• Chinese Core Periodicals
  • Chinese Core Journals of Science and Technology
  • RCCSE Chinese Authoritative Academic Journals
Email Alert RSS
Advanced Search
MENG Guangrui, YANG Chuang. Coal mine dangerous gas inspection system based on multi-sensor fusion[J]. Safety in Coal Mines, 2021, 52(12): 128-132.
Citation: MENG Guangrui, YANG Chuang. Coal mine dangerous gas inspection system based on multi-sensor fusion[J]. Safety in Coal Mines, 2021, 52(12): 128-132.
shu

Coal mine dangerous gas inspection system based on multi-sensor fusion

More Information
  • Published Date: December 19, 2021
    Graphical Abstract
    • Abstract
  • A coal mine dangerous gas inspection system based on multi-sensor fusion technology is designed. The overall architecture of the system is divided into execution layer, network layer and decision layer. The monitoring platform and server in the decision layer and the mobile inspection robot in the execution layer realize data transmission through the network layer. This paper introduces the process of dangerous gas inspection and the module composition and function classification of inspection robot. The intelligent sensing module of the robot has the corresponding environmental parameter monitoring function. The robot can realize autonomous navigation and obstacle avoidance and dangerous gas concentration data acquisition by carrying a variety of sensors. The BP neural network algorithm is used to realize multi-sensor fusion, The real-time monitoring of various dangerous gas concentrations in coal mine underground environment is completed.
    • FullText(HTML)
    • References (11)
  • [1]
    单敏柱.煤矿瓦斯爆炸灾害风险预测研究[D].阜新：辽宁工程技术大学，2017.
    [2]
    倪兴，张开加，仇海生，等.煤矿瓦斯安全监测系统的设计方法及实现路径[J].矿业装备，2021（1）：88-89.
    [3]
    郑仁成，范朝阳，刘立发，等.基于四轮独立驱动/转向的危险气体探测自主巡检机器人设计[C]//2020中国自动化大会（CAC2020）论文集.北京：中国自动化学会，2020:764-769.

    ZHENG Rencheng,FAN Chaoyang,LIU Lifa,etc. Development of autonomous inspection robot for hazardous gas detection based on four-wheel independent drive and steering[C]// Proceedings of 2020 China Automation Conference（ＣＡＣ2020）. Beijing: Chinese Association of Automation, 2020: 764-769.
    [4]
    郗艳梅，石岩，岳红新.煤矿危险气体探测机器人结构和防爆设计[J].煤炭技术，2016，35（9）：260-261.

    XI Yanmei, SHI Yan, YUN Hongxin. Structure and explosion-proof design of coal mine danger gas detection robot[J]. Coal Technology, 2016, 35（9）: 260-261.
    [5]
    张延芝.无线局域网络数据多字符排序加密传输方法[J].自动化与仪器仪表，2021（6）：35-38.

    ZHANG Yanzhi. Multi character sorting encryption transmission method for wlan data[J]. Automation & Instrumentation, 2021（6）: 35-38.
    [6]
    秦秀常.多传感器融合技术的应用研究[J].光源与照明，2020，（12）：44-46.

    QIN Xiuchang. Application research of multi sensor fusion technology[J]. Lamps and Lighting, 2020（12）: 44-46.
    [7]
    单成伟，赵聪慧，蒋泽，等.煤矿井下隔爆兼本质安全型计算机的设计[J].工矿自动化，2010，36（11）：92.

    SHAN Chengwei, ZHAO Conghui, JIANG Ze, et al. Design of explosion-proof and intrinsic safety type computer used in coal mine underground[J]. Industry and Mine Automation, 2010, 36（11）: 92.
    [8]
    程健.煤矿巷道机器人管线视觉辅助定位与导航方法研究[J].煤炭科学技术，2020，48（7）：226-232.

    CHEN Jian. Study on pipeline vision-aided positioning and navigation method for coal mine tunnel robot[J]. Coal Science and Technology, 2020, 48（7）: 226-232.
    [9]
    王美蕴.基于多无线传感器的数据融合算法[J].电子测试，2021，（8）：73.

    WANG Meiyun. Data fusion algorithm based on multiple wireless sensors[J]. Electronic Test, 2021（8）: 73.
    [10]
    金成，王久洪，张成，等.结合传感器阵列和BP神经网络的挥发性有机物定量检测系统[J].西安交通大学学报，2021（10）：1-8.

