Identification and Localization of Wind Turbine Blade Faults Using Deep Learning
Source abstract
Abstract
This study addresses the challenges inherent in the maintenance and inspection of wind turbines through the application of deep learning methodologies for fault detection on Wind Turbine Blades (WTBs). Specifically, this research focuses on defect detection on the blades of small-scale WTBs due to the unavailability of commercial wind turbines. This research compared popular object localization architectures, YOLO and Mask R-CNN, to identify the most effective model to detect common WTB defects, including cracks, holes, and erosion. YOLOv9 C emerged as the most effective model, with the highest scores of mAP50 and mAP50-95 of 0.849 and 0.539, respectively. Modifications to Mask R-CNN, specifically integrating a ResNet18-FPN network, reduced computational complexity by 32 layers and achieved a mAP50 of 0.8415. The findings highlight the potential of deep learning and computer vision in improving WTB fault analysis and inspection.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Advanced Neural Network Applications · Non-Destructive Testing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Machine Learning and Drone Lab, Electrical and Computer Engineering Department, Utah Valley University, Orem, UT 84097, USA
Funding
Office of the Commissioner of Utah System of Higher Education (USHE)—Deep Technology Initiative
References (35)
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