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Integrating the maximum mean discrepancy metric with time–frequency enhanced convolutional neural networks for fault diagnosis

Auteur(s): ORCID (College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, China)
(College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, China)
(School of Automation, Harbin University of Science and Technology, Harbin, China)
(Harbin Aircraft Industry (Group) Co. Ltd, AviationIndustry Corporation of China, Harbin, China)
(College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, China)
(College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, China)
(College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, China)
(College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, China)
(The Department of Mechanical Engineering, Tsinghua University, Beijing, China)
Médium: article de revue
Langue(s): anglais
Publié dans: Structural Health Monitoring
DOI: 10.1177/14759217241302370
Structurae ne peut pas vous offrir cette publication en texte intégral pour l'instant. Le texte intégral est accessible chez l'éditeur. DOI: 10.1177/14759217241302370.
  • Informations
    sur cette fiche
  • Reference-ID
    10812079
  • Publié(e) le:
    17.01.2025
  • Modifié(e) le:
    17.01.2025
 
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