基于深度学习的长距离煤矿皮带运输机多级协同控速方法  

Multi Level Collaborative Speed Control Method for Long-Distance Coal Mine Belt Conveyors Based on Deep Learning

作　　者：史永 SHI Yong(Shaanxi Shaanxi Coal Tongchuan Mining Chenjiashan Coal Mine,Tongchuan 727102)

机构地区：[1]陕西陕煤铜川矿业陈家山煤矿,铜川727102

出　　处：《现代制造技术与装备》2025年第2期203-205,共3页Modern Manufacturing Technology and Equipment

摘　　要：为提升长距离煤矿皮带运输机多级协同控速性能,提出基于深度学习的控速方法研究。首先检测煤流量,确保精准感知运输负载;其次识别长距离煤矿皮带运输机的实时运行状态,为控速提供数据基础;最后利用深度学习,设计多级协同控速算法,实现多级皮带运输机的协同控速目标。实验结果显示,随着皮带运输机运行时间的增加,该方法应用后的带速波动率逐渐降低,展现出优异的控速性能,使皮带机运行更加平稳,有效提升了煤矿运输的安全性和效率。In order to improve the multi-level collaborative speed control performance of long-distance coal mine belt conveyors,a speed control method based on deep learning is proposed.Firstly,detect the coal flow rate to ensure accurate perception of transportation load;Secondly,identify the real-time operating status of long-distance coal mine belt conveyors to provide a data foundation for speed control;Finally,using deep learning,a multi-level collaborative speed control algorithm is designed to achieve the collaborative speed control goal of multi-level belt conveyors.The experimental results show that as the running time of the belt conveyor increases,the fluctuation rate of the belt speed gradually decreases after the application of this method,demonstrating excellent speed control performance,making the belt conveyor run more smoothly,and effectively improving the safety and efficiency of coal mine transportation.

关 键 词：深度学习 长距离 煤矿 皮带运输机 多级 协同 控速 

分 类 号：TD528.1[矿业工程—矿山机电] TP18[自动化与计算机技术—控制理论与控制工程]