A dynamic coal quantity detection system for conveyor belt based on ultrasonic array
-
摘要: 输送带动态煤量检测是实现顺煤流启动和自动调速等多级带式输送机能耗优化措施的基础和关键。针对现有基于超声波的煤量检测方法精度较低、多超声波传感器之间易受干扰等问题，设计了基于超声阵列的输送带动态煤量检测系统。利用超声波测距原理，通过超声阵列实时检测各超声波传感器阵元对应检测点的煤料高度，采用横截面切片法计算单位时间内输送带上通过煤料的总体积，结合煤料堆积密度计算输送带实时煤流量及总煤量。为降低同频声波串扰及超声波在井下恶劣环境中衰减带来的误差，选用10路中心频率不同的超声波传感器阵元，布置为2×5线性阵列形式，通过多行超声波传感器对采集的煤高数据进行补偿，以提高煤高数据检测准确性。实时性分析结果表明，超声阵列检测速度在理论上满足带速为5 m/s的带式输送机煤量检测要求。实验结果表明：在0.125，0.170 m/s带速下，规则物料体积检测的平均相对误差分别为4.99%，5.16%；模拟实际工况条件下，煤量检测的平均相对误差为5.56%。在低带速状态下，该系统对规则物料和煤料的测量准确度达94%以上，基本实现了输送带动态煤量实时准确检测，满足带式输送机煤量检测需求。Abstract: Dynamic coal quantity detection for conveyor belt is the foundation and key to achieving energy consumption optimization measures for multi-stage belt conveyors such as coal flow starting and automatic speed regulation. The existing coal quantity detection methods based on ultrasonic have low precision. Multiple ultrasonic sensors are susceptible to interference. In order to solve the above problems, a dynamic coal quantity detection system for conveyor belts based on ultrasonic array is designed. Using the principle of ultrasonic ranging, the coal material height corresponding to the detection points of each ultrasonic sensor array element is detected in real-time through an ultrasonic array. The cross-section slicing method is used to calculate the total volume of coal material passing through the conveyor belt per unit time. The real-time coal flow and total coal quantity of the conveyor belt are calculated based on the coal material stacking density. In order to reduce the crosstalk of the same frequency acoustic wave and the error caused by the attenuation of ultrasonic waves in harsh underground environments, 10 ultrasonic sensor arrays with different center frequencies are selected and arranged in a 2×5 linear array form. The collected coal height data is compensated through multiple rows of ultrasonic sensors to improve the accuracy of coal height data detection. The analysis results of real-time performance indicate that the ultrasonic array detection speed theoretically meets the coal quantity detection requirements of a belt conveyor with a belt speed of 5 m/s. The experimental results show that the average relative errors of regular material volume detection are 4.99% and 5.16% at belt speeds of 0.125 m/s and 0.170 m/s, respectively. Under simulated actual operating conditions, the average relative error of coal quantity detection is 5.56%. In the low belt speed state, the system has a measurement accuracy of over 94% for regular materials and coal. It basically achieves real-time and accurate detection of the dynamic coal quantity of the conveyor belt, meeting the coal quantity detection requirements of the belt conveyor.
-
-
![]()
图 3 基于超声阵列的输送带动态煤量检测系统结构
Figure 3. Structure of dynamic coal quantity detection system for conveyor belt based on ultrasonic array
![]()
图 13 模拟工况下超声波传感器测试数据曲线
Figure 13. Test data curves of ultrasonic sensors under simulated working condition
表 1 超声波传感器关键技术指标
Table 1 Key technical indicators of ultrasonic sensors
技术指标 参数 输入电压/V 15~30 输出电压/V 0~10 检测范围/mm 50~1 000 中心频率/kHz 205，255，310，380，400 声束角/(°) 10 分辨率/mm 0.35 
下载: 导出CSV
表 2 不同带速下横截面切片法测量结果
Table 2 Measurement results of cross section slicing method at different belt speeds
物料
编号带速为0.125 m/s 带速为0.170 m/s 物料体积
测量值/m3绝对
误差/m3相对
误差/%物料体积
测量值/m3绝对
误差/m3相对
误差%1 0.117 7 0.005 1 4.16 0.117 5 0.005 3 4.32 2 0.105 7 0.005 7 5.69 0.105 9 0.005 9 5.92 3 0.113 6 0.006 2 5.13 0.113 5 0.006 3 5.27 4 0.083 6 0.004 4 4.97 0.083 5 0.004 5 5.12
下载: 导出CSV
表 3 模拟工况下总煤量检测结果
Table 3 Total coal quantity detection results under simulated working conditions
实验编号 总煤量测量值/kg 绝对误差/kg 相对误差/% 1 23.747 1.247 5.54 2 23.783 1.283 5.70 3 23.749 1.249 5.55 4 23.706 1.206 5.36 5 23.771 1.271 5.65
下载: 导出CSV
-
[1] 中华人民共和国国家统计局. 中国统计年鉴[M]. 北京: 中国统计出版社, 2021. National Bureau of Statistics of the People's Republic of China. China Statistical Yearbook[M]. Beijing: China Statistics Press, 2021.
