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

作     者：Jiyeon Choung Sun Lim Seung Hwan Lim Su Chung Chi Mun Ho Nam 

作者机构：Korea Electronics Technology Institute401-402Bucheon Technopark655Pyeongcheon-roWonmi-guBucheon-siGyeonggi-do 14502Korea Sam Yong Inspection Engineering Co.Ltd. 

出 版 物：《Journal of Modern Power Systems and Clean Energy》 (现代电力系统与清洁能源学报（英文）)

年 卷 期：2021年第9卷第1期

页      面：210-218页

核心收录：

学科分类：12[管理学] 080801[工学-电机与电器] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) “Development of System for Damage Detection on the Outer of Fibrous Composite Blade for Wind Power Plants In-process and In-service Inspection”(No. 20153030024070) funded by the Ministry of Trade,Industry,and Energy (MOTIE),Korea 

主　　题：Wind-turbine blade(WTB) blade inspection platform convolutional neural network(CNN) discontinuity phased-array ultrasonic testing(PAUT) A-scan 

摘      要：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-scanimaging-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.