Deep learning with knowledge transfer for explainable anomaly prediction in wind turbines
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[1] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[2] Dongxiang Jiang,et al. Fault diagnosis of wind turbine based on Long Short-term memory networks , 2019, Renewable Energy.
[3] Shuting Wan,et al. Effects of Yaw Error on Wind Turbine Running Characteristics Based on the Equivalent Wind Speed Model , 2015 .
[4] Wei Qiao,et al. A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part II: Signals and Signal Processing Methods , 2015, IEEE Transactions on Industrial Electronics.
[5] Andrés Bustillo,et al. An SVM-Based Solution for Fault Detection in Wind Turbines , 2015, Sensors.
[6] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[7] Liping Sun,et al. Condition based maintenance optimization for offshore wind turbine considering opportunities based on neural network approach , 2018 .
[8] Dahai Zhang,et al. A data-driven approach for fault detection of offshore wind turbines using random forests , 2017, IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society.
[9] Qing He,et al. A SCADA data based anomaly detection method for wind turbines , 2016, 2016 China International Conference on Electricity Distribution (CICED).
[10] Hua Han,et al. Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform , 2019, Applied Sciences.
[11] B Stephen,et al. A Copula Model of Wind Turbine Performance , 2011, IEEE Transactions on Power Systems.
[12] Kathryn E. Johnson,et al. Wind turbine fault detection and fault tolerant control - An enhanced benchmark challenge , 2013, 2013 American Control Conference.
[13] Shuo Yang,et al. Image-based damage recognition of wind turbine blades , 2017, 2017 2nd International Conference on Advanced Robotics and Mechatronics (ICARM).
[14] P Dalhoff,et al. Yawing characteristics during slippage of the nacelle of a multi MW wind turbine , 2016 .
[15] Simon Hogg,et al. Wind energy: UK experiences and offshore operational challenges , 2015 .
[16] Huan Long,et al. Wind Turbine Gearbox Failure Identification With Deep Neural Networks , 2017, IEEE Transactions on Industrial Informatics.
[17] Yoshua Bengio,et al. Practical Recommendations for Gradient-Based Training of Deep Architectures , 2012, Neural Networks: Tricks of the Trade.
[18] David Infield,et al. SCADA‐based wind turbine anomaly detection using Gaussian process models for wind turbine condition monitoring purposes , 2018, IET Renewable Power Generation.
[19] Sepp Hochreiter,et al. The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions , 1998, Int. J. Uncertain. Fuzziness Knowl. Based Syst..
[20] Dongsheng Li,et al. Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data , 2017 .
[21] Antonio Messineo,et al. Monitoring of wind farms’ power curves using machine learning techniques , 2012 .
[22] David McMillan,et al. Availability, operation and maintenance costs of offshore wind turbines with different drive train configurations , 2017 .
[23] F. Trivellato,et al. The ideal power curve of small wind turbines from field data , 2012 .
[24] B. Stephen,et al. Wind Turbine Condition Assessment Through Power Curve Copula Modeling , 2012, IEEE Transactions on Sustainable Energy.
[25] Yuyun Zeng,et al. Real-time condition monitoring and fault detection of components based on machine-learning reconstruction model , 2019, Renewable Energy.
[26] Emmanuel Bacry,et al. tick: a Python Library for Statistical Learning, with an emphasis on Hawkes Processes and Time-Dependent Models , 2017, J. Mach. Learn. Res..
[27] Hexu Sun,et al. Health Status Assessment for Wind Turbine with Recurrent Neural Networks , 2018, Mathematical Problems in Engineering.
[28] Keith Worden,et al. A Performance Monitoring Approach for the Novel Lillgrund Offshore Wind Farm , 2015, IEEE Transactions on Industrial Electronics.
[29] Inderjit S. Dhillon,et al. Non-exhaustive, Overlapping k-means , 2015, SDM.
[30] David Infield,et al. Online wind turbine fault detection through automated SCADA data analysis , 2009 .
[31] Keith Worden,et al. Fault diagnosis of wind turbine structures using decision tree learning algorithms with big data , 2018, Safety and Reliability – Safe Societies in a Changing World.
[32] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[33] Emmanuel Sirimal Silva,et al. A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts , 2015 .
[34] Junjie Wu,et al. The Uniform Effect of K-means Clustering , 2012 .
[35] Yoshua Bengio,et al. The problem of learning long-term dependencies in recurrent networks , 1993, IEEE International Conference on Neural Networks.
[36] H. Robbins. A Stochastic Approximation Method , 1951 .