Belt conveyor deviation and coal stacking monitoring method based on three-dimensional point cloud
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摘要: 输送带跑偏和堆煤是煤矿带式输送机常见故障。传统的接触式输送带跑偏或堆煤检测方法在耐用性、灵敏度、可靠性等方面无法满足煤矿安全生产要求,而基于图像处理方法的检测效果受图像颜色信息影响较大,易产生误识别问题。提出了一种基于三维点云的带式输送机跑偏及堆煤监测方法,采用线激光双目相机采集输送带表面的三维点云数据,通过分析处理点云数据对输送带跑偏和堆煤进行实时监测。在输送带跑偏监测方面,采用欧氏聚类和随机采样一致性算法滤除多余点云数据,提取输送带边沿数据点,并采用均中心表征值表征输送带跑偏程度,以减小输送带宽度方向形状变化对监测的影响。在堆煤监测方面,通过处理点云数据得到煤流等效高度来表征煤流高度和宽度信息,实时评估堆煤程度。搭建了带式输送机跑偏及堆煤监测系统试验台,试验结果表明:输送带速度为0.5~3.0 m/s时,输送带边沿点检测误差为−2.84~1.26 mm,最大误差仅为2.84 mm,说明该系统能可靠实现跑偏故障监测功能,并能准确预测跑偏趋势;在输送带上堆积煤炭样本(质量为14~41 kg,以1 kg为增量),当煤炭质量在14~24 kg及28~41 kg范围内,堆煤检测结果均正确,在25~27 kg范围内存在检测错误情况,原因是该范围内煤炭样本质量较接近触发堆煤报警的临界值27.6 kg。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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表 1 输送带跑偏趋势预测试验结果
Table 1 Prediction test results of forecasting belt deviation trend
速度/(m·s−1) 诱导跑偏方向 试验序号 预测结果 运行圈数 0.5 左 d1−1 正确 1.5 d1−2 正确 1 右 d1−3 正确 2.0 d1−4 正确 3.5 1.0 左 d2−1 错误 − d2−2 正确 2.0 右 d2−3 正确 4.0 d2−4 正确 3.5 1.5 左 d3−1 正确 1.5 d3−2 正确 2.5 右 d3−3 错误 − d3−4 错误 − 2.0 左 d4−1 正确 1.0 d4−2 正确 2.0 右 d4−3 正确 3.5 d4−4 正确 3.5 2.5 左 d5−1 正确 1.0 d5−2 正确 0.5 右 d5−3 正确 1.5 d5−4 正确 1.0 3.0 左 d6−1 正确 0.5 d6−2 正确 0.5 右 d6−3 正确 1.5 d6−4 正确 2.5 表 2 输送带堆煤试验结果(部分)
Table 2 Partial test results of coal stacking on belt
样本质量/kg 试验序号 hf_max /mm 是否触发报警 检测结果 25 s12−1 25.44 否 正确 s12−2 28.27 是 错误 s12−3 25.64 否 正确 26 s13−1 26.88 是 错误 s13−2 25.57 否 正确 s13−3 27.77 是 错误 27 s14−1 26.02 否 正确 s14−2 27.74 是 错误 s14−3 27.28 是 错误 -
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