Unmanned Aerial Vehicle Flight Data Anomaly Detection Based on Multirate-Aware LSTM-Reference-Cited by-同舟云学术

Unmanned Aerial Vehicle Flight Data Anomaly Detection Based on Multirate-Aware LSTM

Published:2024 Issue: Volume:73 Page:1-13
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Lu Hui¹^ORCID,Wang Zan¹^ORCID,Shi Yuhui²^ORCID

Affiliation:

1. School of Electronic and Information Engineering, Beihang University, Beijing, China

2. Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, School of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

Funder

National Natural Science Foundation of China

Beijing Municipal Natural Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/19/10367905/10630804.pdf?arnumber=10630804

Reference31 articles.

1. Real-Time Fault Detection for UAV Based on Model Acceleration Engine

2. Graph Attention Network-Based Fault Detection for UAVs With Multivariant Time Series Flight Data

3. A Novel Methodology for Unsupervised Anomaly Detection in Industrial Electrical Systems

4. Feature Enhancement Based on Regular Sparse Model for Planetary Gearbox Fault Diagnosis

5. Bearing Remaining Useful Life Prediction Using Federated Learning With Taylor-Expansion Network Pruning

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