摘要
矿用带式输送机是矿山企业重要的运输设备,由于传统的检测方法在对该设备运行过程进行检测时,检测结果常常与实际故障量相差较大,严重影响企业生产进度。对此,通过对带标记矿用带式输送机运行图像预处理,去除图像噪声;再基于机器视觉无损检测技术对带式输送机标记进行定位,增加标记数字与背景之间的对比度;最后采用卷及神经网络算法,实现对矿用带式输送机标记识别及故障定位。同时,通过实验证明,新的检测方法可以有效提高对运行过程中的矿用带式输送机的检测精度,保证矿山企业的安全生产,进一步提高企业经济效益。
Mining belt conveyor is an important transportation equipment in mining enterprises,because the traditional detection method in the operation of the equipment detection process,the test results are often different from the actual amount of failure,seriously affect the production progress of enterprises.In this paper,image noise is removed by pre-processing the running image of belt conveyor with marking mine,and the mark of belt conveyor is located based on machine vision nondestructive testing technology to increase the contrast between marking number and background.At the same time,it is proved by experiments that the new detection method can In order to effectively improve the detection accuracy of mine belt conveyor during operation,ensure the safety of mining enterprises,and further improve the economic benefits of enterprises.
作者
王冬
WANG Dong(Jinzhou Tianyu Motor Vehicle Testing Co.,Ltd,Jinzhou 121000,China)
出处
《世界有色金属》
2020年第10期14-15,共2页
World Nonferrous Metals
关键词
机器视觉无损检测技术
矿用
带式
输送机
运行检测
machine vision nondestructive testing technology
mining
belt
conveyor
operation testing
