摘要
为了实现半自磨工艺智能给料及精准运行,提高设备运行效率,降低生产成本,针对磨机负荷监测中存在的单传感器精度不足、工况适应性差和控制滞后等问题,提出了一种基于振动、声学和电流信号多源融合的实时监测与智能控制方法。通过时域分析(RMS、峭度、裕度因子)和频域分析(重心频率、频带能量比)提取关键特征,创新性地引入谐波动态补偿机制和特征动态加权融合模型,结合双通道深度学习架构实现了填充率高精度估计(误差<3%)。工业应用结果表明:系统响应延迟<100 ms,吨矿电耗降低12.4%,有效解决了传统方法在复杂工况下的监测与控制难题,为磨机智能化提供了完整的解决方案。
In order to realize intelligent feeding and precise operation of semi-autogenous grinding process,improve equipment operation efficiency and reduce production cost,a real-time monitoring and intelligent control method based on multi-source fusion of vibration,acoustic and current signals is proposed to solve the problems of insufficient accuracy of single sensor,poor adaptability of working conditions and control lag in mill load monitoring.The key features are extracted by time domain analysis(RMS,kurtosis,margin factor)and frequency domain analysis(center of gravity frequency,frequency band energy ratio).The harmonic dynamic compensation mechanism and feature dynamic weighted fusion model are innovatively introduced,and the high-precision estimation of filling rate(error<3%)is realized by combining the dual-channel deep learning architecture.The industrial application results show that the system response delay is less than 100 ms,and the power consumption per ton of ore is reduced by 12.4%.It effectively solves the monitoring and control problems of traditional methods under complex working conditions,and provides a complete solution for the intelligentization of the mill.
作者
张义坤
任建辉
王征
李彦科
博玉亮
连欢超
ZHANG Yikun;REN Jianhui;WANG Zheng;LI Yanke;BO Yuliang;LIAN Huanchao(Shahe Zhongguan Iron Mine Co.,Ltd.,Hebei Iron and Steel Group;Hebei Province Complex Iron Ore Low Carbon Intelligent and Efficient Mining Technology Innovation Center)
出处
《现代矿业》
2026年第1期44-48,共5页
Modern Mining
关键词
磨机负荷监测
多传感器融合
动态加权
深度学习
闭环控制
mill load monitoring
multi-sensor fusion
dynamic weighting
deep learning
closedloop control
