UAV propeller fault diagnosis using deep learning of non-traditional χ
Artificial intelligence
Fault diagnosis
Lempel–Ziv complexity
Permutation entropy
Teager–Kaiser energy operator
UAV
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
10 Aug 2024
10 Aug 2024
Historique:
received:
13
06
2024
accepted:
05
08
2024
medline:
11
8
2024
pubmed:
11
8
2024
entrez:
10
8
2024
Statut:
epublish
Résumé
Fault detection and isolation in unmanned aerial vehicle (UAV) propellers are critical for operational safety and efficiency. Most existing fault diagnosis techniques rely basically on traditional statistical-based methods that necessitate better approaches. This study explores the application of untraditional feature extraction methodologies, namely Permutation Entropy (PE), Lempel-Ziv Complexity (LZC), and Teager-Kaiser Energy Operator (TKEO), on the PADRE dataset, which encapsulates various rotor fault configurations. The extracted features were subjected to a Chi-Square (χ
Identifiants
pubmed: 39127843
doi: 10.1038/s41598-024-69462-9
pii: 10.1038/s41598-024-69462-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
18599Subventions
Organisme : Politechnika Poznańska
ID : 0214/SBAD/0247
Informations de copyright
© 2024. The Author(s).
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