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Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers

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  • Arcos Jiménez, Alfredo
  • Gómez Muñoz, Carlos Quiterio
  • García Márquez, Fausto Pedro

Abstract

Dirt and mud on wind turbine blades (WTB) reduce productivity and can generate stops and downtimes. A condition monitoring system based on non-destructive tests by ultrasonic waves was used to analyse it. This paper employs an approach that considers advanced signal processing and machine learning to determine the thickness of the dirt and mud in a WTB. Firstly, the signal is filtered by Wavelet transform. FE and Feature Selection(FS) are employed to remove non-useful data and redundant features. FS selects the number of the most significant terms of the model for fault detection and identification, reducing the dimension of the dataset. Pattern recognition is carried out by the following supervised learning classifiers based on statistical models to calculate and classify the signal depending on the fault: Ensemble Subspace Discriminant; k-Nearest Neighbours; Linear Support Vector Machine; Linear Discriminant Analysis; Decision Trees. Receiver Operating Characteristic analysis is used to evaluate the classifiers. Neighbourhood Component Analysis has been employed in feature selection. Several case studies of mud on the WTB surface have been considered to test and validate the approach. Autoregressive (AR) model and Principal Component Analysis (PCA) have been employed to FE. The results provided by PCA show an improvement on the AR results. The novelty of this work is focused on applying this approach to detect and diagnose mud and dirt in WTB.

Suggested Citation

  • Arcos Jiménez, Alfredo & Gómez Muñoz, Carlos Quiterio & García Márquez, Fausto Pedro, 2019. "Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 2-12.
  • Handle: RePEc:eee:reensy:v:184:y:2019:i:c:p:2-12
    DOI: 10.1016/j.ress.2018.02.013
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    References listed on IDEAS

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    1. Niu, Gang & Yang, Bo-Suk & Pecht, Michael, 2010. "Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance," Reliability Engineering and System Safety, Elsevier, vol. 95(7), pages 786-796.
    2. Papaelias, Mayorkinos & Cheng, Liang & Kogia, Maria & Mohimi, Abbas & Kappatos, Vassilios & Selcuk, Cem & Constantinou, Louis & Muñoz, Carlos Quiterio Gómez & Marquez, Fausto Pedro Garcia & Gan, Tat-H, 2016. "Inspection and Structural Health Monitoring techniques for Concentrated Solar Power plants," Renewable Energy, Elsevier, vol. 85(C), pages 1178-1191.
    3. Alberto Pliego Marugán & Fausto Pedro García Márquez & Benjamin Lev, 2017. "Optimal decision-making via binary decision diagrams for investments under a risky environment," International Journal of Production Research, Taylor & Francis Journals, vol. 55(18), pages 5271-5286, September.
    4. Dong, Wenbin & Moan, Torgeir & Gao, Zhen, 2012. "Fatigue reliability analysis of the jacket support structure for offshore wind turbine considering the effect of corrosion and inspection," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 11-27.
    5. Rajpal, P.S. & Shishodia, K.S. & Sekhon, G.S., 2006. "An artificial neural network for modeling reliability, availability and maintainability of a repairable system," Reliability Engineering and System Safety, Elsevier, vol. 91(7), pages 809-819.
    6. Martinez-Luengo, Maria & Kolios, Athanasios & Wang, Lin, 2016. "Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 91-105.
    7. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    8. Fausto Pedro García Márquez & Alberto Pliego Marugán & Jesús María Pinar Pérez & Stuart Hillmansen & Mayorkinos Papaelias, 2017. "Optimal Dynamic Analysis of Electrical/Electronic Components in Wind Turbines," Energies, MDPI, vol. 10(8), pages 1-19, July.
    9. María Pérez-Ortiz & Silvia Jiménez-Fernández & Pedro A. Gutiérrez & Enrique Alexandre & César Hervás-Martínez & Sancho Salcedo-Sanz, 2016. "A Review of Classification Problems and Algorithms in Renewable Energy Applications," Energies, MDPI, vol. 9(8), pages 1-27, August.
    10. Ruiz de la Hermosa González-Carrato, Raúl & García Márquez, Fausto Pedro & Dimlaye, Vichaar, 2015. "Maintenance management of wind turbines structures via MFCs and wavelet transforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 472-482.
    11. Hameed, Z. & Hong, Y.S. & Cho, Y.M. & Ahn, S.H. & Song, C.K., 2009. "Condition monitoring and fault detection of wind turbines and related algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 1-39, January.
    12. Alberto Pliego Marugán & Fausto Pedro García Márquez & Jesús María Pinar Pérez, 2016. "Optimal Maintenance Management of Offshore Wind Farms," Energies, MDPI, vol. 9(1), pages 1-20, January.
    13. Fausto Pedro García Márquez & Diego J. Pedregal & Clive Roberts, 2015. "New methods for the condition monitoring of level crossings," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(5), pages 878-884, April.
    14. Liu, Jie & Zio, Enrico, 2017. "System dynamic reliability assessment and failure prognostics," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 21-36.
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