IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i10p1733-d818693.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/10/1733/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/10/1733/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sunghun Jung & Heon Jeong, 2017. "Extended Kalman Filter-Based State of Charge and State of Power Estimation Algorithm for Unmanned Aerial Vehicle Li-Po Battery Packs," Energies, MDPI, vol. 10(8), pages 1-13, August.
    2. Tang, Xiaopeng & Zou, Changfu & Yao, Ke & Lu, Jingyi & Xia, Yongxiao & Gao, Furong, 2019. "Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method," Applied Energy, Elsevier, vol. 254(C).
    3. Wenbai Chen & Weizhao Chen & Huixiang Liu & Yiqun Wang & Chunli Bi & Yu Gu, 2022. "A RUL Prediction Method of Small Sample Equipment Based on DCNN-BiLSTM and Domain Adaptation," Mathematics, MDPI, vol. 10(7), pages 1-14, March.
    4. Sierra, G. & Orchard, M. & Goebel, K. & Kulkarni, C., 2019. "Battery health management for small-size rotary-wing electric unmanned aerial vehicles: An efficient approach for constrained computing platforms," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 166-178.
    5. Eleftheroglou, Nick & Zarouchas, Dimitrios & Loutas, Theodoros & Alderliesten, Rene & Benedictus, Rinze, 2018. "Structural health monitoring data fusion for in-situ life prognosis of composite structures," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 40-54.
    6. Yu Mo & Qianhui Wu & Xiu Li & Biqing Huang, 2021. "Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1997-2006, October.
    7. Bi, Haiyun & Qi, Guoyuan & Hu, Jianbing & Faradja, Philippe & Chen, Guanrong, 2020. "Hidden and transient chaotic attractors in the attitude system of quadrotor unmanned aerial vehicle," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lin, Yan-Hui & Chang, Liang & Guan, Lu-Xin, 2024. "Enhanced stochastic recurrent hybrid model for RUL Predictions via Semi-supervised learning," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    2. Li, Yuanfu & Chen, Yao & Hu, Zhenchao & Zhang, Huisheng, 2023. "Remaining useful life prediction of aero-engine enabled by fusing knowledge and deep learning models," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    3. Zhang, Jiusi & Li, Xiang & Tian, Jilun & Luo, Hao & Yin, Shen, 2023. "An integrated multi-head dual sparse self-attention network for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    4. Kim, Sung Wook & Oh, Ki-Yong & Lee, Seungchul, 2022. "Novel informed deep learning-based prognostics framework for on-board health monitoring of lithium-ion batteries," Applied Energy, Elsevier, vol. 315(C).
    5. Li, Xiaoyu & Zhang, Zuguang & Wang, Wenhui & Tian, Yong & Li, Dong & Tian, Jindong, 2020. "Multiphysical field measurement and fusion for battery electric-thermal-contour performance analysis," Applied Energy, Elsevier, vol. 262(C).
    6. Kamei, Sayaka & Taghipour, Sharareh, 2023. "A comparison study of centralized and decentralized federated learning approaches utilizing the transformer architecture for estimating remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    7. Hsu, Chia-Wei & Xiong, Rui & Chen, Nan-Yow & Li, Ju & Tsou, Nien-Ti, 2022. "Deep neural network battery life and voltage prediction by using data of one cycle only," Applied Energy, Elsevier, vol. 306(PB).
    8. Wen, Pengfei & Zhao, Shuai & Chen, Shaowei & Li, Yong, 2021. "A generalized remaining useful life prediction method for complex systems based on composite health indicator," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    9. Zhuang, Jichao & Jia, Minping & Ding, Yifei & Ding, Peng, 2021. "Temporal convolution-based transferable cross-domain adaptation approach for remaining useful life estimation under variable failure behaviors," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    10. da Silva, Samuel Filgueira & Eckert, Jony Javorski & Corrêa, Fernanda Cristina & Silva, Fabrício Leonardo & Silva, Ludmila C.A. & Dedini, Franco Giuseppe, 2022. "Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle," Applied Energy, Elsevier, vol. 324(C).
    11. Sunghun Jung & Yonghyeon Jo & Young-Joon Kim, 2019. "Aerial Surveillance with Low-Altitude Long-Endurance Tethered Multirotor UAVs Using Photovoltaic Power Management System," Energies, MDPI, vol. 12(7), pages 1-14, April.
    12. Xu, Dan & Xiao, Xiaoqi & Liu, Jie & Sui, Shaobo, 2023. "Spatio-temporal degradation modeling and remaining useful life prediction under multiple operating conditions based on attention mechanism and deep learning," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    13. Fabian Rücker & Ilka Schoeneberger & Till Wilmschen & Ahmed Chahbaz & Philipp Dechent & Felix Hildenbrand & Elias Barbers & Matthias Kuipers & Jan Figgener & Dirk Uwe Sauer, 2022. "A Comprehensive Electric Vehicle Model for Vehicle-to-Grid Strategy Development," Energies, MDPI, vol. 15(12), pages 1-31, June.
    14. Woo-Yong Kim & Pyeong-Yeon Lee & Jonghoon Kim & Kyung-Soo Kim, 2019. "A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles," Energies, MDPI, vol. 12(17), pages 1-20, September.
    15. Tu, Hao & Moura, Scott & Wang, Yebin & Fang, Huazhen, 2023. "Integrating physics-based modeling with machine learning for lithium-ion batteries," Applied Energy, Elsevier, vol. 329(C).
    16. Hasan Tercan & Tobias Meisen, 2022. "Machine learning and deep learning based predictive quality in manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1879-1905, October.
    17. Yang, Zaoli & Shang, Wen-Long & Zhang, Haoran & Garg, Harish & Han, Chunjia, 2022. "Assessing the green distribution transformer manufacturing process using a cloud-based q-rung orthopair fuzzy multi-criteria framework," Applied Energy, Elsevier, vol. 311(C).
    18. Jing Wang & Shubin Lyu & C. L. Philip Chen & Huimin Zhao & Zhengchun Lin & Pingsheng Quan, 2023. "SPRBF-ABLS: a novel attention-based broad learning systems with sparse polynomial-based radial basis function neural networks," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1779-1794, April.
    19. Li, Shuangqi & He, Hongwen & Su, Chang & Zhao, Pengfei, 2020. "Data driven battery modeling and management method with aging phenomenon considered," Applied Energy, Elsevier, vol. 275(C).
    20. Dixon, James & Andersen, Peter Bach & Bell, Keith & Træholt, Chresten, 2020. "On the ease of being green: An investigation of the inconvenience of electric vehicle charging," Applied Energy, Elsevier, vol. 258(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:10:p:1733-:d:818693. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.