Skip to content
RDL
Network
Ecosystem
Switch app
TR
About
FAQ
Sign in
Get started
A continual test-time domain adaptation method for online machinery fault diagnosis under dynamic operating conditions — Jinghui Tian (2025) | RDL Network
Back
Cite
Save
Save for later
Share
Home
Publications
A continual test-time domain adaptation method for online machinery fault diagnosis under dynamic operating conditions
Shared by
Hamid Reza Karimi
Politecnico di Milano
A continual test-time domain adaptation method for online machinery fault diagnosis under dynamic operating conditions
Article
2025
en
Authors
+2 more
JT
Jinghui Tian
YY
Yue Yu
Hamid Reza Karimi
Politecnico di Milano
Discussion
(0)
Sign in
to like and join the discussion.
No comments yet. Be the first to comment.
Related publications
Preprint
2025
A Continual Test-Time Domain Adaptation Method for Online Machinery Fault Diagnosis Under Dynamic Operating Conditions
Jinghui Tian
,
Yue Yu
,
Hamid Reza Karimi
,
Fei Gao
,
Jing Lin
Article
2025
A test-time adaptation method using evidential deep learning for online machinery fault diagnosis
Jinghui Tian
,
Yue Yu
,
Hamid Reza Karimi
,
Jing Lin
Preprint
2025
Research on Semi-Supervised Gearbox Fault Diagnosis Method Using Masked Contrastive Learning Under Time-Varying Operating Conditions
Zhixiang Huang
,
Jun Li
,
Jun Zhang
Article
2024
A universal multi-source domain adaptation method with unsupervised clustering for mechanical fault diagnosis under incomplete data
Jinghui Tian
,
Dongying Han
,
Hamid Reza Karimi
,
Yu Zhang
,
Peiming Shi
Neural Networks
Article
2025
Intelligent fault diagnosis of bearings based on unsupervised domain adaptive adversarial graph neural network under variable operating conditions
Chaoge Wang
,
Xinyu Tian
,
Funa Zhou
,
Hamid Reza Karimi
Discussion(0)
No comments yet. Be the first to comment.