摘要
煤矿带式运输机作为矿业生产的关键设备,其运行状态直接影响生产效率和安全性。提出一种基于数字孪生的煤矿带式运输机故障诊断与健康管理系统,通过构建物理设备与虚拟模型的实时映射,实现多源数据的动态采集与融合,并结合机器学习算法对运输机的运行状态进行智能分析与预测。系统主要包括实时监控、故障诊断、健康评估与维护决策四大功能模块,能够有效提高故障诊断的准确性与预测的及时性,降低设备维护成本,保障煤矿安全生产。
As a critical piece of equipment in mining production,the operational status of the belt conveyor directly impacts production efficiency and safety.A fault diagnosis and health management system for coal mine belt conveyors based on digital twin technology was proposed.By establishing a real-time mapping between the physical equipment and its virtual model,the system enables dynamic acquisition and integration of multi-source data.Combined with machine learning algorithms,it performs intelligent analysis and prediction of the conveyor's operational status.The system mainly comprises four functional modules:real time monitoring,fault diagnosis,health assessment,and maintenance decision-making.It effectively improves the accuracy of fault diagnosis and the timeliness of predictions,reduces equipment maintenance costs,and ensures safe coal mine production.
作者
周鹏鹏
ZHOU Pengpeng(Shanxi Lu'an Coal Technology Equipment Co.,Ltd.,Changzhi 046000,Shanxi,China)
出处
《能源与节能》
2025年第11期180-183,共4页
Energy and Energy Conservation
关键词
数字孪生
煤矿带式运输机
故障诊断
健康管理
机器学习
实时监控
预测性维护
digital twin
coal mine belt conveyor
fault diagnosis
health management
machine learning
real-time monitoring
predictive maintenance
