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

Author(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)
Medium: journal article
Language(s): English
Published in: Structural Health Monitoring
DOI: 10.1177/14759217241227163
Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1177/14759217241227163.
  • About this
    data sheet
  • Reference-ID
    10775648
  • Published on:
    29/04/2024
  • Last updated on:
    29/04/2024
 
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