基于加速度计的顶煤运移时间测量系统

李增林; 靳舒凯; 刘安强; 张权; 员明涛; 康俊瑄; 杨克虎

doi:10.13272/j.issn.1671-251x.2022060089

基于加速度计的顶煤运移时间测量系统

doi: 10.13272/j.issn.1671-251x.2022060089

1.
陕西陕煤曹家滩矿业有限公司, 陕西榆林　719001
2.
应急管理部信息研究院, 北京　100029
3.
中国矿业大学(北京) 机电与信息工程学院, 北京　100083

基金项目: 国家自然科学基金项目（61936008,61973307）。

详细信息

作者简介:
李增林（1973－），男，陕西富平人，高级工程师，主要从事放顶煤开采等方面的研究工作，E-mail：603042517@qq.com

通讯作者:
杨克虎（1982－），男，湖北仙桃人，教授，博士，博士研究生导师，主要从事煤矿智能开采、电力电子变换器建模与控制等方面的研究工作，E-mail：ykh@cumtb.edu.cn。

中图分类号: TD67
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出版历程
- 收稿日期: 2022-06-23
- 修回日期: 2022-07-12
- 网络出版日期: 2022-08-09

Top coal migration time measurement system based on accelerometer

1.
Shaanxi Coal Caojiatan Mining Co., Ltd., Yulin 719001, China
2.
Information Institute of Ministry of Emergency Management of the People's Republic of China, Beijing 100029, China
3.
School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

摘要

摘要: 多轮顺序记忆放煤工艺能够改善综采工作面的顶煤采出率和含矸率，但在现场应用中需要对每一轮放煤时间进行精确测定与控制。基于顶煤运移跟踪仪的自动化放煤技术在实际应用中顶煤运移跟踪仪仅作为标志点安置于顶煤中，无法获得更多的顶煤运移信息。针对上述问题，基于顶煤运移跟踪仪，设计了一种基于加速度计的顶煤运移时间测量系统，该系统包括标签、采集器及中心计算机3个部分。标签放置于顶煤内部，放煤过程中跟随顶煤运动，通过内置的加速度计实时采集比力值数据，并调用时间测量算法，实现顶煤运移情况监测，进而确定不同放煤阶段，计算得出不同阶段的顶煤运移时间信息；当标签从放煤口放出后，与刮板输送带产生碰撞，通过射频信号将顶煤运移时间信息向外发送给采集器，通过现场总线进一步传输至中心计算机，指导综采工作面现场实现多轮顺序放煤。详细介绍了顶煤运移时间测量标签的软硬件设计，实现了比力值实时采集、无线信号传输、数据存储等功能；搭建了以3D转台为核心，Gauss-Newton方法为标定算法的标定平台，完成了加速度计的标定工作，标定后的加速度计能够准确采集顶煤运移时间测量标签的比力值；根据顶煤在放煤过程中的运移特点，提出了基于阈值的时间测量算法及基于长短期记忆（LSTM）的时间测量算法。基于阈值的时间测量算法通过引入静态阈值、最大阈值实现运动阶段的时间识别；基于LSTM的时间测量算法通过识别时域下比力值矢量和的动态变化，寻找突变点，实现运动阶段的时间识别。通过标签的自由落体实验完成了2种时间测量算法的性能测试，其中时间测量方差分别为0.000 6、0.000 2，时间测量误差分别为13.07%、5.22%，满足现场顶煤运移时间测量需求，基于LSTM的时间测量算法在顶煤运移时间测量具有明显的应用优势。
- 综采工作面 /
- 放顶煤开采 /
- 多轮顺序记忆放煤 /
- 顶煤运移时间 /
- 时间测量 /
- 加速度计 /
- 传感器标定
Abstract: The multi-round sequential memory coal drawing technology can improve the recovery rate of top coal and gangue content in the fully mechanized working face. But it needs to accurately measure and control the time of each round of coal drawing in field application. In the practical application of the automatic coal drawing technology based on the top coal migration tracker, the top coal movement tracker is only used as a mark point and is arranged in the top coal. The top coal movement tracker can not obtain more top coal movement information. In view of the above problems, based on the top coal movement tracker, a top coal migration time measurement system based on accelerometer is designed. The system includes three parts: tag, collector and central computer. The label is placed inside the top coal, and moves along with the top coal in the coal drawing process. Through the built-in accelerometer, the specific force data is collected in real-time. The time measurement algorithm is called to realize the monitoring of top coal migration. Then the different coal drawing stages are determined. The top coal migration time information of different stages is calculated. When the tag is released from the coal chute, it collides with the scraper conveyor belt, and sends the top coal migration time information outward to the collector through the RF signal. The information is further transmitted to the central computer through the field bus to guide the fully mechanized working face to realize multi-round of sequential coal drawing on site. The hardware and software design of the time measurement label of top coal migration is introduced in detail. The functions of real-time acquisition of specific force value, wireless signal transmission and data storage are realized. A calibration platform with 3D turntable as the core and Gauss-Newton method as the calibration algorithm is built. The calibration of the accelerometer is completed. The calibrated accelerometer can accurately collect the specific force data of the top coal migration time measurement label. According to the migration characteristics of top coal in the process of coal drawing, the time measurement algorithm based on threshold and the time measurement algorithm based on long-term and short-term memory (LSTM) are proposed. The time measurement algorithm based on threshold realizes the time identification of motion stage by introducing static threshold and maximum threshold. The time measurement algorithm based on LSTM identifies the dynamic changes of the specific force vector sum in the time domain, finds the mutation point, and realizes the time identification of the motion stage. The performance test of the two time measurement algorithms is completed through the tag free falling experiment. The time measurement variance is 0.000 6 and 0.000 2 respectively. The time measurement error is 13.07% and 5.22% respectively. The results meet the on-site top coal migration time measurement requirements. And the time measurement algorithm based on LSTM has obvious application advantages in top coal migration time measurement.
- fully-mechanized working face /
- top coal caving mining /
- multi-round sequential memory coal drawing /
- top coal migration time /
- time measurement /
- accelerometer /
- sensor calibration

