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

Shaojie Ai, Jia Song and Guobiao Cai
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Shaojie Ai: School of Astronautics, Beihang University, Beijing 100191, China
Jia Song: School of Astronautics, Beihang University, Beijing 100191, China
Guobiao Cai: School of Astronautics, Beihang University, Beijing 100191, China

Mathematics, 2022, vol. 10, issue 10, 1-23

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.

Keywords: remaining useful life; sequence-to-sequence prognostics; transformer network; unmanned aerial vehicle; lithium-polymer battery (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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