Wind Turbine Surface Damage Detection by Deep Learning Aided Drone Inspection Analysis

Shihavuddin Asm, Xiao Chen, Vladimir Fedorov, Anders Nymark Christensen, Nicolai Andre Brogaard Riis, Kim Branner, Anders Bjorholm Dahl and Rasmus Reinhold Paulsen
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Shihavuddin Asm: Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), 2800 Lyngby, Denmark
Xiao Chen: Department of Wind Energy, Technical University of Denmark (DTU), 4000 Roskilde, Denmark
Vladimir Fedorov: EasyInspect ApS, 2605 Brøndby, Denmark
Anders Nymark Christensen: Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), 2800 Lyngby, Denmark
Nicolai Andre Brogaard Riis: Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), 2800 Lyngby, Denmark
Kim Branner: Department of Wind Energy, Technical University of Denmark (DTU), 4000 Roskilde, Denmark
Anders Bjorholm Dahl: Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), 2800 Lyngby, Denmark
Rasmus Reinhold Paulsen: Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), 2800 Lyngby, Denmark

Energies, 2019, vol. 12, issue 4, 1-15

Abstract: Timely detection of surface damages on wind turbine blades is imperative for minimizing downtime and avoiding possible catastrophic structural failures. With recent advances in drone technology, a large number of high-resolution images of wind turbines are routinely acquired and subsequently analyzed by experts to identify imminent damages. Automated analysis of these inspection images with the help of machine learning algorithms can reduce the inspection cost. In this work, we develop a deep learning-based automated damage suggestion system for subsequent analysis of drone inspection images. Experimental results demonstrate that the proposed approach can achieve almost human-level precision in terms of suggested damage location and types on wind turbine blades. We further demonstrate that for relatively small training sets, advanced data augmentation during deep learning training can better generalize the trained model, providing a significant gain in precision.

Keywords: wind energy; rotor blade; wind turbine; drone inspection; damage detection; deep learning; Convolutional Neural Network (CNN) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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