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

  • Hugo AndreEmail author
  • Flavien Allemand
  • Ilyes Khelf
  • Adeline Bourdon
  • Didier Remond
Conference paper
Part of the Applied Condition Monitoring book series (ACM, volume 15)

Abstract

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.

In this paper, the attention is focused on the regression process using classical machine learning tools. The difficulty is to design a process able to efficiently estimate the behavior of any indicator depending on the environmental conditions. Indeed, indicator multivariate laws are expected to present extremely varied shapes, and using common linear regression technique can hardly solve this issue. Kernel machines are therefore presented in this paper as an efficient solution to normalize the indicators, and will be shown to ease the health monitoring of the wind turbine shaft line on a practical case based on instantaneous angular speed signals. The example presents the distinctive feature to have a defect visible only specific operating conditions. This operating conditions being unknown a priori, this example clearly enlightens the need of such a regression tool.

Keywords

Parametrization Kernel machine Support vector machine Non-stationary conditions Instantaneous Angular Speed 

Notes

Acknowledgement

The authors would like to thank ENGIE Green for their generous financial support of the work. This work has also been supported by the French Institute Carnot Télécom et Société Numérique.

References

  1. 1.
    Hua XG, Ni YQ, Ko JM, Wong KY (2007) Modeling of temperature frequency correlation using combined principal component analysis and support vector regression technique. J Comput Civ Eng 21:122–135CrossRefGoogle Scholar
  2. 2.
    Sohn H, Dzwonczyk M, Straser EG, Kiremidjian AS, Law KH, Meng T (1999) An experimental study of temperature effect on modal parameters of the Alamosa canyon bridge. Earthq Eng Struct Dyn 28:879–897CrossRefGoogle Scholar
  3. 3.
    Stander CJ, Heyns PS, Schoombie W (2002) Using vibration monitoring for local fault detection on gears operating under fluctuating load conditions. Mech Syst Signal Process 16:1005–1024CrossRefGoogle Scholar
  4. 4.
    Worden K, Sohn H, Farrar CR (2002) Novelty detection in a changing environment: regression and interpolation approaches. J Sound Vib 258:741–761CrossRefGoogle Scholar
  5. 5.
    MacBain J, Timusk M (2009) Fault detection in variable speed machinery: statistical parameterization. J Sound Vib 327:623–646CrossRefGoogle Scholar
  6. 6.
    Hofmann T, Schlkopf B, Smola AJ (2008) Kernel methods in machine learning. Ann Stat 36(3):1171–220MathSciNetCrossRefGoogle Scholar
  7. 7.
    Saunders C, Gammerman A, Vovk V (1998) Ridge regression learning algorithm in dual variables. In: Proceedings of the 15th International Conference on Machine Learning, ICML 1998Google Scholar
  8. 8.
    Aizerman M, Braverman E, Rozonoer L (1964) Theoretical foundations of the potential function method in pattern recognition learning. Autom Remote Control 25:821–837zbMATHGoogle Scholar
  9. 9.
    Vapnik VN (1998) Statistical learning theory. Wiley, Hoboken ISBN 978-0471030034, Edited by Simon HaykinzbMATHGoogle Scholar
  10. 10.
    Cawley GC, Talbot NLC (2002) Heteroscedastic kernel ridge regression. Neurocomputing 57:105–124 New Aspects in Neurocomputing: 10th European Symposium on Artificial Neural Networks 2002CrossRefGoogle Scholar
  11. 11.
    Nabney IT (1999) Efficient training of RBF networks for classification. Technical report NCRG/99/002, Aston University, Birmingham, UKGoogle Scholar
  12. 12.
    Cawley GC, Talbot NLC (2004) Fast exact leave-one-out cross-validation of sparse leastsquare support vector machines. Neural Netw 17:1467–1475CrossRefGoogle Scholar
  13. 13.
    André H, Girardin F, Bourdon A, Antoni J, Rémond D (2014) Precision of the IAS monitoring system based on the elapsed time method in the spectral domain. Mech Syst Signal Process 44(1–2):14–30.  https://doi.org/10.1016/j.ymssp.2013.06.020 ISSN 0888-3270CrossRefGoogle Scholar
  14. 14.
    André H, Bourdon A, Rémond D (2011) On the use of the instantaneous angular speed measurement in non-stationary mechanism monitoring. In: Proceedings of the ASME 2011 International Design Engineering Technical Conferences & 23rd Biennial Conference on Mechanical Vibration and Noise, IDETC/CIE 2011, Washington, DC, USAGoogle Scholar
  15. 15.
    Andre H, Khelf I, Leclere Q (2017) Wind turbine bearing fault detected with IAS combined with harmonic product spectrum. In: COMADEM, University of Central Lancashire, UKGoogle Scholar
  16. 16.
    Andre H, Khelf I, Leclere Q (2017) Harmonic product spectrum revisited and adapted for rotating machine monitoring based on IAS, Surveillance 9, Fs, MaroccoGoogle Scholar
  17. 17.
    Gomez JL, Bourdon A, André H, Rémond D (2016) Modelling deep groove ball bearing localized defects inducing instantaneous angular speed variations. Tribol Int 98:270–281CrossRefGoogle Scholar
  18. 18.
    Gomez J, Khelf I, Bourdon A, Andre H, Alloin L, Rmond D (2017) Analysis of IAS for condition monitoring by means of a simplified wind turbine dynamic model, WCCM, London, UKGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hugo Andre
    • 1
    Email author
  • Flavien Allemand
    • 2
  • Ilyes Khelf
    • 3
    • 4
  • Adeline Bourdon
    • 4
  • Didier Remond
    • 4
  1. 1.Univ Lyon, UJM-St-Etienne, LASPI, EA3059Saint-EtienneFrance
  2. 2.ENGIE GREENNancyFrance
  3. 3.Univ Lyon, INSA Lyon, LVA EA677VilleurbanneFrance
  4. 4.Univ Lyon, INSA Lyon, LaMCoS, CNRS UMR5259LyonFrance

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