An Anomaly Detection Approach Based on Autoencoders for Condition Monitoring of Wind Turbines
Author:
Affiliation:
1. Karadeniz Technical University,Department of Electrical and Electronics Engineering,Trabzon,Turkey
2. KTH Royal Institute of Technology,Division of Electric Power and Energy Systems,Stockholm,Sweden
Funder
KT
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9810556/9810557/09810575.pdf?arnumber=9810575
Reference22 articles.
1. A fault detection framework using recurrent neural networks for condition monitoring of wind turbines
2. Autoencoders and Recurrent Neural Networks Based Algorithm for Prognosis of Bearing Life
3. Stacked Denoising Autoencoder With Density-Grid Based Clustering Method for Detecting Outlier of Wind Turbine Components
4. A Multi-Level-Denoising Autoencoder Approach for Wind Turbine Fault Detection
5. DBSCAN Clustering — Explained;y?ld?r?m,0
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3. Reliability-Centered Asset Management with Models for Maintenance Optimization and Predictive Maintenance: Including Case Studies for Wind Turbines;Women in Power;2023
4. A Fault Detection Approach Based on Autoencoders for Condition Monitoring of Wind Turbines;Women in Renewable Energy;2023
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