Intelligent Health Management of Fixed-Wing UAVs: A Deep-Learning-based Approach

Aiya Cui; Ying Zhang; Pengyu Zhang; Wei Dong; Chunyan Wang

doi:10.1109/ICARCV50220.2020.9305491

Intelligent Health Management of Fixed-Wing UAVs: A Deep-Learning-based Approach

Aiya Cui, Ying Zhang, Pengyu Zhang, Wei Dong, Chunyan Wang

School of Aerospace Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Citations (Scopus)

Abstract

In this paper, the fault diagnosis and health management of fixed-wing UAVs are investigated based on the deep learning technique. The proposed method includes 5 models: flight data generation model, sample training prediction model based on the Long Short-Term Memory (LSTM) network, prediction model based on the grey model, combined prediction model and health calculation and management model. The realtime output of the health prediction value of the fixed-wing UAVs can be obtained, which makes it possible to take remedial action before the fault occurs. And numerical simulations demonstrate the feasibility of the proposed method.

Original language	English
Title of host publication	16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1055-1060
Number of pages	6
ISBN (Electronic)	9781728177090
DOIs	https://doi.org/10.1109/ICARCV50220.2020.9305491
Publication status	Published - 13 Dec 2020
Event	16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020 - Virtual, Shenzhen, China Duration: 13 Dec 2020 → 15 Dec 2020

Publication series

Name	16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020

Conference

Conference	16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020
Country/Territory	China
City	Virtual, Shenzhen
Period	13/12/20 → 15/12/20

Access to Document

10.1109/ICARCV50220.2020.9305491

Cite this

Cui, A., Zhang, Y., Zhang, P., Dong, W., & Wang, C. (2020). Intelligent Health Management of Fixed-Wing UAVs: A Deep-Learning-based Approach. In 16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020 (pp. 1055-1060). Article 9305491 (16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICARCV50220.2020.9305491

Cui, Aiya ; Zhang, Ying ; Zhang, Pengyu et al. / Intelligent Health Management of Fixed-Wing UAVs : A Deep-Learning-based Approach. 16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1055-1060 (16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020).

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title = "Intelligent Health Management of Fixed-Wing UAVs: A Deep-Learning-based Approach",

abstract = "In this paper, the fault diagnosis and health management of fixed-wing UAVs are investigated based on the deep learning technique. The proposed method includes 5 models: flight data generation model, sample training prediction model based on the Long Short-Term Memory (LSTM) network, prediction model based on the grey model, combined prediction model and health calculation and management model. The realtime output of the health prediction value of the fixed-wing UAVs can be obtained, which makes it possible to take remedial action before the fault occurs. And numerical simulations demonstrate the feasibility of the proposed method.",

author = "Aiya Cui and Ying Zhang and Pengyu Zhang and Wei Dong and Chunyan Wang",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020 ; Conference date: 13-12-2020 Through 15-12-2020",

year = "2020",

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doi = "10.1109/ICARCV50220.2020.9305491",

language = "English",

series = "16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020",

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}

Cui, A, Zhang, Y, Zhang, P, Dong, W & Wang, C 2020, Intelligent Health Management of Fixed-Wing UAVs: A Deep-Learning-based Approach. in 16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020., 9305491, 16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1055-1060, 16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020, Virtual, Shenzhen, China, 13/12/20. https://doi.org/10.1109/ICARCV50220.2020.9305491

Intelligent Health Management of Fixed-Wing UAVs: A Deep-Learning-based Approach. / Cui, Aiya; Zhang, Ying; Zhang, Pengyu et al.
16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1055-1060 9305491 (16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Intelligent Health Management of Fixed-Wing UAVs

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AU - Cui, Aiya

AU - Zhang, Ying

AU - Zhang, Pengyu

AU - Dong, Wei

AU - Wang, Chunyan

PY - 2020/12/13

Y1 - 2020/12/13

N2 - In this paper, the fault diagnosis and health management of fixed-wing UAVs are investigated based on the deep learning technique. The proposed method includes 5 models: flight data generation model, sample training prediction model based on the Long Short-Term Memory (LSTM) network, prediction model based on the grey model, combined prediction model and health calculation and management model. The realtime output of the health prediction value of the fixed-wing UAVs can be obtained, which makes it possible to take remedial action before the fault occurs. And numerical simulations demonstrate the feasibility of the proposed method.

AB - In this paper, the fault diagnosis and health management of fixed-wing UAVs are investigated based on the deep learning technique. The proposed method includes 5 models: flight data generation model, sample training prediction model based on the Long Short-Term Memory (LSTM) network, prediction model based on the grey model, combined prediction model and health calculation and management model. The realtime output of the health prediction value of the fixed-wing UAVs can be obtained, which makes it possible to take remedial action before the fault occurs. And numerical simulations demonstrate the feasibility of the proposed method.

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M3 - Conference contribution

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Cui A, Zhang Y, Zhang P, Dong W, Wang C. Intelligent Health Management of Fixed-Wing UAVs: A Deep-Learning-based Approach. In 16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1055-1060. 9305491. (16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020). doi: 10.1109/ICARCV50220.2020.9305491

Intelligent Health Management of Fixed-Wing UAVs: A Deep-Learning-based Approach

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