preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints201901.0281.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 24 January 2019 / Approved: 28 January 2019 / Online: 28 January 2019 (15:50:04 CET)

Timely detection of surface damages on wind turbine blades is imperative for minimising 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 analysed by experts to identify imminent damages. Automated analysis of these inspection images with the help of machine learning algorithms can reduce the inspection cost, thereby reducing the overall maintenance cost arising from the manual labour involved. 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 could 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 generalise the trained model providing a significant gain in precision.

Wind Energy; Wind Turbine; Drone Inspection; Damage Detection; Deep Learning; Convolutional Neural Network (CNN)

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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