基于三维点云的带式输送机跑偏及堆煤监测方法

徐世昌; 程刚; 袁敦鹏; 孙旭; 金祖进; 李勇

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

基于三维点云的带式输送机跑偏及堆煤监测方法

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

徐世昌^1,,
程刚^{1, 2},
袁敦鹏¹,
孙旭¹,
金祖进¹,
李勇¹

1.
中国矿业大学机电工程学院, 江苏徐州　221116
2.
山东中衡光电科技有限公司, 山东枣庄　277000

基金项目: 江苏高校优势学科建设工程资助项目。

详细信息

作者简介:
徐世昌（1992—），男，山西太原人，博士研究生，主要研究方向为带式输送机智能测控，E-mail：TB19050022B2@cumt.edu.cn

中图分类号: TD634
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出版历程
- 收稿日期: 2022-05-12
- 修回日期: 2022-09-08
- 网络出版日期: 2022-09-16

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

1.
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
2.
Shandong Zhongheng Optoelectronic Technology Co., Ltd., Zaozhuang 277000, China

摘要

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。
- 带式输送机 /
- 输送带跑偏监测 /
- 跑偏趋势预测 /
- 堆煤监测 /
- 线激光双目相机 /
- 三维点云
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.
- belt conveyor /
- belt deviation monitoring /
- deviation trend prediction /
- coal stacking monitoring /
- line laser binocular camera /
- 3D point cloud

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图 1 带式输送机跑偏及堆煤监测系统

Figure 1. Deviation and coal stacking monitoring system for belt conveyor

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图 2 相机布置位置不同时激光线扫描获取的点云图像

Figure 2. Point cloud images obtained by laser line scanning when camera is at different positions

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图 3 欧氏聚类前后输送带点云可视化对比

Figure 3. Visual comparison of belt point cloud before and after Euclidean clustering

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图 4 RANSAC处理前后输送带左侧分段点云可视化对比

Figure 4. Visual comparison of point cloud of left belt segment before and after random sampling consistency processing

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图 5 输送带左右边沿表征

Figure 5. The left and right edges characterizations of belt

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图 6 输送带中心表征值和均中心表征值变化

Figure 6. Change trend of central characterization value and mean central characterization value

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图 7 输送带堆煤前后对比

Figure 7. Comparison of coal flow images and point cloud visualization before and after coal stacking on belt

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图 8 带式输送机跑偏及堆煤监测系统人机界面

Figure 8. Human-machine interface of deviation and coal stacking monitoring system for belt conveyor

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图 9 输送带跑偏

Figure 9. Belt deviation

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图 10 输送带边沿点检测相对误差

Figure 10. Relative errors of belt edge points detection

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图 11 试验d2−1输送带中心表征值变化

Figure 11. Central characterization value deviation change of belt in test d2-1

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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
	左	d1−2	正确	1
	右	d1−3	正确	2.0
	右	d1−4	正确	3.5
1.0	左	d2−1	错误	−
	左	d2−2	正确	2.0
	右	d2−3	正确	4.0
	右	d2−4	正确	3.5
1.5	左	d3−1	正确	1.5
	左	d3−2	正确	2.5
	右	d3−3	错误	−
	右	d3−4	错误	−
2.0	左	d4−1	正确	1.0
	左	d4−2	正确	2.0
	右	d4−3	正确	3.5
	右	d4−4	正确	3.5
2.5	左	d5−1	正确	1.0
	左	d5−2	正确	0.5
	右	d5−3	正确	1.5
	右	d5−4	正确	1.0
3.0	左	d6−1	正确	0.5
	左	d6−2	正确	0.5
	右	d6−3	正确	1.5
	右	d6−4	正确	2.5

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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	h_{f_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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