基于SVM的煤矿带式输送机超载检测系统研究  

Research on Overload Detection System of Coal Mine Belt Conveyor Based on SVM

作　　者：冯彦军 刘茂福[3] Feng Yanjun;Liu Maofu(CCTEG Coal Mining Research Institute Co.,Ltd.,Beijing 100013,China;Tiandi Science and Technology Co.,Ltd.,Beijing 100013,China;SHCCIG Caojiatan Mining Co.,Ltd.,Yulin 719100,China)

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

出　　处：《煤矿机械》2025年第3期42-44,共3页Coal Mine Machinery

基　　金：陕西陕煤曹家滩矿业有限公司项目(KCYJY-2023-ZD-02)。

摘　　要：为了提高煤矿带式输送机运行的安全性和效率,提出了一种基于支持向量机(SVM)的超载检测系统。该系统利用工业相机拍摄带式输送机照片,通过图像预处理、特征提取和径向基函数核支持向量机(RBF-SVM)模型构建,实现了对超载状态的准确识别。在某煤矿现场进行实际测试,结果表明该系统在正常环境、光照干扰环境和粉尘干扰环境下的检测成功率分别为93.5%、86.5%和81.5%。由此证明该系统能够有效识别带式输送机的超载状态,为煤矿安全生产提供一定的技术支持。相关成果可为煤矿开采自动化及煤矿安全监测的研究人员提供一定的借鉴。To improve the safety and efficiency of belt conveyor operation in coal mine,proposed an overload detection system based on support vector machine(SVM).This system utilizes industrial cameras to capture images of the conveyor,and through image preprocessing,feature extraction,and the construction of a radial basis function kernel support vector machine(RBF-SVM)model,it achieves accurate identification of overload conditions.Field tests conducted at a coal mine,the results showed that this system had detection success rates of 93.5%,86.5%and 81.5%under normal conditions,light interference and dust interference environments,respectively.It demonstrates that this system can effectively identify overload conditions of belt conveyor,providing technical support for safe production in coal mine.The relevant findings can serve as a reference for researchers working on automation in coal mining and safety monitoring.

关 键 词：带式输送机 超载检测 SVM 机器学习 

分 类 号：TD528.1[矿业工程—矿山机电] TD76