 ms.

Suggested Citation

  • Shaojie Ai & Jia Song & Guobiao Cai, 2022. "Sequence-to-Sequence Remaining Useful Life Prediction of the Highly Maneuverable Unmanned Aerial Vehicle: A Multilevel Fusion Transformer Network Solution," Mathematics, MDPI, vol. 10(10), pages 1-23, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:10:p:1733-:d:818693
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    References listed on IDEAS

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