煤岩识别技术发展综述

贺艳军; 李海雄; 胡淼龙; 薛竞飞

doi:10.13272/j.issn.1671-251x.18149

煤岩识别技术发展综述

doi: 10.13272/j.issn.1671-251x.18149

1.
国能包头能源有限责任公司，内蒙古包头　014070
2.
榆林市能源局，陕西榆林　719000
3.
浙江维思无线网络技术公司，浙江嘉兴　314001
4.
西安科技大学电气与控制工程学院，陕西西安　710054

基金项目: 陕西省秦创原“科学家+工程师”队伍建设项目（2022KXJ-38）。

详细信息

作者简介:
贺艳军（1989—），男，内蒙古包头人，工程师，研究方向为煤矿自动化技术，E-mail：heyanjun2366@163.com

中图分类号: TD67
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- 被引次数: 0
出版历程
- 收稿日期: 2023-08-28
- 修回日期: 2023-12-04
- 网络出版日期: 2023-12-11

Overview of the development of coal rock recognition technology

1.
CHN Energy Baotou Energy Co., Ltd., Baotou 014070, China
2.
Yulin Energy Bureau, Yulin 719000, China
3.
Wins Wireless Network Technology Ltd., Jiaxing 314001, China
4.
College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an 710054, China

摘要

摘要:
煤岩识别技术可为采煤机自动调高提供依据，是实现煤矿智能无人化开采的关键。现有煤岩识别技术包括图像识别、过程信号监测识别、电磁波识别、超声波探测识别、多传感器融合识别等。详细介绍了上述几种技术原理及应用现状：① 图像识别技术目前处于实验阶段，主要涉及大规模煤岩图像数据标注和复杂地质条件下的识别问题。② 过程信号监测识别技术可分析煤矿开采过程中的相关信号，识别潜在的煤岩界面信息，但需要解决信号噪声干扰和复杂煤岩界面识别问题。③ 电磁波识别技术和超声波探测识别技术已在实际煤岩界面探测中应用，但仍需要提高识别准确性和可靠性，尤其是对于复杂煤岩结构和界面情况。④ 多传感器融合识别技术需解决数据融合和匹配的难题，确保不同传感器之间的精确校准和可靠性，并验证其在实际应用中的可行性和实用性。针对上述问题，指出煤岩识别技术发展方向：① 煤岩识别研究应着重提高算法的实时性和抗干扰能力，确保在特定条件下并兼有复杂环境干扰下也能准确识别煤岩，满足井下实际开采需求。② 加强矿用传感器的研究，以提高其抗干扰性能，同时采用先进的视觉相机和智能设备，与传感器相结合，提高煤岩识别的精度和效率。③ 多种煤岩识别技术交叉融合使用：对于不同硬度的煤岩，可采用过程信号监测识别和多传感器融合技术；对于硬度接近的情况，可结合图像识别和电磁波识别技术，实现煤岩壁界面和煤层厚度的准确识别。
- 煤岩识别 /
- 采煤机滚筒 /
- 图像识别 /
- 过程信号监测识别 /
- 电磁波识别 /
- 超声波探测识别 /
- 多传感器融合识别
Abstract:
Coal rock recognition technology can provide a basis for automatization improvement of shearer and is the key to achieving intelligent unmanned mining in coal mines. The existing coal rock recognition technologies include image recognition, process signal monitoring recognition, electromagnetic wave recognition, and ultrasonic detection recognition, multi-sensor fusion recognition. This article provides a detailed introduction to the principles and application status of the above-mentioned technologies. ① Image recognition technology is currently in the experimental stage, mainly involving large-scale coal rock image data annotation and recognition problems under complex geological conditions. ② Process signal monitoring and recognition technology can analyze relevant signals during coal mining and recognize potential coal rock interface information. But it needs to solve the problems of signal noise interference and complex coal rock interface recognition. ③ Electromagnetic wave recognition technology and ultrasonic detection recognition technology have been applied in actual coal rock interface detection. But there is still a need to improve recognition accuracy and reliability, especially for complex coal rock structures and interface situations. ④ Multi sensor fusion recognition technology needs to solve the problem of data fusion and matching, ensure accurate calibration and reliability between different sensors, and verify its feasibility and practicality in practical applications. In order to solve the above problems, the development directions of coal rock recognition technology are pointed out. ① Research on coal rock recognition should focus on improving the real-time performance and anti-interference capability of algorithms. It will ensure accurate recognition of coal rock under specific conditions and complex environmental interference, and meet the actual mining needs underground. ② Research on coal rock recognition should strengthen the research on mining sensors to improve their anti-interference performance. It is suggested to adopt advanced visual cameras and intelligent devices to combine with sensors to improve the precision and efficiency of coal rock recognition. ③ Research on coal rock recognition should focus on the cross fusion of multiple coal and rock recognition technologies. For coal and rock with different hardness, process signal monitoring recognition and multi-sensor fusion technology can be adopted. For cases with similar hardness, image recognition and electromagnetic wave recognition techniques can be combined to achieve accurate recognition of coal rock wall interfaces and coal seam thickness.
- coal rock recognition /
- shearer drum /
- image recognition /
- process signal monitoring and recognition /
- electromagnetic wave recognition /
- ultrasonic detection and recognition /
- multi sensor fusion recognition

