Adaptive Gated Attention Network With Weighted Metric Enhancement for Fault Diagnosis of Wind Turbine Gearbox
Author:
Affiliation:
1. School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
2. School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
Funder
Science and Technology Research Program of Chongqing Municipal Education Commission
Special Foundation for Postdoctoral Research Program
Postdoctoral Natural Science Foundation of Chongqing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10012124/10147862.pdf?arnumber=10147862
Reference41 articles.
1. Fault Diagnosis of Wind Turbine Gearbox Using a Novel Method of Fast Deep Graph Convolutional Networks
2. A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox
3. Wavelet Packet Decomposition-Based Multiscale CNN for Fault Diagnosis of Wind Turbine Gearbox
4. Wind Turbine Gearbox Failure Detection Based on SCADA Data: A Deep Learning-Based Approach
5. RUL Prediction of Wind Turbine Gearbox Bearings Based on Self-Calibration Temporal Convolutional Network
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