Generative Domain Generalization Network based on Local Maximum Mean Discrepancy for Bearing Fault Diagnosis of Wind Turbine
Author:
Affiliation:
1. Shenyang University of Technology,College of Electrical Engineering,Shenyang,110870
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10587298/10587321/10587439.pdf?arnumber=10587439
Reference14 articles.
1. Fault Diagnosis for Wind Turbine Generators Using Normal Behavior Model Based on Multi-Task Learning
2. Fault detection in wind turbine generators using a meta-learning-based convolutional neural network
3. Unsupervised domain adaptation of bearing fault diagnosis based on Join Sliced Wasserstein Distance
4. A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings
5. New domain adaptation method in shallow and deep layers of the CNN for bearing fault diagnosis under different working conditions
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