Labelling Drifts in a Fault Detection System for Wind Turbine Maintenance

  • Iñigo MartinezEmail author
  • Elisabeth Viles
  • Iñaki Cabrejas
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 798)


A failure detection system is the first step towards predictive maintenance strategies. A popular data-driven method to detect incipient failures and anomalies is the training of normal behaviour models by applying a machine learning technique like feed-forward neural networks (FFNN) or extreme learning machines (ELM). However, the performance of any of these modelling techniques can be deteriorated by the unexpected rise of non-stationarities in the dynamic environment in which industrial assets operate. This unpredictable statistical change in the measured variable is known as concept drift. In this article a wind turbine maintenance case is presented, where non-stationarities of various kinds can happen unexpectedly. Such concept drift events are desired to be detected by means of statistical detectors and window-based approaches. However, in real complex systems, concept drifts are not as clear and evident as in artificially generated datasets. In order to evaluate the effectiveness of current drift detectors and also to design an appropriate novel technique for this specific industrial application, it is essential to dispose beforehand of a characterization of the existent drifts. Under the lack of information in this regard, a methodology for labelling concept drift events in the lifetime of wind turbines is proposed. This methodology will facilitate the creation of a drift database that will serve both as a training ground for concept drift detectors and as a valuable information to enhance the knowledge about maintenance of complex systems.


Failure detection Predictive maintenance Concept drift Supervised learning Neural networks Extreme learning machine Wind turbine Expert labelling 



This research has been supported by NEM Solutions, a technology-based company focused that provides intelligent maintenance of complex systems to O&M businesses.


