Source-free domain adaptation network for rolling bearing fault diagnosis
Author:
Affiliation:
1. Chang’an University,Key Laboratory of Road Construction Technology and Equipment of Ministry of Education,Xi’an,Shaanxi,China,710064
Funder
National Natural Science Foundation of China
Research and Development
Fundamental Research Funds for the Central Universities
China Postdoctoral Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10215380/10215516/10216194.pdf?arnumber=10216194
Reference27 articles.
1. Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation;liang;Proceedings of the 37th International Conference on Machine Learning,2020
2. Simulation-Driven Domain Adaptation for Rolling Element Bearing Fault Diagnosis;liu;IEEE Transactions on Industrial Informatics,2021
3. Domain Adaptive Neural Networks for Object Recognition;ghifary;PRICAI 2014 Trends in Artificial Intelligence,2014
4. A Survey on Deep Transfer Learning
5. Transfer learning for process fault diagnosis: Knowledge transfer from simulation to physical processes
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