Volume 50 Issue 1
Jan.  2024
Turn off MathJax
Article Contents
Article Navigation >  Journal of Mine Automation  > 2024  >  50(1): 1-8, 16
LIU Dongyang, ZHANG Lang, YAO Haifei, et al. Research and application of intelligent early warning system for coal mine fires[J]. Journal of Mine Automation，2024，50（1）：1-8, 16.  doi: 10.13272/j.issn.1671-251x.2023070092
Citation: LIU Dongyang, ZHANG Lang, YAO Haifei, et al. Research and application of intelligent early warning system for coal mine fires[J]. Journal of Mine Automation，2024，50（1）：1-8, 16.  doi: 10.13272/j.issn.1671-251x.2023070092
shu

Research and application of intelligent early warning system for coal mine fires

doi: 10.13272/j.issn.1671-251x.2023070092
  • 1.

    Mine Intelligent Ventilation Division, China Coal Research Institute, Beijing 100013, China

  • 2.

    CCRI Tong'an (Beijing) Intelligent Control Technology Co., Ltd., Beijing 100013, China

  • 3.

    Beijing Engineering and Research Center of Mine Safe, Beijing 100013, China

  • 4.

    National Key Laboratory of Intelligent Coal Mining and Rock Strata Control, Beijing 100013, China

  • Received Date: 2023-07-26
  • Rev Recd Date: 2024-01-16
    • Available Online: 2024-01-31
    Abstract
  • Currently, the coal mine fire monitoring system has achieved separate monitoring of some indicators such as the iconic gases, temperature, smoke, and flame of coal spontaneous combustion in mines.But the system has not effectively, comprehensively, and uniformly monitored the factors related to coal mine fires. In order to solve this problem, potential risk factors of coal mine fires are analyzed from two aspects: internal and external factors. A method of monitoring fire situation in different sources and areas is proposed. In terms of internal fires, monitoring is mainly carried out on goaf areas, enclosed goaf areas, and artificial natural fire observation points that are prone to fires. In terms of external fires, monitoring is mainly carried out on the mechanical and electrical chambers and their distribution points, belt conveyor systems, cables, and other aspects. A monitoring index system for coal mine fire sources and areas has been established. The system regularly collects or updates fire feature parameter data through manual or online monitoring. According to the data collection method and impact degree, fire monitoring indicators are divided into dynamic indicators, static indicators, and related indicators. The overall architecture and business process of a fire intelligent warning system is designed. The system uses a warning method based on multi index joint logical reasoning to achieve internal fire warning, and uses a multi parameter fusion warning method based on D-S evidence theory to achieve external fire warning. The on-site test results show that the fire intelligent warning system has achieved effective monitoring and warning of mine fires, with a visual display function of a coal mine fire risk warning "one picture". The system has a fire intelligent simulation demonstration function and a dynamic planning function for disaster avoidance routes.

     

    • FullText(HTML)
  • loading
    • References(29)
  • [1]
    邓军,文虎,张辛亥,等. 煤田火灾防治理论与技术[M]. 徐州:中国矿业大学出版社,2014.

    DENG Jun,WEN Hu,ZHANG Xinhai,et al. Coal field fire prevention theory and technology[M]. Xuzhou:China University of Mining & Technology Press,2014.
    [2]
    邓军,肖旸,张辛亥,等. 煤火灾害防治技术的研究与应用[J]. 煤矿安全,2012,43(增刊1):58-61.

    DENG Jun,XIAO Yang,ZHANG Xinhai,et al. Research and application of coal fire disaster prevention and control technology[J]. Safety in Coal Mines,2012,43(S1):58-61.
    [3]
    梁运涛,侯贤军,罗海珠,等. 我国煤矿火灾防治现状及发展对策[J]. 煤炭科学技术,2016,44(6):1-6,13.

    LIANG Yuntao,HOU Xianjun,LUO Haizhu,et al. Development countermeasures and current situation of coal mine fire prevention & extinguishing in China[J]. Coal Science and Technology,2016,44(6):1-6,13.
    [4]
    孙继平,孙雁宇. 矿井火灾监测与趋势预测方法研究[J]. 工矿自动化,2019,45(3):1-4.

    SUN Jiping,SUN Yanyu. Research on methods of mine fire monitoring and trend prediction[J]. Industry and Mine Automation,2019,45(3):1-4.
    [5]
    国家发展改革委,国家能源局,应急管理部,等. 关于印发《关于加快煤矿智能化发展的指导意见》的通知[EB/OL]. [2023-06-10]. https://www.gov.cn/zhengce/zhengceku/2020-03/05/content_5487081.htm.

