0
  • DE
  • EN
  • FR
  • International Database and Gallery of Structures

Advertisement

Intelligent fault diagnosis of hoisting systems under complex working conditions using deep graph convolutional generative adversarial networks with limited data

Author(s): ORCID (Institute of Smart City and Intelligent Transportation, Southwest Jiaotong University, Chengdu, China)
ORCID (School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China)
ORCID (School of Mechanical Engineering, Southeast University, Nanjing, China)
(School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China)
Medium: journal article
Language(s): English
Published in: Structural Health Monitoring
DOI: 10.1177/14759217241279789
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: 10.1177/14759217241279789.
  • About this
    data sheet
  • Reference-ID
    10806183
  • Published on:
    10/11/2024
  • Last updated on:
    10/11/2024
 
Structurae cooperates with
International Association for Bridge and Structural Engineering (IABSE)
e-mosty Magazine
e-BrIM Magazine