Automatic Discontinuity Classification of Wind-turbine Blades Using A-scan-based Convolutional Neural Network

• Published in: Journal of modern power systems and clean energy Vol. 9; no. 1; pp. 210 - 218
• Main Authors: Jiyeon Choung, Sun Lim, Seung Hwan Lim, Su Chung Chi, Mun Ho Nam
• Format: Journal Article
• Language: English
• Published: IEEE 01.01.2021
• Subjects: A-scan; blade inspection platform; convolutional neural network (CNN); discontinuity; phased-array ultrasonic testing (PAUT); Wind-turbine blade (WTB)
• ISSN: 2196-5420
• DOI: 10.35833/MPCE.2018.000672

Recent development trends in wind power generation have increased the importance of the safe operation of wind-turbine blades (WTBs). To realize this objective, it is essential to inspect WTBs for any defects before they are placed into operation. However, conventional methods of fault inspection in WTBs can be rather difficult to implement, since complex curvatures that characterize the WTB structures must ensure accurate and reliable inspection. Moreover, it is considered useful if inspection results can be objectively and consistently classified and analyzed by an automated system and not by the subjective judgment of an inspector. To address this concern, the construction of a pressure- and shape-adaptive phased-array ultrasonic testing platform, which is controlled by a nanoengine operation system to inspect WTBs for internal defects, has been presented in this paper. An automatic classifier has been designed to detect discontinuities in WTBs by using an A-scan-imaging-based convolutional neural network (CNN). The proposed CNN classifier design demonstrates a classification accuracy of nearly 99%. Results of the study demonstrate that the proposed CNN classifier is capable of automatically classifying the discontinuities of WTB with high accuracy, all of which could be considered as defect candidates.