TIAN Yuan, WANG De-guang. Research of Performance Testing Method of Section Monitoring System for Roadheader[J]. Journal of Mine Automation, 2012, 38(4): 20-23.
Citation: TIAN Yuan, WANG De-guang. Research of Performance Testing Method of Section Monitoring System for Roadheader[J]. Journal of Mine Automation, 2012, 38(4): 20-23.

Research of Performance Testing Method of Section Monitoring System for Roadheader

More Information
  • The paper presented a performance testing method of section monitoring system according to general working principle of section monitoring system. The method measures spatial position of feature point on cutting header by setting references of roadheader and roadway, and projected spatial track of a series feature points obtained on the plane where section outline was defined according to the references of roadheader and roadway when cutting header completes section scanning under guiding to section monitoring system, so as to form a real section outline. Finally, the method compared the real section outline with the defined one according to indexes of angle error, position error and shape error to complete performance evaluation of section monitoring system. The experimental result showed that the method is effective and accurate.
  • Related Articles

    [1]TIAN Jie, YIN Xiaoqi, WEN Yicheng. Method of cutting trajectory planning of roadheader based on hybrid IWO-PSO algorithm[J]. Journal of Mine Automation, 2021, 47(12): 55-61. DOI: 10.13272/j.issn.1671-251x.2021050018
    [2]CUI Lizhen, XU Fanfei, WANG Qiaoli, GAO Lili. Underground adaptive positioning algorithm based on PSO-BP neural network[J]. Journal of Mine Automation, 2018, 44(2): 74-79. DOI: 10.13272/j.issn.1671-251x.2017090028
    [3]ZHANG Yuwu, XU Huadong, WANG Yingying. Segment data fitting method for support pressure monitoring[J]. Journal of Mine Automation, 2016, 42(2): 51-54. DOI: 10.13272/j.issn.1671-251x.2016.02.013
    [4]LI Jianhong, ZHOU Lawu, XUE Zhengshan. Comprehensive energy-saving control of asynchronous motor based on penalty functio[J]. Journal of Mine Automation, 2014, 40(4): 59-62. DOI: 10.13272/j.issn.1671-251x.2014.04.014
    [5]YOU Wen-jian, LIANG Bing, LI Yin-jun. Research of output characteristic fitting of eddy-current sensor based on radial-basis function neural network[J]. Journal of Mine Automation, 2013, 39(2): 47-50.
    [6]PAN Lei, LI Li-juan, DING Ting-ting, LIU Dui. Forecasting of Short-term Power Load Based on Improved PSO Algorithm and LS-SVM[J]. Journal of Mine Automation, 2012, 38(9): 55-59.
    [7]WANG Jian-jun, WANG Shi-ying, LEI Meng. Application of Particle Swarm Optimization Algorithm in Prediction of Coal Calorific Value[J]. Journal of Mine Automation, 2012, 38(5): 50-53.
    [8]GONG Wen, LI Long-qing, XU Yong-gang. Evaluation of Coal Mine Informatization Level Based on Entropy Weight Method and Grey Correlation Degree Method[J]. Journal of Mine Automation, 2012, 38(3): 23-25.
    [9]SUN Jie, HAN Yan, DUAN Yong, CUI Bao-xia. PID Neural Network Control System of Ball Mill Based on Modified PSO Algorithm[J]. Journal of Mine Automation, 2011, 37(5): 59-62.
    [10]AN Feng-shua, . Optimization of PID Controller Parameters Based on Modified Particle Swarm Optimization Algorithm[J]. Journal of Mine Automation, 2010, 36(5): 54-57.

Catalog

    Article Metrics

    Article views (64) PDF downloads (11) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return