Deeply Integrated Autoencoder-Based Anomaly Detection and Critical Parameter Identification for Unmanned Aerial Vehicle Actuators-Reference-Cited by-同舟云学术

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Deeply Integrated Autoencoder-Based Anomaly Detection and Critical Parameter Identification for Unmanned Aerial Vehicle Actuators

Published:2024-08-01 Issue:15 Volume:24 Page:24905-24920
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Wang Yan¹,Li Shaobo²^ORCID,Yang Lei³^ORCID,Zhang Yizhong³^ORCID,Li Chuanjiang²^ORCID,Zhang Ansi²,An Xue³

Affiliation:

1. Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang, China

2. State Key Laboratory of Public Big Data, Guizhou University, Guiyang, China

3. School of Mechanical Engineering, Guizhou University, Guiyang, China

Funder

National Natural Science Foundation of China

Guizhou Provincial Science and Technology Program

Guizhou Provincial Department of Education Higher Education Schools Integrated Tackling Large Platform

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/7361/10617804/10555520.pdf?arnumber=10555520

Reference32 articles.

1. A survey of unmanned aerial vehicle flight data anomaly detection: Technologies, applications, and future directions

2. UAV Fault Detection Methods, State-of-the-Art

3. An IoT Based UAV Network For Military Applications

4. A Siamese hybrid neural network framework for few-shot fault diagnosis of fixed-wing unmanned aerial vehicles

5. Integrated method for the UAV navigation sensor anomaly detection

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