    JIN Cheng, WANG Jiuhong, ZHANG Cheng, et al. Quantitative detection system of volatile organic compound combining sensor array and BP neural network[J]. Journal of Xi'an Jiaotong University, 2021（10）: 1-8.
    [11]
    陈辉，周圆圆.基于PCA-BP神经网络的设备故障预测研究[J].物流工程与管理，2021，43（6）：150-153.

    CHEN Hui, ZHOU Yuanyuan. Research on equipment failure prediction based on PCA-BP neural network[J]. Logistics Engineering and Management, 2021, 43（6）: 150-153.
    • Related Articles

  • Related Articles

    [1]LIU Feng. Prediction of gas emission based on PCA-SAPSO-BP neural network[J]. Safety in Coal Mines, 2023, 54(4): 60-68.
    [2]ZHAO Wei, CHEN Peihong, CAO Yang. Prediction model of coal seam gas content based on ACSOA optimized BP neural network[J]. Safety in Coal Mines, 2022, 53(1): 174-180.
    [3]LIU Tuanjie, ZHAO Xiangzhuo, HAN Yongliang, LI Yunpeng, CHEN Xi. Strength prediction of solid wastes filling body based on GRA--BP neural network[J]. Safety in Coal Mines, 2021, 52(9): 231-238.
    [4]WANG Shenhao, XIE Wanli, XI Jiami, JING Xu. Prediction of mine collapse risk based on GIS and BP neural network[J]. Safety in Coal Mines, 2021, 52(9): 218-223,230.
    [5]ZHANG Fan, ZHONG Hao. Research on Mine Thermodynamic Disaster Monitoring Based on BP Neural Network[J]. Safety in Coal Mines, 2020, 51(11): 216-220.
    [6]XIE Lirong, WANG Jinrui, Mutellip Ahmat, LU Peng, NIU Yongchao. Prediction for Coal Mine Gas Emission Based on LVQ-GA-BP Neural Network[J]. Safety in Coal Mines, 2017, 48(12): 150-152,156.
    [7]LI Hongliang, HUA Xinzhu, ZHANG Zhonghao. Distribution Prediction of Advanced Abutment Pressure of Based on BP Neural Network Model[J]. Safety in Coal Mines, 2015, 46(2): 209-212.
    [8]PAN Bin, LI Yuan-long, GE Xiao-ming, LAI Chuan-long, CHEN Jing-can. Overlying Rock Movement BP Neural Networks Prediction Based on Orthogonal Experiment Design[J]. Safety in Coal Mines, 2013, 44(10): 209-211.
    [9]XU Dong-jing, SHI Long-qing, QIU Mei, SUN Qi, LIU Lei. Forecast of Small Structure for Mine Based on BP Neural Networks[J]. Safety in Coal Mines, 2013, 44(2): 50-52,56.
    [10]DAI Hong-lei, HAN Li-tao, CHEN Chuan-fa. Prediction Model of Gas Disaster Based on Quasi-Newton Algorithm BP Neural Network[J]. Safety in Coal Mines, 2012, 43(10): 11-14.

  • Cited by

    Periodical cited type(3)

    1. 李争荣，杨八九，冯兴隆，牛向东，赵明亮，侯克鹏，徐柱圆. 普朗铜矿出矿口封堵墙侧压力计算分析. 采矿技术. 2024(05): 150-156 .
    2. 韩东洋，张建忠，樊龙，王振兴，宋桂军. 综放工作面顺槽联巷密闭墙构建参数研究. 煤炭技术. 2024(12): 89-93 .
    3. 张建忠，黄克军，刘鑫，杨琛. 一种新型临时密闭墙材料. 机电一体化. 2021(04): 47-52 .

    Other cited types(2)

Catalog

    Get Citation
    PDF
    XML
    Article views (62) PDF downloads (37) Cited by(5)
    Turn off MathJax
    Article Contents
    Abstract
    References
    Related Articles
    Links

    辽公网安备 21049802000106号　辽ICP备11020636号-9

    Website Copyright © CCTEG Shenyang Research Institute

    Address：No. 11 Binhe Road, Fushun Economic Development Zone, Liaoning　Postal Code：113122

    Tel：024-56616988/56616881　Email：mkaq@163.com

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return