A Condition Monitoring System for Blades of Wind Turbine Maintenance Management

  • Isaac Segovia Ramirez
  • Carlos Quiterio Gómez Muñoz
  • Fausto Pedro García Marquez
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 502)

Abstract

Wind energy is one of the most competitive and efficient renewable energy. It requires an efficient management system to reduce costs, predict failures and increase the production. The main objective of this paper is to design the appropriate tests and develop a condition monitoring system (CMS) to display the surface temperature of any body state using infrared radiation. The data obtained from this system lead to identify the state of the surface. The CMS is used for maintenance management of wind turbines because it is necessary an effective system to display the surface temperature to reduce the energy losses. This paper analyses numerous scenarios and experiments on different surfaces in preparation for actual measurements of blade surfaces.

Keywords

Maintenance management Fault detection and diagnosis Infrared sensors Non-destructive tests Wind energy 

Notes

Acknowledgments

The work reported herewith has been financially supported by the Spanish Ministerio de Economła y Competitividad, under Research Grant DPI2015-67264, and the FP7 Research project with reference FP-7-Energy-2012-TREN-1:322430.

References

  1. 1.
    Blonquist J, Tanner B, Bugbee B (2009) Evaluation of measurement accuracy and comparison of two new and three traditional net radiometers. Agric For Meteorol 149(10):1709–1721CrossRefGoogle Scholar
  2. 2.
    de la Hermosa González RR, Márquez FPG et al (2014) Pattern recognition by wavelet transforms using macro fibre composites transducers. Mech Syst Signal Process 48(1):339–350Google Scholar
  3. 3.
    de la Hermosa González RR, Márquez FPG et al (2015) Maintenance management of wind turbines structures via mfcs and wavelet transforms. Renew Sustain Energy Rev 48:472–482CrossRefGoogle Scholar
  4. 4.
    García Márquez FP, García-Pardo IP (2010) Principal component analysis applied to filtered signals for maintenance management. Qual Reliab Eng Int 26(6):523–527CrossRefGoogle Scholar
  5. 5.
    Gómez Muñoz CQ, García Márquez FP (2016) A new fault location approach for acoustic emission techniques in wind turbines. Energies 9(1):40CrossRefGoogle Scholar
  6. 6.
    Gómez Muñoz CQ, De la Hermosa Gonzalez-Carrato R et al (2014) A novel approach to fault detection and diagnosis on wind turbines. Glob Nest J 16:1029–1037Google Scholar
  7. 7.
    Gómez CQ, Villegas MA et al (2015) Big data and web intelligence for condition monitoring: a case study on wind turbines. Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence; Information Science Reference, IGI Global: Hershey, PA, USAGoogle Scholar
  8. 8.
    Ibarra-Castanedo C, Bendada A, Maldague X (2011) In infrared vision applications for the nondestructive testing of materials. In: 5th Pan American conference for NDT, Cancun, MexicoGoogle Scholar
  9. 9.
    Klassen S, Ritchie G et al (2003) Real-time imaging of ground cover: relationships with radiation capture, canopy photosynthesis, and daily growth rate. Digital imaging and spectral techniques: applications to precision agriculture and crop physiology (digitalimaginga):3–14Google Scholar
  10. 10.
    Marquez FPG (2006) An approach to remote condition monitoring systems management. In: Railway Condition Monitoring, 2006. The Institution of Engineering and Technology International Conference on, IET, pp 156–160Google Scholar
  11. 11.
    Márquez FPG, Pedregal DJ, Roberts C (2015) New methods for the condition monitoring of level crossings. Int J Syst Sci 46(5):878–884CrossRefGoogle Scholar
  12. 12.
    Márquez FPG, Pérez JMP et al (2016) Identification of critical components of wind turbines using fta over the time. Renew Energy 87:869–883CrossRefGoogle Scholar
  13. 13.
    Muñoz CQG, Marquez FPG et al (2015) A new condition monitoring approach for maintenance management in concentrate solar plants. In: Proceedings of the Ninth International Conference on Management Science and Engineering Management, Springer, pp 999–1008Google Scholar
  14. 14.
    Papaelias M, Cheng L et al (2016) Inspection and structural health monitoring techniques for concentrated solar power plants. Renew Energy 85:1178–1191CrossRefGoogle Scholar
  15. 15.
    Pliego Marugán A, García Márquez FP, Pinar Pérez JM (2016) Optimal maintenance management of offshore wind farms. Energies 9(1):46Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  • Isaac Segovia Ramirez
    • 1
  • Carlos Quiterio Gómez Muñoz
    • 1
  • Fausto Pedro García Marquez
    • 1
  1. 1.Ingenium Research GroupCastilla-La Mancha UniversityCiudad RealSpain

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