Dynamic Wind Blade Defect Classification Problem
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URL
Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu |
Master's thesis
Authors
Date
2021-08-23
Department
Major/Subject
Data Science
Mcode
SCI3095
Degree programme
Master's Programme in ICT Innovation
Language
en
Pages
54+10
Series
Abstract
Wind Turbine (WT) blades undergo high operational loads, experience critical environmental conditions, and are susceptible to faults. These factors severely affect the efficiency of WTs, and increase the importance of developing an effective methodology of maintenance of WTs in practice. To detect exterior WT blade faults, visual surface inspection through images is the most common inspection method in practice. With recent advances in deep learning, the available high- resolution blade images acquired in past inspection campaigns represent an enormous potential to use neural networks to automate the analysis of these inspection images. This project explores the applicability of deep learning in the detection of faults in wind turbine (WT) blade images taken from the ground using a telescope. A two-stage approach is proposed to detect WT blade faults, first classifying the images based on the location on the blade, and then detecting the faults. Through collaboration with Xabet Digital Solutions, multiple datasets from available historical wind park inspections are built for training and optimizing. ResNet50 architecture together with transfer learning and data augmentation techniques is used for the localization stage. Successful object detection algorithms such as SSD are tested in the detection stage. Further, an architecture to deploy the proposed approach in a real-world scenario is presented. The design of the models and the deployment is motivated by the available resources and the specific application demands in the practical environment.Description
Supervisor
Pechenizkiy, MykolaThesis advisor
Hämäläinen, WilhelmiinaKeywords
deep learning, wind turbine inspection, object detection, computer vision