基于卷积神经网络焊管缺陷分类识别  

Classification and recognition of welded pipe defects based on convolutional neural networks

作　　者：云晗 付红红 王宗仁 侯怀书 YUN Han;FU Honghong;WANG Zongren;HOU Huaishu(Hainan Special Equipment Inspection Institute,Haikou 570203,China;School of Mechanical Engineering,Shanghai Institute of Technology,Shanghai 201418,China)

机构地区：[1]海南省检验检测研究院特种设备检验所,海口570203 [2]上海应用技术大学机械工程学院,上海201418

出　　处：《理化检验（物理分册）》2024年第7期35-39,共5页Physical Testing and Chemical Analysis(Part A:Physical Testing)

摘　　要：针对常规涡流检测阻抗平面分析法无法对不锈钢焊管缺陷种类进行识别的问题,提出了一种基于涡流检测技术结合机器学习对不锈钢焊管缺陷进行分类识别的有效方法。首先对提取到的涡流信号进行短时傅里叶变换,将原始涡流信号转换成二维时频图;再将二维时频图输入到VGG-16和GoogLeNet两种神经网络训练模型的输入层中。结果表明:VGG-16和GoogLeNet两种神经网络训练模型能成功识别不锈管焊管的缺陷,且VGG-16模型在0.01的学习率下的整体分类精度高于GoogLeNet模型。Aiming at the problem that conventional eddy current testing impedance plane analysis method could not identify the types of defects in stainless steel welded pipes,an effective method based on eddy current testing technology combined with machine learning was proposed to classify and identify defects in stainless steel welded pipes.Firstly,performed a short-time Fourier transform on the extracted eddy current signal to convert the original eddy current signal into a two-dimensional time-frequency map.Then input the two-dimensional time-frequency map into the input layer of the VGG-16 and GoogLeNet neural network training models.The results show that the VGG-16 and GoogLeNet neural network training models could successfully identify defects in stainless steel welded pipes,and the overall classification accuracy of the VGG-16 model was higher than that of the GoogLeNet model at a learni ng rate of 0.01.

关 键 词：不锈钢焊管 涡流检测 分类识别 神经网络 缺陷 

分 类 号：TB31[一般工业技术—材料科学与工程] TG115.2[金属学及工艺—物理冶金]