    National Development and Reform Commission,National Energy Administration,Ministry of Emergency Management,et al. Notice on printing and distributing The guiding opinions on speeding up the intelligent development of coal mines[EB/OL]. [2023-06-10]. http://www.gov.cn/zhengce/zhengceku/2020-03/05/content_5487081.htm.
    [6]
    国家能源局,国家矿山安全监察局. 国家能源局国家矿山安全监察局关于印发《煤矿智能化建设指南(2021年版)》的通知[EB/OL]. [2023-06-10]. http://www.gov.cn/zhengce/zhengceku/2021-06/19/content_5619502.htm.

    National Energy Administration,National Mine Safety Administration. Notice on issuing the Guide to Intelligent Construction in Coal Mines(2021 Edition) [EB/OL]. [2023-06-10]. http://www.gov.cn/zhengce/zhengceku/2021-06/19/content_5619502.htm.
    [7]
    仲晓星,王建涛,周昆. 矿井煤自燃监测预警技术研究现状及智能化发展趋势[J]. 工矿自动化,2021,47(9):7-17.

    ZHONG Xiaoxing,WANG Jiantao,ZHOU Kun. Monitoring and early warning technology of coal spontaneous combustion in coal mines:research status and intelligent development trends[J]. Industry and Mine Automation,2021,47(9):7-17.
    [8]
    车辉,邢慧芬,樊玉琦,等. 基于大数据的火灾智能预警系统[J]. 计算机系统应用,2020,29(10):120-126.

    CHE Hui,XING Huifen,FAN Yuqi,et al. Fire intelligent early warning system based on big data[J]. Computer Systems & Applications,2020,29(10):120-126.
    [9]
    曹一凡. 基于物联网的火灾监测预警系统研究[D]. 唐山:华北理工大学,2020.

    CAO Yifan. Research on the Iot based fire monitoring and early warning system[D]. Tangshan:North China University of Science and Technology,2020.
    [10]
    郭庆. 采空区煤自燃预警技术及应用研究[D]. 徐州:中国矿业大学,2021.

    GUO Qing. Research on early warning technology and application of coal spontaneous combustion in mined areas[D]. Xuzhou:China University of Mining and Technology,2021.
    [11]
    陈晓晶. 基于“云−边−端”协同的煤矿火灾智能化防控体系建设[J]. 煤炭科学技术,2022,50(12):136-143.

    CHEN Xiaojing. Construction of intelligent prevention and control of coal mine fire based on "cloud-edge-end" cooperation[J]. Coal Science and Technology,2022,50(12):136-143.
    [12]
    徐磊,李希建. 基于大数据的矿井灾害预警模型[J]. 煤矿安全,2018,49(3):98-101.

    XU Lei,LI Xijian. Mine disaster warning model based on big data[J]. Safety in Coal Mines,2018,49(3):98-101.
    [13]
    岳宁芳,金彦,孙明福,等. 基于多指标气体的煤自燃进程分级预警研究[J]. 安全与环境学报,2020,20(6):2139-2146.

    YUE Ningfang,JIN Yan,SUN Mingfu,et al. Multi-staged warning system for controlling the coal spontaneous combustion based on the various index gases[J]. Journal of Safety and Environment,2020,20(6):2139-2146.
    [14]
    丁震,李浩荡,张庆华. 煤矿灾害智能预警架构及关键技术研究[J]. 工矿自动化,2023,49(4):15-22.

    DING Zhen,LI Haodang,ZHANG Qinghua. Research on intelligent hazard early warning architecture and key technologies for coal mine[J]. Journal of Mine Automation,2023,49(4):15-22.
    [15]
    张庆华,马国龙. 我国煤矿重大灾害预警技术现状及智能化发展展望[J]. 智能矿山,2020,1(1):52-62.

    ZHANG Qinghua,MA Guolong. Status and intelligent development prospect of coal mine major disaster early-warning technology in China[J]. Journal of Intelligent Mine,2020,1(1):52-62.
    [16]
    李明建. 煤矿多灾种融合预警技术与装备[J]. 智能矿山,2022,3(7):156-157.

    LI Mingjian. Multi disaster integrated early warning technology and equipment for coal mines[J]. Journal of Intelligent Mine,2022,3(7):156-157.
    [17]
    李杰. 青龙煤矿煤自燃无线监测预警技术研究[D]. 西安:西安科技大学,2020.

    LI Jie. Study on wireless monitoring and early warning technology of coal spontaneous combustion in Qinglong Coal Mine[D]. Xi'an:Xi'an University of Science and Technology,2020.
    [18]
    程永新. 煤矿带式输送机火灾光纤传感检测技术研究[J]. 煤炭科学技术,2019,47(2):131-135.