[2] 王国法,任世华,庞义辉,等. 煤炭工业“十三五”发展成效与“双碳”目标实施路径[J]. 煤炭科学技术,2021,49(9):1-8. WANG Guofa,REN Shihua,PANG Yihui,et al. Development achievements of China's coal industry during the 13 th Five-Year Plan period and future prospects[J]. Coal Science and Technology,2021,49(9):1-8.
[3] 刘宝军. 矿井带式输送机能耗优化控制系统研究[D]. 西安: 西安科技大学, 2020. LIU Baojun. Research on energy consumption optimization control system of mine belt conveyor[D]. Xi'an: Xi'an University of Science and Technology, 2020.
[4] 张雅俊,乔铁柱. 基于速度控制的多级带式输送机顺序启动方法[J]. 工矿自动化,2017,43(1):52-55. ZHANG Yajun,QIAO Tiezhu. Method of sequence start of multi-level belt conveyors based on speed control[J]. Industry and Mine Automation,2017,43(1):52-55.
[5] HILTERMANN J,LODEWIJKS G,SCHOTT D L,et al. A methodology to predict power savings of troughed belt conveyors by speed control[J]. Particulate Science & Technology,2011,29(1):14-27.
[6] GAN Hong,CHEN Kun,ZHONG Xinghong. Static analysis on the detection system of an electronic belt scale[J]. Applied Mechanics and Materials,2013,345:525-529. DOI: 10.4028/www.scientific.net/AMM.345.525
[7] DJOKORAYONO R, ARFITTARIAH, PRIANTAMA D B, et al. Design of belt conveyor flow scale using gamma radiation technique[C]. Engineering Physics International Conference, 2019.
[8] 姜玉峰,张立亚,李标,等. 基于单线激光雷达的带式输送机煤流量检测研究[J]. 煤矿机械,2022,43(8):151-153. JIANG Yufeng,ZHANG Liya,LI Biao,et al. Study on coal flow detection of belt conveyor based on single-line LiDAR[J]. Coal Mine Machinery,2022,43(8):151-153.
[9] 关丙火. 基于激光扫描的带式输送机瞬时煤量检测方法[J]. 工矿自动化,2018,44(4):20-24. GUAN Binghuo. Detection method of instantaneous coal quantity of belt conveyor based on laser scanning[J]. Industry and Mine Automation,2018,44(4):20-24.
[10] 胡而已. 基于激光扫描的综放工作面放煤量智能监测技术[J]. 煤炭科学技术,2022,50(2):244-251. HU Eryi. Intelligent monitoring technology of coal caving in fully-mechanized caving face based on laser scanning[J]. Coal Science and Technology,2022,50(2):244-251.
[11] 徐世昌,程刚,袁敦鹏,等. 基于三维点云的带式输送机跑偏及堆煤监测方法[J]. 工矿自动化,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.
[12] 顾振, 施寅生, 杨春雨. 基于边缘引导双目视觉测量的带式输送机煤量测量系统[C]. 第31届中国过程控制会议, 徐州, 2020: 250. GU Zhen, SHI Yinsheng, YANG Chunyu. Coal quantity measuring system of belt conveyor based on edge-guided binocular vision measurement[C]. The 31st Chinese Process Control Conference, Xuzhou, 2020: 250.
[13] 华明明, 葛锡聪. 基于图像处理的带式输送机煤流量监测系统[C]. 第30届全国煤矿自动化与信息化学术会议暨第11届中国煤矿信息化与自动化高层论坛, 海口, 2022: 210-218. HUA Mingming, GE Xicong. Coal flow monitoring system for belt conveyor based on image processing[C]. The 30th National Coal Mine Automation and Informatization Academic Conference and the 11th China Coal Mine Informatization and Automation High Level Forum, Haikou, 2022: 210-218.
[14] WANG Guimei, LI Xuehui, YANG Lijie. Dynamic coal quantity detection and classification of permanent magnet direct drive belt conveyor based on machine vision and deep learning[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2021, 35（11）. DOI: 10.1142/S0218001421520170.
[15] 寇金成. 基于传感器检测技术的选煤厂煤流协同控制系统设计[J]. 煤矿机电,2021,42(3):71-74. KOU Jincheng. Design of coal flow cooperative control system based on sensor detection technology[J]. Colliery Mechanical & Electrical Technology,2021,42(3):71-74.