  1. 1.
    Jardine, A.K., Lin, D., Banjevic, D.: A review on machinery diagnostics and prognostics implementing condition-based maintenance (2006). Scholar
  2. 2.
    Heng, A., Zhang, S., Tan, A.C.C., Mathew, J.: Mech. Syst. Signal Process. 23(3), 724 (2009). Scholar
  3. 3.
    Zio, E., Kadry, S.: Diagnostics and prognostics of engineering systems: methods and techniques, pp. 333–356 (2012).>
  4. 4.
    Vichare, N.M., Pecht, M.G.: IEEE Trans. Compon. Packag. Technol. 29(1), 222 (2006). Scholar
  5. 5.
    Cheng, S., Azarian, M.H., Pecht, M.G.: Sensor systems for prognostics and health management (2010). Scholar
  6. 6.
    Salfner, F., Lenk, M., Malek, M.: ACM Comput. Surv. 42(3), 1 (2010). Scholar
  7. 7.
    Yang, W., Court, R., Jiang, J.: Renew. Energy 53, 365 (2013). Scholar
  8. 8.
    Sheng, S., Veers, P.: Machinery Failure Prevention Technology (MFPT): The Applied Systems Health Management Conference 2011, vol. 2, p. 5, October 2011Google Scholar
  9. 9.
    Al-Turki, U.M., Ayar, T., Yilbas, B.S., Sahin, A.Z.: SpringerBriefs in Applied Sciences and Technology, pp. i–iv (2014). Scholar
  10. 10.
    Kubat, M.: Knowl. Eng. Rev. 13(4), S0269888998214044 (1999). Scholar
  11. 11.
    Liu, Z., Gao, W., Wan, Y.H., Muljadi, E.: IEEE Energy Conversion Congress and Exposition (ECCE) (August), 3154 (2012).
  12. 12.
    Pelletier, F., Masson, C., Tahan, A.: Renew. Energy 89, 207 (2016). Scholar
  13. 13.
    Qian, P., Ma, X., Wang, Y.: Autom. Comput. (ICAC) 11 (2015).
  14. 14.
    Qian, P., Ma, X., Zhang, D.: Energies 10(10), 1583 (2017). Scholar
  15. 15.
    Saavedra-Moreno, B., Salcedo-Sanz, S., Carro-Calvo, L., Gascón-Moreno, J., Jiménez-Fernández, S., Prieto, L.: J. Wind Eng. Ind. Aerodyn. 116, 49 (2013). Scholar
  16. 16.
    Wan, C., Xu, Z., Pinson, P., Dong, Z.Y., Wong, K.P.: IEEE Trans. Power Syst. 29(3), 1033 (2014). Scholar
  17. 17.
    Garcia, M.C., Sanz-Bobi, M.A., del Pico, J.: Comput. Ind. 57(6), 552 (2006). Scholar
  18. 18.
    Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: ACM Comput. Surv. 46(4), 1 (2014). Scholar
  19. 19.
    Žliobaite, I.: International Conference on Machine Learning, pp. 1009–1017 (2010).
  20. 20.
    Webb, G.I., Hyde, R., Cao, H., Nguyen, H.L., Petitjean, F.: Data Mining Knowl. Discov. 30(4), 964 (2016). Scholar
  21. 21.
    Tsymbal, A.: Computer Science Department, Trinity College Dublin 4(C), 2004 (2004).
  22. 22.
    Hoens, T.R., Polikar, R., Chawla, N.V.: Prog. Artif. Intell. 1(1), 89 (2012). Scholar
  23. 23.
    Ditzler, G., Roveri, M., Alippi, C., Polikar, R.: Learning in nonstationary environments: a survey (2015). Scholar
  24. 24.
    Krawczyk, B., Minku, L.L., Gama, J., Stefanowski, J., Woźniak, M.: Inf. Fusion 37, 132 (2017). Scholar
  25. 25.
    Mouret, J.B., Tonelli, P.: Stud. Comput. Intell. 557, 251 (2015). Scholar
  26. 26.
    Gonçalves, P.M., De Carvalho Santos, S.G.T., Barros, R.S.M., Vieira, D.C.L.: A comparative study on concept drift detectors (2014). Scholar
  27. 27.
    Sobolewski, P., Woźniak, M.: Adv. Intell. Syst. Comput. 226, 329 (2013). Scholar
  28. 28.
    Sebastião, R., Gama, J.: 14th Portuguese Conference on Artificial Intelligence, pp. 353–364 (2009).
  29. 29.
    Santos, S., Barros, R., Gonçalves, P.: Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, vol. 2016, pp. 1077–1084, January 2016.
  30. 30.
    Pears, R., Sakthithasan, S., Koh, Y.S.: Mach. Learn. 97(3), 259 (2014). Scholar
  31. 31.
    Ross, G.J., Adams, N.M., Tasoulis, D.K., Hand, D.J.: Pattern Recogn. Lett. 33(2), 191 (2012). Scholar
  32. 32.
    Bangalore, P., Patriksson, M.: Renew. Energy 115, 521 (2018). Scholar
  33. 33.
    Huang, G.B., et al.: Neurocomputing 70(1–3), 489 (2006). Scholar
  34. 34.
    Lan, Y., Soh, Y.C., Huang, G.B.: Ensemble of online sequential extreme learning machine (2009). Scholar
  35. 35.
    Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: J. Mach. Learn. Res. 11, 1601 (2010).
  36. 36.
    Maciel, B.I.F., Santos, S.G.T.C., Barros, R.S.M.: Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, vol. 2016, pp. 1061–1068, January 2016.
  37. 37.
    Sobolewski, P., Woźniak, M.: J. Univ. Comput. Sci. 19(4), 462 (2013)Google Scholar
  38. 38.
    Woźniak, M., Ksieniewicz, P., Kasprzak, A., Puchała, K., Ryba, P.: Advances in Intelligent Systems and Computing, vol. 525, pp. 27–34 (2017). Scholar
  39. 39.
    Du, L., Song, Q., Zhu, L., Zhu, X.: Comput. J. 58(3), 457 (2015). Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Iñigo Martinez
    • 1
    Email author
  • Elisabeth Viles
    • 2
  • Iñaki Cabrejas
    • 1
  1. 1.NEM SolutionsSan SebastianSpain
  2. 2.University of Navarra - TecnunSan SebastianSpain

Personalised recommendations