Bearing Fault Diagnosis Method Based on Morphological Feature and Time-Frequency Interactive Convolutional Network
Author:
Affiliation:
1. College of Electrical Engineering and Automation, Anhui University,Hefei,China
2. Belarusian State University of Informatics and Radioelectronics
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10579949/10579951/10581428.pdf?arnumber=10581428
Reference13 articles.
1. An Integrated Multitasking Intelligent Bearing Fault Diagnosis Scheme Based on Representation Learning Under Imbalanced Sample Condition
2. A novel conditional weighting transfer Wasserstein auto-encoder for rolling bearing fault diagnosis with multi-source domains
3. Fault Diagnosis of Wind Turbine Gearbox Based on Deep Bi-Directional Long Short-Term Memory Under Time-Varying Non-Stationary Operating Conditions
4. Adaptive power spectrum Fourier decomposition method with application in fault diagnosis for rolling bearing
5. Compound gear-bearing fault feature extraction using statistical features based on time-frequency method
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