Scalable systems for early fault detection in wind turbines: A data driven approach

The world has changed dramatically for wind farm operators and service providers in the last decade. Organizations whose turbine portfolios was counted in 10-100s ten years ago are now managing large scale operation and service programs for fleet sizes well above one thousand turbines. A big challenge such organizations now face is the question of how the massive amount of operational data that are generated by large fleets are effectively managed and how value is gained from the data. A particular hard challenge is the handling of data streams collected from advanced condition monitoring systems. These data are highly complex and typically require expert knowledge to interpret correctly resulting in poor scalability when moving to large Operation and Maintenance (O&M) platforms.

[1]  Mayorkinos Papaelias,et al.  Condition monitoring of wind turbines: Techniques and methods , 2012 .

[2]  Xiyun Yang,et al.  Wind Turbine Generator Condition-Monitoring Using Temperature Trend Analysis , 2012, IEEE Transactions on Sustainable Energy.

[3]  David Infield,et al.  Online wind turbine fault detection through automated SCADA data analysis , 2009 .

[4]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[5]  John V. Ringwood,et al.  A feasibility study into prognostics for the main bearing of a wind turbine , 2012, 2012 IEEE International Conference on Control Applications.

[6]  Mohammad Ali Sadrnia,et al.  Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks , 2014, Comput. Intell. Neurosci..

[7]  Yaoyu Li,et al.  A review of recent advances in wind turbine condition monitoring and fault diagnosis , 2009, 2009 IEEE Power Electronics and Machines in Wind Applications.

[8]  Robert B. Randall,et al.  Vibration-based Condition Monitoring: Industrial, Aerospace and Automotive Applications , 2011 .

[9]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[10]  Kesheng Wang,et al.  Wind turbine fault detection based on SCADA data analysis using ANN , 2014 .

[11]  Meik Schlechtingen,et al.  Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection , 2011 .

[12]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[13]  Xiandong Ma,et al.  Model-based condition monitoring for wind turbines , 2013, 2013 19th International Conference on Automation and Computing.

[14]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[15]  Andrew Kusiak,et al.  Analyzing bearing faults in wind turbines: A data-mining approach , 2012 .

[16]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Researchers , 2007 .