Leveraging Vision Transformers for Detecting Porosity in Wind Energy Composites

authored by: A. W. Khan, C. Balzani
Abstract: The structural robustness and operational efficiency of wind turbine rotor blades are crucial for the overall effectiveness of wind energy systems, often constructed with fiber-reinforced polymers (FRPs) and adhesives. However, porosity within these materials poses a significant threat, weakening structural strength and effectiveness. Air pockets lead to stress concentration points, reducing load-carrying capacity and elevating the risk of blade failure, especially under dynamic wind loads. Manual detection of these air pockets is laborious, necessitating automated inspection techniques. Advanced imaging technologies, such as computed tomography (CT) scanning and deep learning, hold promise for identifying and quantifying porosity in FRPs and adhesives, reducing labor while enhancing accuracy. The study introduces a transformer-based model for porosity detection, departing from convolution-based methods, emphasizing the incorporation of global context throughout the network. Leveraging Vision Transformer (ViT) framework advances, the model is applied to porosity segmentation in wind energy blades, showing promising results with limited datasets. The prospect of using larger datasets suggests potential for a versatile solution in segmenting porosity or voids in various wind energy blade composites, including adhesives.
Organisation(s): Institute of Wind Energy Systems
Type: Conference article
Journal: Journal of Physics: Conference Series
Volume: 2767
No. of pages: 10
ISSN: 1742-6588
Publication date: 2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Renewable Energy, Sustainability and the Environment, Computational Mechanics, Mechanics of Materials
Research Area (based on ÖFOS 2012): Computational engineering, Renewable energy, Micromechanics, Computational engineering
Sustainable Development Goals: SDG 7 - Affordable and Clean Energy
Electronic version(s): https://doi.org/10.1088/1742-6596/2767/5/052044 (Access: Open)