煤矿采掘装备多源数据融合智能监控与动态预警系统研究

Research on Multi-source Data Fusion Intelligent Monitoring and Dynamic Early Warning System for Coal Mine Mining and Excavation Device

  • 摘要: 针对在煤矿安全评价工作中,采掘装备监控系统存在覆盖不全和预警不及时的问题,研发了基于物联网的智能监控预警系统。通过评估安全风险,制定了涵盖振动、温度、气压等关键参数的监测方案,建立了面向安全评价的智能监控指标体系;结合安全评价经验,设计了设备故障风险评估模型,实现了对采煤机与掘进机等核心装备的安全状态分析。该系统整体预警准确率达到88%(其中设备故障预警准确率为77.2%,设备状态预警准确率为92.3%),故障响应时间从5.2 h缩短至0.8 h,降低了84.6%,设备可用率提升了5.8个百分点至95.0%。系统协助完成了32次设备安全评价,发现并预警潜在隐患6次,设备安全事故率同比下降15%,为煤矿安全生产提供了有效支持。

     

    Abstract: In view of the existing problems of incomplete coverage and untimely early warning in the monitoring system of mining and excavation device in coal mine safety evaluation work, an intelligent monitoring and early warning system based on IoT is developed. Through evaluating safety risks, a monitoring scheme covering key parameters such as vibration, temperature, air pressure and others is formulated, and an intelligent monitoring index system for safety evaluation is established; Combined with the experience of safety evaluation, a risk evaluation model for equipment fault is designed, and an analysis of safety status for core devices such as shearer, roadheader and others is achieved. The overall early warning accuracy rate of this system reaches 88% (with an early warning accuracy rate of equipment fault of 77.2%, an early warning accuracy rate of equipment status of 92.3%), the fault response time is shortened from 5.2 hours to 0.8 hours, with a reduction of 84.6%, and the equipment availability rate is improved by 5.8 percentage points to 95.0%. The system assists in completing 32 times of equipment safety evaluation, identifies and early warns 6 times of potential hidden dangerous, and the equipment safety accident rate is reduced by 15% year-on-year, providing a effective support for coal mine safety production.

     

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