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

被引：0

作者：

Cui, Aiya ^{[1
]}

Zhang, Ying ^{[2
,3
]}

Zhang, Pengyu ^{[4
]}

Dong, Wei ^{[1
]}

Wang, Chunyan ^{[1
]}

机构：

[1] Beijing Inst Technol, Sch Aerosp Engn, Beijing 100081, Peoples R China

[2] Peking Univ, Sch Software & Microeletron, Beijing 100871, Peoples R China

[3] Beijing Aerosp Automat Control Inst, Beijing 100854, Peoples R China

[4] China Aerosp Sci & Technol Corp, Sci & Technol Space Phys Lab, Beijing 100000, Peoples R China

来源：

16TH IEEE INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV 2020) | 2020年

基金：

中国国家自然科学基金;

关键词：

LSTM; grey model; fault diagnosis; health prediction; fixed-wing UAVs; FAULT-DIAGNOSIS; MACHINE; MODEL;

D O I：

10.1109/icarcv50220.2020.9305491

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 real-time 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.

引用

页码：1055 / 1060

页数：6

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