Articles | Volume 15, issue 1
https://doi.org/10.5194/ms-15-87-2024
https://doi.org/10.5194/ms-15-87-2024
Research article
 | 
19 Feb 2024
Research article |  | 19 Feb 2024

A convolutional neural-network-based diagnostic framework for industrial bearing

Bowen Yu and Chunli Xie

Viewed

Total article views: 551 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
478 50 23 551 20 17
  • HTML: 478
  • PDF: 50
  • XML: 23
  • Total: 551
  • BibTeX: 20
  • EndNote: 17
Views and downloads (calculated since 19 Feb 2024)
Cumulative views and downloads (calculated since 19 Feb 2024)

Viewed (geographical distribution)

Total article views: 521 (including HTML, PDF, and XML) Thereof 521 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 13 Oct 2024
Download
Short summary
In this study, we introduce an innovative diagnostic framework tailored to industrial bearings that facilitates automated feature extraction and enables end-to-end fault detection. Our validation across multiple bearing vibration datasets confirms the framework's superiority in handling complex and non-stationary signals in industrial environments.