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图 1 顶煤运移时间测量系统结构

Figure 1. Structure of top coal migration time measurement system

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图 2 时间测量标签硬件框图

Figure 2. Hardware block diagram of time measurement label

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图 3 时间测量标签实物

Figure 3. Material object of time measurement label

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图 4 时间测量标签软件设计

Figure 4. Time measurement label software design

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图 5 未标定加速度计的比力值矢量和F测量曲线

Figure 5. Specific force vector sum F measurement curve of non-calibrated accelerometer

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图 6 标定平台

Figure 6. Calibration platform

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图 7 标定前后比力值矢量和F的测量曲线

Figure 7. Specific force vector sum F measurement curve before and after calibration

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图 8 放煤过程中比力值矢量和F模拟曲线

Figure 8. Specific force vector sum F simulating curve in the coal drawing process

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图 9 损失值变化曲线

Figure 9. Loss value curve

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图 10 LSTM算法识别曲线突变点

Figure 10. Mutation points of identification curve based on LSTM algorithm

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表 1 标定参数设置

Table 1. Calibration parameter setting

参数	数值
采样次数	100
迭代步数itmax	10 000
收敛阈值ε	1×10⁻¹⁰
初始值	[1,1,1,0,0,0,0,0,0]

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表 2 标定前后比力值矢量和F对比

Table 2. Comparison of specific force vector sum F before and after calibration

比力值矢量和	标定前	标定后
平均值/g	1.016 739	0.999 988
方差	0.013 154	0.000 004 7

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表 3 时间测量标签静止实验

Table 3. Stationary experiment of time measurement label

标签	标签姿势	比力值矢量和
标签	标签姿势	平均值/g	方差
标签1	1	1.013228	4.05×10⁻⁵
	2	1.019693	1.59×10⁻⁵
	3	1.000189	3.46×10⁻⁵
	4	1.000916	2.61×10⁻⁵
标签2	1	0.996145	2.82×10⁻⁵
	2	1.008506	2.76×10⁻⁵
	3	0.99687	1.52×10⁻⁵
	4	1.006726	1.76×10⁻⁵
标签3	1	0.986678	2.81×10⁻⁵
	2	0.966817	1.87×10⁻⁵
	3	1.031543	2.57×10⁻⁵
	4	0.996493	4.03×10⁻⁵

