基于LabVIEW的火车轮面裂纹深度在线监测技术  

Online monitoring technology of train wheel tread crack based on Lab VIEW

作　　者：夏蓉花 刘春 赵慧娟 XIA Ronghua;LIU Chun;ZHAO Huijuan(Jiangsu College of Safety Technology,Xuzhou 221011,China)

机构地区：[1]江苏安全技术职业学院,江苏徐州221011

出　　处：《电子设计工程》2022年第21期24-28,共5页Electronic Design Engineering

基　　金：江苏省高等教育学会专项课题(2020NDKT031)。

摘　　要：针对高速高温运行时火车车轮踏面裂纹检测困难且精度差的问题,采用虚拟仪器技术开发了一种基于电磁超声检测的深度在线检测方案。为了解决电磁超声系统的EMTS限制,设计了一款新型磁场发生器,满足测量磁场的发射要求。通过引入比例积分控制算法提高电流控制精度,利用深度学习网络降低大规模信号噪声分析的操作成本,从而有效提升测量精度和信号的分析效率。由实验与测试结果可知,文中所提方法的电流控制精度在0.01%以内,系统分析精度随着测量距离和数据量的增大而有所提高,为工程应用提供了参考。In order to solve the problem of difficult and poor accuracy of crack detection on the tread of train wheels in high-speed and high temperature operation,a deep online detection scheme based on electromagnetic ultrasonic detection is developed by using virtual instrument technology.In order to solve the limitation of EMTS in electromagnetic ultrasonic system,a new magnetic field generator is designed to meet the emission requirements of measuring magnetic field.By introducing the proportional integral control algorithm to improve the accuracy of current control,the operation cost of large-scale signal noise analysis is reduced by using deep learning network,so as to effectively improve the measurement accuracy and signal analysis efficiency.The results of experiments and tests show that the current control accuracy of the method is less than 0.01%,and the accuracy of system analysis is improved with the increase of measurement distance and data quantity,which provides reference for engineering application.

关 键 词：虚拟仪器 电磁场超声检测 LABVIEW 深度学习 

分 类 号：TP393[自动化与计算机技术—计算机应用技术] TN99[自动化与计算机技术—计算机科学与技术]