Rolling Bearing Fault Diagnosis Based on Time-Frequency Transform-Assisted CNN: A Comparison Study
Author:
Affiliation:
1. College of Electrical Engineering and Automation, Shandong University of Science and Technology,Qingdao,P. R. China,266590
Funder
National Natural Science Foundation (NNSF) of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10165684/10165785/10166631.pdf?arnumber=10166631
Reference25 articles.
1. A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings
2. A survey of deep neural network architectures and their applications
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2. Fault Diagnosis of Rolling Bearing Based on CBAM-ICNN;2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC);2024-06-07
3. Rolling bearing fault diagnosis based on channel attention multi-scale convolution-BiLSTM network;2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS);2024-05-17
4. An Adversarial Single-Domain Generalization Network for Fault Diagnosis of Wind Turbine Gearboxes;Journal of Marine Science and Engineering;2023-12-18
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