LIU Yong, CUI Hongqing. Research on coal-bed image fractures identification based on fracture shape characteristics[J]. Journal of Mine Automation, 2017, 43(10): 59-64. DOI: 10.13272/j.issn.1671-251x.2017.10.012
Citation: LIU Yong, CUI Hongqing. Research on coal-bed image fractures identification based on fracture shape characteristics[J]. Journal of Mine Automation, 2017, 43(10): 59-64. DOI: 10.13272/j.issn.1671-251x.2017.10.012

Research on coal-bed image fractures identification based on fracture shape characteristics

More Information
  • In view of problems that existing coal-bed image fractures identification method does not take good account of shape characteristics of the fractures or overall information of the fractures is not well obtained, by analyzing coal-bed image and shape characteristics of the fractures in binary image under different gray thresholds, determination coefficient of the coal-bed fractures was defined, and regio's length, width and the ratio of length to width in the coal-bed binary image were defined as shape parameters. At the same time, a method of the coal-bed image fractures identification based on fracture shape characteristics was proposed. Under condition of the given shape parameters, travel a given gray scale threshold range and identify fracture regions in every coal-bed binary image, then merge all the identified fracture regions as the last coal-bed fractures identification result. Finally, an example was given to demonstrate effectiveness of the method. It is concluded that information of the coal-bed image fractures can be acquired to the maximum extent by traveling a certain gray scale threshold range and identify the coal-bed image fractures, and it can effectively improve accuracy of the coal-bed fracture identification by selecting reasonable thresholds of the shape parameters of the fracture determining coefficient.
  • Related Articles

    [1]YAN Guofeng, HUANG Xingli, YAN Zhenguo. Research on exothermic and kinetic characteristics of low-temperature oxidation of preoxidized coal[J]. Journal of Mine Automation, 2022, 48(7): 135-141. DOI: 10.13272/j.issn.1671-251x.2022030032
    [2]HOU Fei, CAO Weihu, WANG Yi, ZHONG Xiaoxing. Comparative study on test methods of coal low-temperature oxidation kinetic parameters[J]. Journal of Mine Automation, 2021, 47(9): 58-64. DOI: 10.13272/j.issn.1671-251x.17812
    [3]LONG Nengzeng, YUAN Mei, AO Xuanjun, LI Xinling, ZHANG Ping. Prediction of coal and gas outburst intensity based on LLE-FOA-BP model[J]. Journal of Mine Automation, 2019, 45(10): 68-73. DOI: 10.13272/j.issn.1671-251x.2019010054
    [4]LI Shengpu, WANG Xiaohui. Study of mining method of signal of coal and gas outburst[J]. Journal of Mine Automation, 2015, 41(6): 58-60. DOI: 10.13272/j.issn.1671-251x.2015.06.014
    [5]WANG Sheguo, TIAN Zhimin, ZHANG Feng, WU Shasha. System of coal and gas outburst prediction based on improved BP neural network[J]. Journal of Mine Automation, 2014, 40(5): 34-37. DOI: 10.13272/j.issn.1671-251x.2014.05.009
    [6]HUANG Yu-feng, CUI Jian-ming, LIU Zhun, SUN Long-tao. Design of Monitoring System of Coal and Gas Outburst Based on DSP and Single-chip Microcomputer[J]. Journal of Mine Automation, 2011, 37(4): 56-59.
    [7]LI Da-feng, ZHAO Shuai, YANG Dai-ping. Forecasting Method of Coal and Gas Outburst Based on KPCA-SVM[J]. Journal of Mine Automation, 2010, 36(10): 36-38.
    [8]DU feng~, SU Heng-yu~, LI Chun-hui~. Design of Management System of Prediction to Coal and Gas Outburst Based on SuperMap[J]. Journal of Mine Automation, 2010, 36(2): 4-7.
    [9]LI Yang, SHI Bi-ming. Research of Prediction of Coal and Gas Outburst Based on BP Artificial Neural Network Utilizing Bayesian Regularizatio[J]. Journal of Mine Automation, 2009, 35(2): 1-5.
    [10]XU Jian, MA Bi. Application of Distributed Temperature Sensing System of Optical Fiber in Temperature Measurement of Freezing Overburden Section of Coal Mine[J]. Journal of Mine Automation, 2007, 33(2): 99-101.
  • Cited by

