Volume 48 Issue 7
Aug.  2022
Turn off MathJax
Article Contents
Article Navigation >  Journal of Mine Automation  > 2022  >  48(7): 73-80
LI Zenglin, JIN Shukai, LIU Anqiang, et al. Top coal migration time measurement system based on accelerometer[J]. Journal of Mine Automation，2022，48(7)：73-80.  doi: 10.13272/j.issn.1671-251x.2022060089
Citation: LI Zenglin, JIN Shukai, LIU Anqiang, et al. Top coal migration time measurement system based on accelerometer[J]. Journal of Mine Automation，2022，48(7)：73-80.  doi: 10.13272/j.issn.1671-251x.2022060089
shu

Top coal migration time measurement system based on accelerometer

doi: 10.13272/j.issn.1671-251x.2022060089
  • 1.

    Shaanxi Coal Caojiatan Mining Co., Ltd., Yulin 719001, China

  • 2.

    Information Institute of Ministry of Emergency Management of the People's Republic of China, Beijing 100029, China

  • 3.

    School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

  • Received Date: 2022-06-23
  • Rev Recd Date: 2022-07-12
    • Available Online: 2022-08-09
    Abstract
  • The multi-round sequential memory coal drawing technology can improve the recovery rate of top coal and gangue content in the fully mechanized working face. But it needs to accurately measure and control the time of each round of coal drawing in field application. In the practical application of the automatic coal drawing technology based on the top coal migration tracker, the top coal movement tracker is only used as a mark point and is arranged in the top coal. The top coal movement tracker can not obtain more top coal movement information. In view of the above problems, based on the top coal movement tracker, a top coal migration time measurement system based on accelerometer is designed. The system includes three parts: tag, collector and central computer. The label is placed inside the top coal, and moves along with the top coal in the coal drawing process. Through the built-in accelerometer, the specific force data is collected in real-time. The time measurement algorithm is called to realize the monitoring of top coal migration. Then the different coal drawing stages are determined. The top coal migration time information of different stages is calculated. When the tag is released from the coal chute, it collides with the scraper conveyor belt, and sends the top coal migration time information outward to the collector through the RF signal. The information is further transmitted to the central computer through the field bus to guide the fully mechanized working face to realize multi-round of sequential coal drawing on site. The hardware and software design of the time measurement label of top coal migration is introduced in detail. The functions of real-time acquisition of specific force value, wireless signal transmission and data storage are realized. A calibration platform with 3D turntable as the core and Gauss-Newton method as the calibration algorithm is built. The calibration of the accelerometer is completed. The calibrated accelerometer can accurately collect the specific force data of the top coal migration time measurement label. According to the migration characteristics of top coal in the process of coal drawing, the time measurement algorithm based on threshold and the time measurement algorithm based on long-term and short-term memory (LSTM) are proposed. The time measurement algorithm based on threshold realizes the time identification of motion stage by introducing static threshold and maximum threshold. The time measurement algorithm based on LSTM identifies the dynamic changes of the specific force vector sum in the time domain, finds the mutation point, and realizes the time identification of the motion stage. The performance test of the two time measurement algorithms is completed through the tag free falling experiment. The time measurement variance is 0.000 6 and 0.000 2 respectively. The time measurement error is 13.07% and 5.22% respectively. The results meet the on-site top coal migration time measurement requirements. And the time measurement algorithm based on LSTM has obvious application advantages in top coal migration time measurement.

     

    • FullText(HTML)
  • loading
    • References(13)
  • [1]
    刘温飞. 放顶煤采煤工艺分析[J]. 矿业装备,2021(1):8-9. doi: 10.3969/j.issn.2095-1418.2021.01.003

    LIU Wenfei. Analysis of coal mining technology in caving coal[J]. Mining Equipment,2021(1):8-9. doi: 10.3969/j.issn.2095-1418.2021.01.003
    [2]
    王国法. 加快煤矿智能化建设 推进煤炭行业高质量发展[J]. 中国煤炭,2021,47(1):2-10. doi: 10.3969/j.issn.1006-530X.2021.01.002

    WANG Guofa. Speeding up intelligent construction of coal mine and promoting high-quality development of coal industry[J]. China Coal,2021,47(1):2-10. doi: 10.3969/j.issn.1006-530X.2021.01.002
    [3]
    王国法,范京道,徐亚军,等. 煤炭智能化开采关键技术创新进展与展望[J]. 工矿自动化,2018,44(2):5-12. doi: 10.13272/j.issn.1671-251x.17307

    WANG Guofa,FAN Jingdao,XU Yajun,et al. Innovation progress and prospect on key technologies of intelligent coal mining[J]. Industry and Mine Automation,2018,44(2):5-12. doi: 10.13272/j.issn.1671-251x.17307
    [4]
    王家臣, 杨克虎, 潘卫东, 等. 一种多轮顺序分层记忆放煤方法: 202110658946.7[P]. 2022-03-01.

