Intelligent fault diagnosis of rotating machinery under variable working conditions based on deep transfer learning with fusion of local and global time–frequency features
| Author(s): |
Xiao Yu
(School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China)
Songcheng Wang (School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China) Hongyang Xu 
(School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China)
Kun Yu
(School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China)
Ke Feng (School of Engineering, University of British Columbia, Kelowna, BC, Canada) Yongchao Zhang (School of Engineering, University of British Columbia, Kelowna, BC, Canada) Xiaowen Liu (School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China) |
|---|---|
| Medium: | journal article |
| Language(s): | English |
| Published in: | Structural Health Monitoring, November 2023, n. 4, v. 23 |
| Page(s): | 2238-2254 |
| DOI: | 10.1177/14759217231199427 |
- About this
data sheet - Reference-ID
10755766 - Published on:
14/01/2024 - Last updated on:
20/09/2024
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