基于深度学习与多频电磁阵列检测的金属板材表面和内部缺陷识别方法  

A recognition method for sheet metal surface and internal defects based on deep learning and multi-frequency electromagnetic array detection

作　　者：李秀艳[1] 夏琦琦 王琦[1] 张荣华[2] 汪剑鸣[1] 王化祥 Li Xiuyan;Xia Qiqi;Wang Qi;Zhang Ronghua;Wang Jianming;Wang Huaxiang(School of Electronics and Information Engineering,Tianjin Polytechnic University,Tianjin 300387,China;School of Electrical Engineering and Automation,Tianjin Polytechnic University,Tianjin 300387,China;School of Electrical Automation and Information Engineering,Tianjin University»Tianjin 300372,China)

机构地区：[1]天津工业大学电子与信息工程学院,天津300387 [2]天津工业大学电气工程与自动化学院,天津300387 [3]天津大学电气自动化与信息工程学院,天津300372

出　　处：《电子测量技术》2021年第4期118-125,共8页Electronic Measurement Technology

基　　金：国家自然科学基金(61872269,61903273);天津市自然科学基金(18JCYBJC85300)项目资助。

摘　　要：针对传统电磁检测技术对金属内部缺陷检测能力不足问题,提出一种基于深度学习与电磁检测的金属板材表面和内部缺陷识别方法。实验建立了不同深度、位置、数量的9类表面与内部典型气隙缺陷模型,验证了多频检测可行性,考虑阵列传感器激励电压与感应电压关系,引入保持平衡性的数据采集方法扩充数据集并预处理;构建DNN与CNN深度学习网络对各类检测数据特征训练,并由实验效果选取合适参数的网络。实验结果表明,应用DNN或CNN的电磁检测,可实现9类金属板材表面与内部缺陷识别,准确率为90%以上,解决电磁检测数据分类困难问题;对比DNN、CNN效果,DNN分类训练速度更快且更高效。In practice, traditional metal electromagnetic detection technology is insufficient to detect sheet metal internal defects. In order to solve this problem, a method of recognizing surface and internal defects based on deep learning and electromagnetic detection is proposed in this paper. In our experiment, nine types of surface and internal air-gap defect models with different depths, positions and quantities are built. The feasibility of multi-frequency detection is verified. The relation between excitation voltage and induction voltage detected by array electromagnetic sensor is concerned. Data balance acquisition method is introduced to expand data set before data preprocessing. In order to get the characteristics of measurement data, DNN and CNN are constructed. The appropriate network with suitable parameters is chosen according to the recognition results. The experimental results show that 9 kinds of air-gap defects with different quantities, positions and depths can be recognized by electromagnetic detection applied with DNN or CNN. The recognition accuracy is over 90%. Compared with CNN, DNN is faster and more efficient to classify.

关 键 词：深度学习 电磁检测 内部缺陷 分类 

分 类 号：TP274.3[自动化与计算机技术—检测技术与自动化装置]