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Intelligent fault diagnosis of storage stacking machinery under variable working conditions using attention-based adaptive multimodal feature fusion networks

Auteur(s): ORCID (School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China)
(Institute of Smart City and Intelligent Transportation, Southwest Jiaotong University, Chengdu, China)
(School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China)
(School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China)
(School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China)
(School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China)
(School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China)
(School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China)
(School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China)
(School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China)
(School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China)
Médium: article de revue
Langue(s): anglais
Publié dans: Structural Health Monitoring, , n. 6, v. 23
Page(s): 3465-3485
DOI: 10.1177/14759217241227163
Structurae ne peut pas vous offrir cette publication en texte intégral pour l'instant. Le texte intégral est accessible chez l'éditeur. DOI: 10.1177/14759217241227163.
  • Informations
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  • Reference-ID
    10775648
  • Publié(e) le:
    29.04.2024
  • Modifié(e) le:
    10.11.2024
 
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