强力输送带钢绳芯故障检测系统设计  

Design of core fault detection system for steel cord conveyer belt

作　　者：马牧燕[1] 沈冰夏[2] 冷俊敏[1] 

机构地区：[1]北京信息科技大学光电信息与通信工程学院,北京100192 [2]西门子(中国)有限责任公司,北京100102

出　　处：《北京信息科技大学学报（自然科学版）》2011年第6期62-65,共4页Journal of Beijing Information Science and Technology University

基　　金：北京市教委科技发展计划项目面上项目(KM201010772008)

摘　　要：对矿用强力输送带内部钢绳芯出现接头伸长和锈蚀等问题,提出采用基于虚拟仪器的机器视觉方法,建立一套强力输送带钢绳芯故障检测系统,完成钢绳芯图像获取、图像预处理、故障判别和对接头长度测量、锈蚀面积测量等。建立实现钢绳芯故障图像自动分类BP神经网络,并提出一种自适应调节学习速率算法;利用机器视觉对复杂精细目标进行准确、迅速识别和测量,对钢绳芯接头长度和锈蚀面积进行了实测。实验结果表明,该检测系统可以迅速、准确地判断出钢绳芯接头拉伸和锈蚀故障,为煤矿运输生产提供安全保证。A machine vision method based on the virtual instrument has been put forward for the problem of joint elongation and rust in the steel core of mine conveyer belt.A set of fault detection system has been established for steel cores of the conveyer belt.The images of the steel core have been acquired and preprocessed and the faults have been discriminated.The joint＇s length and corrosion area have been measured.The BP neural network of automatic image classification for steel core faults has been achieved.An adaptive learning rate algorithm is proposed.Elaborate targets are recognized and measured accurately and promptly using machine vision.The measurements of steel core joint length and corrosion area are made.The detection system can detect rapidly and accurately the joint elongation and corrosion faults of the steel core,which provides security guarantee for coal transportation and production.

关 键 词：强力输送带 虚拟仪器 机器视觉 故障检测 故障分类 

分 类 号：TN911.73[电子电信—通信与信息系统]