Characterisation of Composite Materials for Wind Turbines Using Frequency Modulated Continuous Wave Sensing
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-destruct...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Journal of Composites Science |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-477X/7/2/75 |
| _version_ | 1797620102410010624 |
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| author | Wenshuo Tang Jamie Blanche Daniel Mitchell Samuel Harper David Flynn |
| author_facet | Wenshuo Tang Jamie Blanche Daniel Mitchell Samuel Harper David Flynn |
| author_sort | Wenshuo Tang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T08:36:58Z |
| format | Article |
| id | doaj.art-182783e834404e8580f326cd27bbe0e7 |
| institution | Directory Open Access Journal |
| issn | 2504-477X |
| language | English |
| last_indexed | 2024-03-11T08:36:58Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Composites Science |
| spelling | doaj.art-182783e834404e8580f326cd27bbe0e72023-11-16T21:23:47ZengMDPI AGJournal of Composites Science2504-477X2023-02-01727510.3390/jcs7020075Characterisation of Composite Materials for Wind Turbines Using Frequency Modulated Continuous Wave SensingWenshuo Tang0Jamie Blanche1Daniel Mitchell2Samuel Harper3David Flynn4James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UKJames Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UKJames Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UKJames Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UKJames Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UKWind 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.https://www.mdpi.com/2504-477X/7/2/75non-destructive evaluationFMCWsensingcomposite materialswind turbine blades |
| spellingShingle | Wenshuo Tang Jamie Blanche Daniel Mitchell Samuel Harper David Flynn Characterisation of Composite Materials for Wind Turbines Using Frequency Modulated Continuous Wave Sensing Journal of Composites Science non-destructive evaluation FMCW sensing composite materials wind turbine blades |
| title | Characterisation of Composite Materials for Wind Turbines Using Frequency Modulated Continuous Wave Sensing |
| title_full | Characterisation of Composite Materials for Wind Turbines Using Frequency Modulated Continuous Wave Sensing |
| title_fullStr | Characterisation of Composite Materials for Wind Turbines Using Frequency Modulated Continuous Wave Sensing |
| title_full_unstemmed | Characterisation of Composite Materials for Wind Turbines Using Frequency Modulated Continuous Wave Sensing |
| title_short | Characterisation of Composite Materials for Wind Turbines Using Frequency Modulated Continuous Wave Sensing |
| title_sort | characterisation of composite materials for wind turbines using frequency modulated continuous wave sensing |
| topic | non-destructive evaluation FMCW sensing composite materials wind turbine blades |
| url | https://www.mdpi.com/2504-477X/7/2/75 |
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