    WANG Jiachen, YANG Kehu, PAN Weidong, et al. A multi-round sequential layering memory coal drawing method: 202110658946.7[P]. 2022-03-01.
    [5]
    吴婕萍,李国辉. 煤岩界面自动识别技术发展现状及其趋势[J]. 工矿自动化,2015,41(12):44-49. doi: 10.13272/j.issn.1671-251x.2015.12.012

    WU Jieping,LI Guohui. Development status and tendency of automatic identification technologies of coal-rock interface[J]. Industry and Mine Automation,2015,41(12):44-49. doi: 10.13272/j.issn.1671-251x.2015.12.012
    [6]
    郭永存,何磊,刘普壮,等. 煤矸双能X射线图像多维度分析识别方法[J]. 煤炭学报,2021,46(1):300-309. doi: 10.13225/j.cnki.jccs.2020.1626

    GUO Yongcun,HE Lei,LIU Puzhuang,et al. Multi-dimensional analysis and recognition method of coal and gangue dual-energy X-ray images[J]. Journal of China Coal Society,2021,46(1):300-309. doi: 10.13225/j.cnki.jccs.2020.1626
    [7]
    马英. 基于尾梁振动信号采集的煤矸识别智能放煤方法研究[J]. 煤矿开采,2016,21(4):40-42,25.

    MA Ying. Intelligent coal caving with gangue identification based on tail beam vibration signal collection[J]. Coal Mine Mining,2016,21(4):40-42,25.
    [8]
    张宁波,刘长友,陈现辉,等. 综放煤矸低水平自然射线的涨落规律及测量识别分析[J]. 煤炭学报,2015,40(5):988-993.

    ZHANG Ningbo,LIU Changyou,CHEN Xianhui,et al. Measurement analysis on the fluctuation characteristics of low level natural radiation from gangue[J]. Journal of China Coal Society,2015,40(5):988-993.
    [9]
    潘卫东,李新源,员明涛,等. 基于顶煤运移跟踪仪的自动化放煤技术原理及应用[J]. 煤炭学报,2020,45(增刊1):23-30.

    PAN Weidong,LI Xinyuan,YUN Mingtao,et al. Technology principle and field application of automatic coal drawing based on the top coal tracker[J]. Journal of China Coal Society,2020,45(S1):23-30.
    [10]
    王国富,刘忠奇,叶金才,等. 基于煤矿应用的超高密度的正反演算法[J]. 煤炭技术,2017,36(8):98-100.

    WANG Guofu,LIU Zhongqi,YE Jincai,et al. Forward and inverse algorithm based on ultra high density in coal mine application[J]. Coal Technology,2017,36(8):98-100.
    [11]
    杨克虎, 郁文生. 嵌入式智能传感器: 系统标定与建模[C] //第二十九届中国控制会议论文集, 北京, 2010: 3694-3699.

    YANG Kehu, YU Wensheng. Embedded smart sensor: system calibration and modeling[C] // Proceedings of the 29th Chinese Control Conference, Beijing, 2010: 3694-3699.
    [12]
    LI Guannan, ZHAO Xiaowei, FAN Cheng, et al. Assessment of long short-term memory and its modifications for enhanced short-term building energy predictions[J/OL]. Journal of Building Engineering, 2021, 43: 103182. https://doi.org/10.1016/jobe.2021.103182.
    [13]
    李旭帆,安霆. 基于nRF24L01的物联网无线数据传输系统[J]. 信息技术与信息化,2021(6):132-134.

    LI Xufan,AN Ting. Wireless data transmission system of Internet of things based on nRF24L01[J]. Information Technology and Informatization,2021(6):132-134.
    • 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(10)  / Tables(6)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (124) PDF downloads(22) 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