Random Forest Applied to Mass Imbalance Classification in Wind Turbines
Author:
Affiliation:
1. UFSM,Centro de Tecnologia,Santa Maria,RS,Brasil
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10322551/10322584/10322625.pdf?arnumber=10322625
Reference19 articles.
1. The prediction and diagnosis of wind turbine faults
2. Stacked Multilevel-Denoising Autoencoders: A New Representation Learning Approach for Wind Turbine Gearbox Fault Diagnosis
3. Métodos de otimização hiperparamétrica: um estudo comparativo utilizando árvores de decisão e florestas aleatórias na classificação binária;júnior,2018
4. Random search for hyper-parameter optimization, Journal of Machine Learning Research;bergstra,2012
5. Rotor-Current-Based Fault Diagnosis for DFIG Wind Turbine Drivetrain Gearboxes Using Frequency Analysis and a Deep Classifier
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