Normalization Process Based on Kernel Ridge Regression Applied on Wind Turbine IAS Monitoring

Variable speed wind turbines use the available wind resource more efficiently than a fixed speed wind turbine, especially during light wind conditions. This enhancement forces the monitoring methods to deal with these large variations in speed and torque, since the conditions are seldom if ever stationary. The unsteady behavior of these wind turbines is also a difficulty in terms of long term diagnostic, since the comparison of successive measurements is usually performed under the same operating conditions. Normalization of the indicators according to well-chosen variables might bring a valuable tool regarding several aspects.

[1]  Gavin C. Cawley,et al.  Heteroscedastic kernel ridge regression , 2004, Neurocomputing.

[2]  Hoon Sohn,et al.  An experimental study of temperature effect on modal parameters of the Alamosa Canyon Bridge , 1999 .

[3]  Alex Smola,et al.  Kernel methods in machine learning , 2007, math/0701907.

[4]  Gavin C. Cawley,et al.  Fast exact leave-one-out cross-validation of sparse least-squares support vector machines , 2004, Neural Networks.

[5]  M. Aizerman,et al.  Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning , 1964 .

[6]  Alexander Gammerman,et al.  Ridge Regression Learning Algorithm in Dual Variables , 1998, ICML.

[7]  Hugo André,et al.  Modelling deep groove ball bearing localized defects inducing instantaneous angular speed variations , 2016 .

[8]  Quentin Leclere,et al.  Wind Turbine Bearing fault detected with IAS combined with Harmonic Product Spectrum , 2017 .

[9]  I. Nabney.  Efficient training of RBF networks for classification , 1999 .

[10]  Yi-Qing Ni,et al.  Modeling of Temperature–Frequency Correlation Using Combined Principal Component Analysis and Support Vector Regression Technique , 2007 .

[11]  Jérôme Antoni,et al.  Precision of the IAS monitoring system based on the elapsed time method in the spectral domain , 2012 .

[12]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[13]  P. S. Heyns,et al.  USING VIBRATION MONITORING FOR LOCAL FAULT DETECTION ON GEARS OPERATING UNDER FLUCTUATING LOAD CONDITIONS , 2002 .

[14]  Didier Remond,et al.  On the Use of the Instantaneous Angular Speed Measurement in Non Stationary Mechanism Monitoring , 2011 .

[15]  Markus Timusk,et al.  Fault detection in variable speed machinery: Statistical parameterization , 2009 .

[16]  Charles R. Farrar,et al.  Novelty detection in a changing environment: Regression and interpolation approaches , 2002 .