Characterisation of composite materials for wind turbines using frequency modulated continuous wave sensing

Tang, W., Blanche, J., Mitchell, D., Harper, S. and Flynn, D. (2023) Characterisation of composite materials for wind turbines using frequency modulated continuous wave sensing. Journal of Composites Science, 7(2), 75. (doi: 10.3390/jcs7020075)

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Wind turbine blades (WTBs) are critical sub-systems consisting of composite multi-layer material structures. WTB inspection is a complex and labour intensive process, and failure of it can lead to substantial energy and economic losses to asset owners. In this paper, we proposed a novel non-destructive evaluation method for blade composite materials, which employs Frequency Modulated Continuous Wave (FMCW) radar, robotics and machine learning (ML) analytics. We show that using FMCW raster scan data, our ML algorithms (SVM, BP, Decision Tree and Naïve Bayes) can distinguish different types of composite materials with accuracy of over 97.5%. The best performance is achieved by SVM algorithms, with 94.3% accuracy. Furthermore, the proposed method can also achieve solid results for detecting surface defect: interlaminar porosity with 80% accuracy overall. In particular, the SVM classifier shows highest accuracy of 92.5% to 98.9%. We also show the ability to detect air voids of 1mm differences within the composite material WT structure with 94.1% accuracy performance using SVM, and 84.5% using Naïve Bayes. Lastly, we create a digital twin of the physical composite sample to support the integration and qualitative analysis of the FMCW data with respect to composite sample characteristics. The proposed method explores a new sensing modality for non-contact surface and subsurface for composite materials, and offer insights for developing alternative, more cost-effective inspection and maintenance regimes.

Item Type:Articles
Additional Information:This research was funded by the EPSRC Offshore Robotics for Certification of Assets Hub under Grant EP/R026173/1. The APC was funded by Heriot Watt University.
Glasgow Author(s) Enlighten ID:Blanche, Dr Jamie and Harper, Mr Samuel and Flynn, Professor David and Mitchell, Daniel and Tang, Mr Wenshuo
Authors: Tang, W., Blanche, J., Mitchell, D., Harper, S., and Flynn, D.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Journal of Composites Science
ISSN (Online):2504-477X
Published Online:10 February 2023
Copyright Holders:Copyright © 2023 The Authors
First Published:First published in Journal of Composites Science 7(2): 75
Publisher Policy:Reproduced under a Creative Commons License

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