PhysiCausalNet: A Causal- and Physics-Driven Domain Generalization Network for Cross-Machine Fault Diagnosis of Unseen Domain
Author:
Affiliation:
1. State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, China
Funder
National Natural Science Foundation of China
National Key Research and Development Program of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/9424/10550081/10473208.pdf?arnumber=10473208
Reference39 articles.
1. Thermographic fault diagnosis of electrical faults of commutator and induction motors
2. Thermographic Fault Diagnosis of Shaft of BLDC Motor
3. A survey on Deep Learning based bearing fault diagnosis
4. Artificial intelligence for fault diagnosis of rotating machinery: A review
5. Deep learning for AI
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1. Fault vibration model driven fault-aware domain generalization framework for bearing fault diagnosis;Advanced Engineering Informatics;2024-10
2. Domain generalized open-set intelligent fault diagnosis based on feature disentanglement meta-learning;Measurement Science and Technology;2024-08-02
3. Feature Adaptive Modulation and Prototype Learning for Domain Generalization Intelligent Fault Diagnosis;IEEE Transactions on Industrial Informatics;2024
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