    Periodical cited type(22)

    1. 王浩. 选煤厂自动加介质系统的设计. 机械制造. 2024(02): 53-55 .
    2. 刘新辉,袁雪,吕鹏辉,雷伟刚,薛振磊,卜祥宁,沙杰. 选煤厂重介质分选工艺智能化改造及应用. 煤炭加工与综合利用. 2024(03): 10-13+17 .
    3. 申杰. 选煤厂自动化重介质分选技术的应用分析. 矿业装备. 2024(05): 198-200 .
    4. 倪云峰,魏富太,郭苹. 重介质分选过程中悬浮液密度和黏度控制算法研究. 煤炭技术. 2024(08): 296-299 .
    5. 张文军. 选煤厂生产线调度最优决策专家系统设计. 自动化仪表. 2024(07): 75-79 .
    6. 张军,蔚文朋,张硕,姜坤坤,王杰,李少宁,董良,代伟. 基于云熵优化的云模型-组合赋权煤炭分选工艺综合评价方法. 洁净煤技术. 2024(S2): 508-514 .
    7. 王美君,谭章禄,吕晗冰,桂谕典. 选煤厂智能化建设技术架构与技术策略研究. 矿业科学学报. 2024(06): 1017-1026 .
    8. 郎艳波. 重介质选煤装备的智能化设计改造及应用. 机械研究与应用. 2023(01): 136-139+143 .
    9. 班海俊,武源,张锦龙,刘诗宇,常艇. 李家壕选煤厂智能加介系统研究. 煤炭工程. 2023(04): 168-172 .
    10. 柴进,张海斌,高平小,王湛,乔宏. 基于特征融合的选煤厂振动筛故障诊断方法. 煤炭工程. 2023(06): 158-163 .
    11. 代伟,王昱栋,彭勇. 重介质选煤过程数学模型的研究现状与展望. 控制工程. 2023(10): 1759-1766 .
    12. 吴毅刚,朱陈雨. 重介质悬浮液密度的压差式测量方法研究现状及趋势. 煤炭加工与综合利用. 2023(10): 20-24+28 .
    13. 司海波. 重介质洗煤自动控制系统设计研究. 机械管理开发. 2022(08): 257-259 .
    14. 代伟,王昱栋,董良,赵跃民. 煤炭智能重介分选技术进展与探索. 工矿自动化. 2022(11): 20-26+44 . 本站查看
    15. 周增宏. 选煤厂制介及加介系统设计与应用. 陕西煤炭. 2021(01): 162-166+173 .
    16. 寇金成. 选煤厂重介质悬浮液密度控制方案优化. 山西焦煤科技. 2021(03): 41-43 .
    17. 王庆飞,齐健,王洪兵. 乌东选煤厂重介质浅槽分选系统的分选试验研究. 能源与环保. 2021(09): 260-265 .
    18. 汤优优,喻连香,陈雄. 重介质选矿技术在处理有色金属矿和非金属矿的研究现状及展望. 矿产综合利用. 2021(04): 118-124 .
    19. 王光辉,彭勇,代伟,董良,马小平. 基于灵敏度分析与增强捕食-食饵优化的重介质选煤过程动态模型. 煤炭学报. 2021(09): 2813-2823 .
    20. 邢欢,周增宏. 一种射流喷射式自动加介系统. 洁净煤技术. 2021(S1): 97-101 .
    21. 李志军,韩伟,王光辉. 基于DASCN的重介质浅槽分选灰分预测. 煤炭工程. 2021(S1): 122-126 .
    22. 钱丽霞. 选煤厂智能介质添加系统研究. 内蒙古煤炭经济. 2021(21): 55-57 .

    Other cited types(7)

Catalog

    Article Metrics

    Article views (45) PDF downloads (11) Cited by(29)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return