Volume 48 Issue 9
Sep.  2022
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Article Navigation >  Journal of Mine Automation  > 2022  >  48(9): 8-15, 24
XU Shichang, CHENG Gang, YUAN Dunpeng, et al. Belt conveyor deviation and coal stacking monitoring method based on three-dimensional point cloud[J]. Journal of Mine Automation，2022，48(9)：8-15, 24.  doi: 10.13272/j.issn.1671-251x.17948
Citation: XU Shichang, CHENG Gang, YUAN Dunpeng, et al. Belt conveyor deviation and coal stacking monitoring method based on three-dimensional point cloud[J]. Journal of Mine Automation，2022，48(9)：8-15, 24.  doi: 10.13272/j.issn.1671-251x.17948
shu

Belt conveyor deviation and coal stacking monitoring method based on three-dimensional point cloud

doi: 10.13272/j.issn.1671-251x.17948
  • 1.

    School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China

  • 2.

    Shandong Zhongheng Optoelectronic Technology Co., Ltd., Zaozhuang 277000, China

  • Received Date: 2022-05-12
  • Rev Recd Date: 2022-09-08
    • Available Online: 2022-09-16
    Abstract
  • Conveyor belt deviation and coal stacking are common faults of belt conveyor in the coal mine. The traditional contact conveyor belt deviation or coal stacking detection methods can not meet the requirements of coal mine safety production in terms of durability, sensitivity and reliability. However, the detection effect based on the image processing method is greatly affected by image color information, which is prone to false identification. The belt conveyor deviation and coal stacking monitoring method based on 3D point cloud is proposed. The 3D point cloud data of the conveyor belt surface is collected by line laser binocular camera. The real-time monitoring of belt deviation and coal stacking is carried out by analyzing and processing the point cloud data. In terms of conveyor belt deviation monitoring, Euclidean clustering and random sampling consistency algorithm are used to filter redundant point cloud data, and extract edge data points of the conveyor belt. The mean central characterization value is used to characterize the degree of conveyor belt deviation, so as to reduce the influence of the shape change in the width direction of the conveyor belt on the monitoring. In terms of coal stacking monitoring, the equivalent height of coal flow is obtained by processing point cloud data. The height and width information of coal flow is characterized by the equivalent height, so that the coal stacking degree is evaluated in real-time. The test bed of the belt conveyor deviation and coal stacking monitoring system is built. The test results show the following points. When the speed of the conveyor belt is 0.5-3.0 m/s, the detection error of the edge point of the conveyor belt is − 2.84-1.26 mm, and the maximum error is only 2.84 mm. It shows that the system can reliably realize the function of deviation fault monitoring and accurately predict the deviation trend. Coal samples (14-41 kg, in increments of 1 kg) are stacked on the conveyor belt. When the coal mass is within the range of 14-24 kg and 28-41 kg, the coal stacking detection results are correct. There are detection errors in the range of 25-27 kg. The reason is that the coal sample quality in this range is close to the critical value of 27.6 kg for triggering the coal stacking alarm.

     

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