LI Bingrui, WANG Wei, CHEN Fengmei, LIU Na. Optimal arrangement of wind speed sensor based on directed path matrix method[J]. Journal of Mine Automation, 2021, 47(5): 52-57. DOI: 10.13272/j.issn.1671-251x.2020110066
Citation: LI Bingrui, WANG Wei, CHEN Fengmei, LIU Na. Optimal arrangement of wind speed sensor based on directed path matrix method[J]. Journal of Mine Automation, 2021, 47(5): 52-57. DOI: 10.13272/j.issn.1671-251x.2020110066

Optimal arrangement of wind speed sensor based on directed path matrix method

More Information
  • The existing mine wind speed sensor arrangement methods have problems as follows. The determined sensor branch cannot measure the wind speed accurately because the wind speed is smaller than the sensor start wind speed. Most of the methods need to be listed multiple matrices and the calculation is complicated. Moreover, the sensor positions selected by some methods are unreasonable. In order to achieve mine full coverage air volume monitoring without blind area, and to monitor the air volume variation in all roadways with the minimum number of wind speed sensors, the coverage of sensor branches is analyzed by using the directed path matrix, and the optimal arrangement of wind speed sensors based on the directed path matrix method is proposed. This method determines the unique directed path matrix based on the wind flow direction of the ventilation network diagram, determines the coverage of the branches, and selects the branch with the largest coverage to determine the position of the wind speed sensor. The results show that the optimal arrangement of wind speed sensors based on directed path matrix method can achieve mine full coverage air volume monitoring without blind area, and the number of sensors is less than or equal to the number of independent directed paths. Calculation analysis shows that when sensors are arranged according to this method, there is a measurement error of 6% in one sensor branch, the lowest impact on the ventilation network is 0.52, and the lowest impact on other branches is 0. Moreover, the calculation error decreases as the number of sensors increases. If the impact of sensor branch error on the ventilation network is controlled to be less than 1, more than 12 wind speed sensors should be arranged.
  • Related Articles

    [1]WU Yulun, XIAO Tannan, CHEN Ying. Fault diagnosis method for substations based on fault enumeration tree to generate fuzzy Petri net[J]. Journal of Mine Automation, 2025, 51(1): 85-94. DOI: 10.13272/j.issn.1671-251x.18233
    [2]SHI Zhiyuan, TENG Hu, MA Chi. Fault diagnosis of planetary gearbox based on multi-information fusion and convolutional neural network[J]. Journal of Mine Automation, 2022, 48(9): 56-62. DOI: 10.13272/j.issn.1671-251x.2022060011
    [3]WU Chuanlong, CHEN Wei, LIU Xiaowen, SHI Xinguo, LIU Ke, REN Xiaohong. Feature fusion based fault diagnosis of hoist inverter[J]. Journal of Mine Automation, 2021, 47(5): 46-51. DOI: 10.13272/j.issn.1671-251x.17772
    [4]JU Chen, ZHANG Chao, FAN Hongwei, ZHANG Xuhui, YANG Yiqing, YAN Yang. Rolling bearing fault diagnosis based on wavelet packet decomposition and PSO-BPN[J]. Journal of Mine Automation, 2020, 46(8): 70-74. DOI: 10.13272/j.issn.1671-251x.2019120022
    [5]LI Shiguang, XUE Han, LI Zhen, GAO Zhengzhong, LI Ying. Fault diagnosis of mine-used transformer based on optimized fuzzy Petri net[J]. Journal of Mine Automation, 2017, 43(5): 54-57. DOI: 10.13272/j.issn.1671-251x.2017.05.013
    [6]SUN Huiying, LIN Zhongpeng, HUANG Can, CHEN Peng. Fault diagnosis of mine ventilator based on improved BP neural network[J]. Journal of Mine Automation, 2017, 43(4): 37-41. DOI: 10.13272/j.issn.1671-251x.2017.04.009
    [7]GONG Maofa, LIU Yanni, WANG Laihe, ZHANG Chao, HOU Linyua. Fault diagnosis of mine hoist based on optimizing fuzzy Petri networks[J]. Journal of Mine Automation, 2016, 42(7): 50-53. DOI: 10.13272/j.issn.1671-251x.2016.07.012
    [8]GAO Zhengzhong, GONG Qunying, ZHAO Lina, XU Huanqi, XIAO Jiayi. Fault diagnosis of underground water pump based on fuzzy Petri net and condition monitoring[J]. Journal of Mine Automation, 2016, 42(5): 28-31. DOI: 10.13272/j.issn.1671-251x.2016.05.007
    [9]HU Wei, LI Ou. Application of fuzzy information fusion in fault diagnosis of belt conveyor[J]. Journal of Mine Automation, 2013, 39(6): 48-51.
    [10]WANG Qi-jun~, CHENG Jiu-long~. Research of Fault Diagnosis of Gas Sensor Based on Information Fusion Technology[J]. Journal of Mine Automation, 2008, 34(2): 22-25.

Catalog

    Article Metrics

    Article views (120) PDF downloads (11) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return