UAV propeller fault diagnosis using deep learning of non-traditional χ2-selected Taguchi method-tested Lempel–Ziv complexity and Teager–Kaiser energy features

Location
Deutsche Nationalbibliothek Frankfurt am Main
Extent
1 Online-Ressource.
Language
Englisch

Bibliographic citation
UAV propeller fault diagnosis using deep learning of non-traditional χ2-selected Taguchi method-tested Lempel–Ziv complexity and Teager–Kaiser energy features ; volume:14 ; number:1 ; day:10 ; month:8 ; year:2024 ; pages:1-16 ; date:12.2024
Scientific reports ; 14, Heft 1 (10.8.2024), 1-16, 12.2024

Creator
Al-Haddad, Luttfi A.
Giernacki, Wojciech
Basem, Ali
Khan, Zeashan Hameed
Jaber, Alaa Abdulhady
Al-Haddad, Sinan A.
Contributor
SpringerLink (Online service)

DOI
10.1038/s41598-024-69462-9
URN
urn:nbn:de:101:1-2410292109316.380492530128
Rights
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Last update
15.08.2025, 7:32 AM CEST

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Associated

  • Al-Haddad, Luttfi A.
  • Giernacki, Wojciech
  • Basem, Ali
  • Khan, Zeashan Hameed
  • Jaber, Alaa Abdulhady
  • Al-Haddad, Sinan A.
  • SpringerLink (Online service)

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