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表 4 时间测量标签自由落体实验

Table 4. Free-fall experiment of time measurement label g

标签	落地时比力值矢量和
标签1	12.87,	10.10,	11.52,	15.13,	2.26,	8.47,	16.74,	4.36,	8.80,	14.14,
标签1	5.14,	15.29,	13.13,	3.46,	6.36,	14.34,	3.25,	17.38,	5.37,	13.36
标签2	13.91,	8.34,	16.37,	2.06,	2.83,	19.30,	13.45,	13.22,	15.93,	5.46,
标签2	6.23,	11.71,	2.29,	11.97,	17.03,	16.92,	15.00,	9.41,	8.18,	7.30
标签3	14.52,	2.46,	21.63,	2.28,	21.25,	15.88,	12.28,	22.32,	9.59,	16.71,
标签3	13.22,	15.65,	5.02,	5.27,	6.05,	14.62,	20.55,	12.19,	18.69,	11.90

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表 5 基于阈值的时间测量算法的测量结果

Table 5. Time measurement results of time measurement algorithm based on state threshold ms

标签	下落时间
标签1	510.9,	529.9,	509.0,	513.0,	487.9,	487.9,	529.9,	509.0,	510.9,	490.0,
标签1	487.9,	552.9,	532.0,	553.0,	510.9,	513.0,	490.0,	490.0,	487.9,	472.9
标签2	515.0,	515.0,	463.9,	509.0,	467.0,	529.9,	509.0,	509.0,	551.0,	490.0,
标签2	510.9,	469.0,	490.0,	487.9,	509.0,	552.0,	470.9,	490.0,	510.9,	490.0
标签3	529.9,	509.0,	536.0,	469.0,	534.0,	552.0,	529.9,	532.0,	555.0,	555.0,
标签3	509.0,	532.0,	487.9,	467.0,	509.0,	551.0,	529.9,	509.0,	533.9,	509.0

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表 6 基于LSTM的时间测量算法的时间测量结果

Table 6. Time measurement results of time measurement algorithm based on LSTM algorithm ms

标签	下落时间
标签1	466.4,	484.7,	493.6,	484.7,	492.1,	466.7,	484.7,	475.7,	493.7,	484.7,
标签1	475.8,	466.8,	475.8,	475.7,	493.7,	484.5,	493.9,	484.8,	484.9,	484.8
标签2	475.6,	493.4,	494.0,	457.5,	448.8,	448.8,	440.1,	457.8,	457.8,	457.9,
标签2	475.5,	458.0,	475.7,	475.7,	479.1,	457.7,	484.6,	475.8,	466.5,	484.9
标签3	466.4,	484.7,	493.6,	484.7,	492.1,	466.7,	484.7,	475.7,	493.7,	484.7,
标签3	475.8,	466.8,	475.8,	475.7,	493.7,	484.5,	493.9,	484.8,	484.9,	484.8

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参考文献(13)

