理化检验-物理分册2024，Vol.60Issue(7)：35-39,5.DOI:10.11973/lhjy-wl240085
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
云晗 1付红红 2王宗仁 2侯怀书2
作者信息
- 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
- 折叠
摘要
Abstract
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 learning 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Key words
stainless steel welded pipe/eddy current testing/classification and recognition/neural network/defect分类
通用工业技术引用本文复制引用
云晗,付红红,王宗仁,侯怀书..基于卷积神经网络焊管缺陷分类识别[J].理化检验-物理分册,2024,60(7):35-39,5.
