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

Autor(en): 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)
Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Structural Health Monitoring
DOI: 10.1177/14759217241227163
Structurae kann Ihnen derzeit diese Veröffentlichung nicht im Volltext zur Verfügung stellen. Der Volltext ist beim Verlag erhältlich über die DOI: 10.1177/14759217241227163.
  • Über diese
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  • Reference-ID
    10775648
  • Veröffentlicht am:
    29.04.2024
  • Geändert am:
    29.04.2024
 
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