Open Journal of Applied Sciences

Open Journal of Applied Sciences

ISSN Print: 2165-3917
ISSN Online: 2165-3925
www.scirp.org/journal/ojapps
E-mail: ojapps@scirp.org
"Automatic Fault Prediction of Wind Turbine Main Bearing Based on SCADA Data and Artificial Neural Network"
written by Zhenyou Zhang,
published by Open Journal of Applied Sciences, Vol.8 No.6, 2018
has been cited by the following article(s):
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[2] Early fault diagnosis strategy for WT main bearings based on SCADA data and one-class SVM
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[3] Wind Turbine Fault Detection with Multi-module Feature Extraction Network and Adaptive Strategy
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[6] Main bearing fault prognosis in wind turbines based on gated recurrent unit neural networks
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[7] Early fault detection in the main bearing of wind turbines based on Gated Recurrent Unit (GRU) neural networks and SCADA data
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[8] Condition monitoring of wind turbine main bearing based on multivariate time series forecasting
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[11] Usability of SCADA as predictive maintenance for wind turbines
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[12] SSAE‐MLP: Stacked sparse autoencoders‐based multi‐layer perceptron for main bearing temperature prediction of large‐scale wind turbines
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[13] Load Control Aerodynamics in Offshore Wind Turbines
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[14] A review of research works on supervised learning algorithms for SCADA intrusion detection and classification
Mahfouz, S Rimer… - Sustainability, 2021
[15] Fault detection of wind turbine generator bearing using attention-based neural networks and voting-based strategy
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[16] Loads Control Aerodynamic in Offshore Wind Turbines
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[17] ANALISIS PROGNOSTIK TERHADAP KERUSAKAN BANTALAN PADA POROS KECEPATAN TINGGI TURBIN ANGIN MENGGUNAKAN MACHINE LEARNING …
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[18] Data driven case study of a wind turbine main-bearing failure
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[19] A review of wind turbine main bearings: design, operation, modelling, damage mechanisms and fault detection
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[20] Development of a diagnostic and prognostic tool for predictive maintenance in the railcar industry
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[21] Fault Prediction of Wind Turbine Gearbox Based on SCADA Data and Machine Learning
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[22] Performance of a wind turbine blade in sandstorms using a CFD-BEM based neural network
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[23] Fractional‐order nonlinear PID controller based maximum power extraction method for a direct‐driven wind energy system
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[24] Prognosis of Wind Turbine Gearbox Bearing Failures using SCADA and Modeled Data
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[25] Development of Alarm Prediction System for Monitoring Steam Turbine Based on SCADA Data
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[26] A novel fractional‐order nonlinear PID‐based pitch angle control strategy for a PMSG‐based wind energy system
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[27] ПРИМЕНЕНИЕ ИСКУССТВЕННЫХ НЕЙРОННЫХ СЕТЕЙ ПРИ УПРАВЛЕНИИ ЭНЕРГЕТИЧЕСКИМ ОБОРУДОВАНИЕМ. ЧАСТЬ 2. ПРОГНОЗИРОВАНИЕ …
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[28] Fault Diagnosis of Variable Load Bearing Based on Quantum Chaotic Fruit Fly VMD and Variational RVM
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[29] Wind turbine main‐bearing loading and wind field characteristics
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[30] A review of wind turbine main-bearings: design, operation, modelling, damage mechanisms and fault detection
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[31] Deep Learning for Fault Prediction in Offshore Wind Turbines
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[32] A comprehensive classification of services failures based on intentionality and duration of failures
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