Deep Adversarial Hybrid Domain-Adaptation Network for Varying Working Conditions Fault Diagnosis of High-Speed Train Bogie
Author:
Affiliation:
1. School of Mechanical and Vehicle Engineering, Hunan University, Changsha, China
2. School of Traffic and Transportation Engineering, Central South University, Changsha, China
Funder
Major Special Projects in Changsha City
Joint Funds of the National Natural Science Foundation of China
Young Elite Scientists Sponsorship Program by the China Association for Science and Technology
Natural Science Foundation of Hunan Province China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10012124/10124762.pdf?arnumber=10124762
Reference29 articles.
1. A Brief Review of Domain Adaptation
2. Rolling Bearing Fault Diagnosis Based on Domain Adaptation and Preferred Feature Selection under Variable Working Conditions
3. A new method for intelligent fault diagnosis of machines based on unsupervised domain adaptation
4. Intelligent Fault Diagnosis by Fusing Domain Adversarial Training and Maximum Mean Discrepancy via Ensemble Learning
5. A multi-branch convolutional transfer learning diagnostic method for bearings under diverse working conditions and devices
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2. Fault Diagnosis of High-Speed Railway Track Grinding Motor Using Convolutional Neural Network;2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC);2024-02-19
3. Nonlinear predictable feature learning with explanatory reasoning for complicated industrial system fault diagnosis;Knowledge-Based Systems;2024-02
4. A Domain Generalization Network Exploiting Causal Representations and Non-Causal Representations for Three-Phase Converter Fault Diagnosis;IEEE Transactions on Instrumentation and Measurement;2024
5. A Simulated-to-Real Transfer Fault Diagnosis Method Based on Prototype Clustering Subdomain Adversarial Adaptation Network for HST Bogie Bearing;IEEE Transactions on Instrumentation and Measurement;2024
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