Bearing Fault Classification using Temporal Features for Wind Turbine Application: Harnessing Neural and Non-Neural Techniques
Author:
Affiliation:
1. The University of The South Pacific,School of Information Technology, Engineering, Mathematics, and Physics,Fiji
2. University of Picardie Jules Verne,Laboratory of Novel Technologies,Amiens,France
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10406302/10407105/10408580.pdf?arnumber=10408580
Reference16 articles.
1. Early Fault Detection in the Main Bearing of Wind Turbines Based on Gated Recurrent Unit (GRU) Neural Networks and SCADA Data
2. Bearing Fault Detection Based on Improved Multiscale Dispersion Entropy and Single Value Classification
3. Classification of Bearing Fault Based on Multi-class Recurrent Neural Network
4. Bearing fault detection with vibration and acoustic signals: Comparison among different machine leaning classification methods
5. A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors
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