[16] 王宗省,管振栋,张欣,等. 基于图像处理的输送带煤量动态计量研究[J]. 煤炭工程,2020,52(8):138-142. WANG Zongsheng,GUAN Zhendong,ZHANG Xin,et al. Dynamic measurement of coal quantity on belt conveyor based on visual processing[J]. Coal Engineering,2020,52(8):138-142.
[17] 贺杰,王桂梅,刘杰辉,等. 基于图像处理的皮带机上煤量体积计量[J]. 计量学报,2020,41(12):1516-1520. HE Jie,WANG Guimei,LIU Jiehui,et al. Volume measurement of coal volume on belt conveyor based on image processing[J]. Acta Metrologica Sinica,2020,41(12):1516-1520.
[18] 陈广立. 超声波煤流传感器在胶带变频调速系统中的应用[J]. 陕西煤炭,2016,35(6):83-86. CHEN Guangli. Application of ultrasonic coal flow sensor in variable frequency speed regulation system[J]. Shaanxi Coal,2016,35(6):83-86.
[19] 杨栋,江虹,罗颖,等. 基于FPGA的多路传感信号采集系统设计[J]. 仪表技术与传感器,2022(6):75-79. YANG Dong,JIANG Hong,LUO Ying,et al. Design of multi-channel sensor signal acquisition system based on FPGA[J]. Instrument Technique and Sensor,2022(6):75-79.
[20] 黄平,杨理践,高松巍,等. 基于STM32的多通道探头阵列低频电磁检测系统研究[J]. 仪表技术与传感器,2021(9):38-42,53. HUANG Ping,YANG Lijian,GAO Songwei,et al. Research on low-frequency electromagnetic detection system with multi-channel probe array based on STM32[J]. Instrument Technique and Sensor,2021(9):38-42,53.
[21] 王甲福,常海天,刘志远. 基于Qt的非接触测量系统软件设计[J]. 长春工业大学学报,2018,39(1):51-55. WANG Jiafu,CHANG Haitian,LIU Zhiyuan. Software design of a non-contact measurement system based on Qt[J]. Journal of Changchun University of Technology,2018,39(1):51-55.
-
期刊类型引用(12)
1. 赵亚东，马腾飞，思旺斗，王猛. 煤矿井下移动机器人同步定位关键技术研究. 煤矿机械. 2024(02): 48-51 . 
百度学术
2. 司垒，王忠宾，魏东，顾进恒，闫海峰，谭超，朱远胜. 基于IMU-LiDAR紧耦合的煤矿防冲钻孔机器人定位导航方法. 煤炭学报. 2024(04): 2179-2194 . 百度学术
3. 高毅楠，姚顽强，蔺小虎，郑俊良，马柏林，冯玮，高康洲. 煤矿井下多重约束的视觉SLAM关键帧选取方法. 煤炭学报. 2024(S1): 472-482 . 百度学术
4. 刘敬东，李旭，于凤启，苟丙荣，贺国庆，巩泽文. 激光SLAM技术在巷道精细建模的应用研究. 煤矿机械. 2024(10): 199-202 . 百度学术
5. 黄晨烜，常健，王雷. 基于激光雷达的井下带式输送机边缘提取方法. 工矿自动化. 2024(09): 115-123 . 本站查看
6. 胡青松，李敬雯，张元生，李世银，孙彦景. 面向矿井无人驾驶的IMU与激光雷达融合SLAM技术. 工矿自动化. 2024(10): 21-28 . 本站查看
7. 崔邵云，鲍久圣，胡德平，袁晓明，张可琨，阴妍，王茂森，朱晨钟. SLAM技术及其在矿山无人驾驶领域的研究现状与发展趋势. 工矿自动化. 2024(10): 38-52 . 本站查看
8. 马亮，高亮，廉博翔，张琦，蔺小虎，姜之跃. 基于已知点约束的高精度煤矿巷道三维点云建模方法. 工矿自动化. 2024(11): 78-83+151 . 本站查看
9. 夏建超，周亮亮，陈仁. 恶劣环境下钢包脱挂钩状态自动识别技术研究. 重型机械. 2023(04): 62-67 . 百度学术
10. 高海跃，王凯，王保兵，王丹丹. 基于全局点云地图的煤矿井下无人机定位方法. 工矿自动化. 2023(08): 81-87+133 . 本站查看
11. 程健，李昊，马昆，刘斌，孙大智，马永壮，殷罡，王广福，李和平. 矿井视觉计算体系架构与关键技术. 煤炭科学技术. 2023(09): 202-218 . 百度学术
12. 李少安，刘欣，郭长鑫，王博，丁浩然，李晓健. 基于激光雷达自主定位导航的多功能机器人. 无线互联科技. 2023(17): 54-57 . 百度学术
其他类型引用(6)
计量
- 文章访问数: 261
- HTML全文浏览量: 66
- PDF下载量: 28
- 被引次数: 18