[1]	刘温飞. 放顶煤采煤工艺分析[J]. 矿业装备,2021(1):8-9. doi: 10.3969/j.issn.2095-1418.2021.01.003 LIU Wenfei. Analysis of coal mining technology in caving coal[J]. Mining Equipment,2021(1):8-9. doi: 10.3969/j.issn.2095-1418.2021.01.003
[2]	王国法. 加快煤矿智能化建设推进煤炭行业高质量发展[J]. 中国煤炭,2021,47(1):2-10. doi: 10.3969/j.issn.1006-530X.2021.01.002 WANG Guofa. Speeding up intelligent construction of coal mine and promoting high-quality development of coal industry[J]. China Coal,2021,47(1):2-10. doi: 10.3969/j.issn.1006-530X.2021.01.002
[3]	王国法,范京道,徐亚军,等. 煤炭智能化开采关键技术创新进展与展望[J]. 工矿自动化,2018,44(2):5-12. doi: 10.13272/j.issn.1671-251x.17307 WANG Guofa,FAN Jingdao,XU Yajun,et al. Innovation progress and prospect on key technologies of intelligent coal mining[J]. Industry and Mine Automation,2018,44(2):5-12. doi: 10.13272/j.issn.1671-251x.17307
[4]	王家臣, 杨克虎, 潘卫东, 等. 一种多轮顺序分层记忆放煤方法: 202110658946.7[P]. 2022-03-01. WANG Jiachen, YANG Kehu, PAN Weidong, et al. A multi-round sequential layering memory coal drawing method: 202110658946.7[P]. 2022-03-01.
[5]	吴婕萍,李国辉. 煤岩界面自动识别技术发展现状及其趋势[J]. 工矿自动化,2015,41(12):44-49. doi: 10.13272/j.issn.1671-251x.2015.12.012 WU Jieping,LI Guohui. Development status and tendency of automatic identification technologies of coal-rock interface[J]. Industry and Mine Automation,2015,41(12):44-49. doi: 10.13272/j.issn.1671-251x.2015.12.012
[6]	郭永存,何磊,刘普壮,等. 煤矸双能X射线图像多维度分析识别方法[J]. 煤炭学报,2021,46(1):300-309. doi: 10.13225/j.cnki.jccs.2020.1626 GUO Yongcun,HE Lei,LIU Puzhuang,et al. Multi-dimensional analysis and recognition method of coal and gangue dual-energy X-ray images[J]. Journal of China Coal Society,2021,46(1):300-309. doi: 10.13225/j.cnki.jccs.2020.1626
[7]	马英. 基于尾梁振动信号采集的煤矸识别智能放煤方法研究[J]. 煤矿开采,2016,21(4):40-42,25. MA Ying. Intelligent coal caving with gangue identification based on tail beam vibration signal collection[J]. Coal Mine Mining,2016,21(4):40-42,25.
[8]	张宁波,刘长友,陈现辉,等. 综放煤矸低水平自然射线的涨落规律及测量识别分析[J]. 煤炭学报,2015,40(5):988-993. ZHANG Ningbo,LIU Changyou,CHEN Xianhui,et al. Measurement analysis on the fluctuation characteristics of low level natural radiation from gangue[J]. Journal of China Coal Society,2015,40(5):988-993.
[9]	潘卫东,李新源,员明涛,等. 基于顶煤运移跟踪仪的自动化放煤技术原理及应用[J]. 煤炭学报,2020,45(增刊1):23-30. PAN Weidong,LI Xinyuan,YUN Mingtao,et al. Technology principle and field application of automatic coal drawing based on the top coal tracker[J]. Journal of China Coal Society,2020,45(S1):23-30.
[10]	王国富,刘忠奇,叶金才,等. 基于煤矿应用的超高密度的正反演算法[J]. 煤炭技术,2017,36(8):98-100. WANG Guofu,LIU Zhongqi,YE Jincai,et al. Forward and inverse algorithm based on ultra high density in coal mine application[J]. Coal Technology,2017,36(8):98-100.
[11]	杨克虎, 郁文生. 嵌入式智能传感器: 系统标定与建模[C] //第二十九届中国控制会议论文集, 北京, 2010: 3694-3699. YANG Kehu, YU Wensheng. Embedded smart sensor: system calibration and modeling[C] // Proceedings of the 29th Chinese Control Conference, Beijing, 2010: 3694-3699.
[12]	LI Guannan, ZHAO Xiaowei, FAN Cheng, et al. Assessment of long short-term memory and its modifications for enhanced short-term building energy predictions[J/OL]. Journal of Building Engineering, 2021, 43: 103182. https://doi.org/10.1016/jobe.2021.103182.
[13]	李旭帆,安霆. 基于nRF24L01的物联网无线数据传输系统[J]. 信息技术与信息化,2021(6):132-134. LI Xufan,AN Ting. Wireless data transmission system of Internet of things based on nRF24L01[J]. Information Technology and Informatization,2021(6):132-134.