    CHENG Yongxin. Technology research on optical fiber sensing detection for belt conveyor fire in coal mine[J]. Coal Science and Technology,2019,47(2):131-135.
    [19]
    赵双斌. 煤矿机电硐室温度监测与预警功能实现[J]. 煤炭与化工,2022,45(7):86-88.

    ZHAO Shuangbin. The realization and effect evaluation of temperature monitoring and early warning function of coal mine electromechanical chamber[J]. Coal and Chemical Industry,2022,45(7):86-88.
    [20]
    王国法,任怀伟,庞义辉,等. 煤矿智能化(初级阶段)技术体系研究与工程进展[J]. 煤炭科学技术,2020,48(7):1-27.

    WANG Guofa,REN Huaiwei,PANG Yihui,et al. Research and engineering progress of intelligent coal mine technical system in early stages[J]. Coal Science and Technology,2020,48(7):1-27.
    [21]
    谭波,邵壮壮,郭岩,等. 基于指标气体关联分析的煤自燃分级预警研究[J]. 中国安全科学学报,2021,31(2):33-39.

    TAN Bo,SHAO Zhuangzhuang,GUO Yan,et al. Research on grading and early warning of coal spontaneous combustion based on correlation analysis of index gas[J]. China Safety Science Journal,2021,31(2):33-39.
    [22]
    邓军,肖旸,陈晓坤,等. 矿井火灾多源信息融合预警方法的研究[J]. 采矿与安全工程学报,2011,28(4):638-643. doi: 10.3969/j.issn.1673-3363.2011.04.026

    DENG Jun,XIAO Yang,CHEN Xiaokun,et al. Study on early warning method of multi-source information fusion for coal mine fire[J]. Journal of Mining & Safety Engineering,2011,28(4):638-643. doi: 10.3969/j.issn.1673-3363.2011.04.026
    [23]
    洪向共,钟地长,赵庆敏. 基于多传感器融合的陆空两栖机器人移动控制系统设计[J]. 科学技术与工程,2020,20(8):3103-3108.

    HONG Xianggong,ZHONG Dichang,ZHAO Qingmin. Design of mobile control system for air-ground amphibious robot based on multi-sensor fusion[J]. Science Technology and Engineering,2020,20(8):3103-3108.
    [24]
    颜云华,金炜东. 基于多传感器信息融合的列车转向架机械故障诊断方法[J]. 计算机应用与软件,2020,37(8):48-51. doi: 10.3969/j.issn.1000-386x.2020.08.009

    YAN Yunhua,JIN Weidong. Mechanical fault diagnosis method of train bogie based on multi-sensor information fusion[J]. Computer Applications and Software,2020,37(8):48-51. doi: 10.3969/j.issn.1000-386x.2020.08.009
    [25]
    王嫒娜,李英顺,贺喆. D−S证据理论融合粗糙集的火控系统状态评估[J]. 控制工程,2020,27(12):2176-2184.

    WANG Aina,LI Yingshun,HE Zhe. State evaluation of fire control system based on fusion of D-S evidence theory and rough set[J]. Control Engineering of China,2020,27(12):2176-2184.
    [26]
    王俊松,李建林. D−S证据理论改进方案综述[J]. 信息化研究,2011,37(6):4-7.

    WANG Junsong,LI Jianlin. Overview of D-S evidence theory modification[J]. Informatization Research,2011,37(6):4-7.
    [27]
    杨呈永,刘佳祎. 基于物联网节点加权的D−S证据理论数据融合算法[J]. 桂林理工大学学报,2019,39(3):731-736.

    YANG Chengyong,LIU Jiayi. Data fusion algorithm based on weighted D-S evidence theory in Internet of things[J]. Journal of Guilin University of Technology,2019,39(3):731-736.
    [28]
    叶瑾,许枫,杨娟,等. 一种基于多传感器的复合量测IMM−EKF数据融合算法[J]. 电子学报,2020,48(12):2326-2330.

    YE Jin,XU Feng,YANG Juan,et al. A composite measurement IMM-EKF data fusion algorithm based on multi-sensor[J]. Acta Electronica Sinica,2020,48(12):2326-2330.
    [29]
    韩丙光,赵子源,刘建,等. 基于多传感器信息融合的电缆火灾预警建模与仿真[J]. 电子设计工程,2022,30(10):150-154.

    HAN Bingguang,ZHAO Ziyuan,LIU Jian,et al. Modeling and simulation of cable fire warning based on multi-sensor information fusion[J]. Electronic Design Engineering,2022,30(10):150-154.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(3)  / Tables(4)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (859) PDF downloads(137) Cited by()
    Proportional views
    Related

    苏ICP备 05016349号-2

    Supported by: Beijing Renhe Information Technology Co. Ltd.  Visits：    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return