衬胶管道脱粘缺陷超声检测与识别方法研究  

Research on ultrasonic detection and recognition methods for debonding defects in rubber-lined pipes

作　　者：魏丞耀 王雪梅[1] 倪文波[1] 陈果 钟昊 Wei Chengyao;Wang Xuemei;Ni Wenbo;Chen Guo;Zhong Hao(School of Mechanical Engineering,Southwest Jiaotong University,Chengdu 610031,China)

机构地区：[1]西南交通大学机械工程学院,成都610031

出　　处：《电子测量技术》2024年第1期23-30,共8页Electronic Measurement Technology

摘　　要：针对目前对在役衬胶管道脱粘缺陷缺乏有效检测手段,且检测效率和准确率较低的问题,基于超声脉冲回波法的基本原理,设计了适用于圆柱形衬胶管道超声检测的扫查和探头夹持装置,建立了相应的超声检测试验系统。分析了实际应用中多种干扰因素对超声回波信号的影响,构建了基于一维CNN的超声回波信号二分类模型。通过试验和与传统超声检测缺陷识别方法进行对比,结果表明利用所建立的超声检测系统及一维CNN模型能够在多种干扰因素存在的情况下实现对脱粘缺陷较精确的识别,识别准确率达到96.22%,为在役衬胶管道脱粘缺陷的自动化检测和识别提供了一种有效的方法和手段。In view of the current lack of effective detection methods for debonding defects in in-service rubber-lined pipes,as well as low detection efficiency and accuracy,based on the basic principle of ultrasonic pulse echo method,a scanning and probe clamping device suitable for ultrasonic detection of cylindrical rubber-lined pipes was designed,and a corresponding ultrasonic detection experimental system was established.Various interference factors that affect ultrasound echo signals in practical applications have been analyzed,and a binary classification model for ultrasound echo signals based on one-dimensional convolutional neural network(CNN)has been specifically constructed.Through experiments and comparison with traditional ultrasonic detection defect recognition methods,the results show that the established ultrasonic detection system and one-dimensional CNN model can achieve more accurate identification of debonding defects even in the presence of multiple interference factors,with an accuracy rate of 96.22%.This provides an effective method and means for the automated detection and recognition of debonding defects in in-service rubberlined pipes.

关 键 词：超声检测 衬胶管道 脱粘缺陷 卷积神经网络 

分 类 号：TB551[理学—物理] TP391.4[理学—声学]