基于机器视觉的煤矿井下带式输送机堆煤故障监测系统研发  

Development of Fault Monitoring System for Coal Stacking of Belt Conveyor in Underground Coal Mine Based on Machine Vision

作　　者：王锐 冯彦军 任建超[1] 刘茂福 丁维波 赵燚 Wang Rui;Feng Yanjun;Ren Jianchao;Liu Maofu;Ding Weibo;Zhao Yi(SHCCIG Caojiatan Mining Co.,Ltd.,Yulin 719100,China;CCTEG Mining Research Institute Co.,Ltd.,Beijing 100013,China;Tiandi Science and Technology Co.,Ltd.,Beijing 100013,China)

机构地区：[1]陕西陕煤曹家滩矿业有限公司,陕西榆林719100 [2]中煤科工开采研究院有限公司,北京100013 [3]天地科技股份有限公司,北京100013

出　　处：《煤矿机械》2024年第9期157-159,共3页Coal Mine Machinery

基　　金：国家重点研发计划项目(2023YFC2907502);陕西陕煤曹家滩矿业有限公司项目(KCYJY-2023-ZD-02);天地科技股份有限公司科技创新创业资金专项项目(2023-TD-ZD003-003)。

摘　　要：提出了一种基于机器视觉的煤矿井下带式输送机堆煤故障监测系统。在图像预处理阶段分别采用小波去噪算法和加权平均法进行去噪和灰度化处理,随后采用Canny算子进行堆煤图像边缘检测,进而将检测结果与设定阈值对比后进行堆煤故障判断,若发生故障则控制继电器关闭井下带式输送机。试验结果显示,该系统对堆煤故障识别的准确率可达96%,可为煤矿安全生产、监测设备及装备故障诊断方面的研究人员提供一定的借鉴。A coal stacking fault monitoring system for belt conveyor in underground coal mine based on machine vision was proposed.The wavelet denoising algorithm and weighted average method were used for denoising and grayscale processing in the image preprocessing stage,then the Canny operator was used for edge detection of coal stacking images,and then the detection results were compared with the set threshold for coal stacking fault judgment.If the fault occurs,the relay is controlled to close the underground belt conveyor.The test results show that the accuracy of this system for coal stacking fault identification can reach 96%.It can provide a certain reference for researchers in the aspects of coal mine safety production,monitoring equipment and equipment fault diagnosis.

关 键 词：带式输送机 堆煤故障 机器视觉 

分 类 号：TD528.1[矿业工程—矿山机电]