HTML全文

图 1 常见煤岩介质相对介电常数

Figure 1. Relative dielectric constant of common coal rock media

下载: 全尺寸图片幻灯片

图 2 常见煤岩介质电导率

Figure 2. Electrical conductivity of common coal rock media

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图 3 红外光谱识别流程

Figure 3. Infrared spectrum recognition process

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图 4 多传感器融合模型

Figure 4. Multi-sensor fusion model

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表 1 过程信号监测识别技术特点汇总

Table 1. Summary of technical features of process signal monitoring and recognition

信号	缺点	优点
振动信号	对煤岩硬度有要求	受采煤环境干扰小
截割力信号	多轴数据量大	可识别突出地质条件
声发射信号	数据量大，易受噪声干扰	识别率高
温度信号	因滚筒阻挡，数据难采集	识别速度快，识别率高
电流信号	易受复杂信号干扰	可很好地应对煤岩界面突变

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表 2 煤岩坚固性系数

Table 2. Coal and rock firmness coefficient

	类别	坚固性系数
煤	极硬煤层	4.0～5.0
	硬煤层	3.0～4.0
	中硬度层	1.5～3.0
	软煤层	0.8～1.5
	极软煤层	0.5～0.8
岩石	极坚固岩石	15～20
	坚硬岩石	8～10
	中等坚固岩石	4～6
	不坚固岩石	0.3

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表 3 电磁波识别技术特点汇总

Table 3. Summary of technical features of electromagnetic wave recognition

信号	缺点	优点
雷达信号	煤岩物理特征的不同会导致误判	无需预先求取煤岩物理特性，适用范围更广
γ射线信号	探测煤层厚	需含放射性顶底岩
红外光谱	元素成分相似会导致误判	识别率高，可识别夹矸层
太赫兹光谱	无法在井下复杂环境使用	特征信息充分，识别率高
高光谱	数据量大，实时性不够	特征信息充分，识别率高

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参考文献(52)

[1]	IPHAR M,CUKURLUOZ A K. Fuzzy risk assessment for mechanized underground coal mines in Turkey[J]. Journal of International Management,2020,26(2):256-271.
[2]	张婷. 基于变换域与高斯混合模型聚类的煤岩识别方法[J]. 煤炭技术,2018,37(11):320-323. ZHANG Ting. Coal and rock recognition method based on transform domain and clustering of gaussian mixture model[J]. Coal Technology,2018,37(11):320-323.
[3]	黄韶杰,刘建功. 基于高斯混合聚类的煤岩识别技术研究[J]. 煤炭学报,2015,40(增刊2):576-582. HUANG Shaojie,LIU Jiangong. Research of coal-rock recognition technology based on GMM clustering analysis[J]. Journal of China Coal Society,2015,40(S2):576-582.
[4]	吴德忠,刘泉声,黄兴,等. 基于边界跟踪和神经网络的煤岩界面识别方法研究[J]. 煤炭工程,2021,53(6):140-146. WU Dezhong,LIU Quansheng,HUANG Xing,et al. Coal-rock interface recognition method based on boundary tracking algorithm and artificial neural network[J]. Coal Engineering,2021,53(6):140-146.
[5]	LIU Chunsheng,REN Chunping. Research on coal-rock fracture image edge detection based on tikhonov regularization and fractional order differential operator[J]. Journal of Electrical & Computer Engineering,2019,2019(26):1-13.
[6]	伍云霞,田一民. 基于字典学习的煤岩图像特征提取与识别方法[J]. 煤炭学报,2016,41(12):3190-3196. WU Yunxia,TIAN Yimin. Method of coal-rock image feature extraction and recognition based on dictionary learning[J]. Journal of China Coal Society,2016,41(12):3190-3196.
[7]	伍云霞,申少飞. 基于距离度量学习的煤岩识别方法[J]. 工矿自动化,2017,43(5):22-26. WU Yunxia,SHEN Shaofei. Coal-rock recognition method based on distance metric learning[J]. Industry and Mine Automation,2017,43(5):22-26.
[8]	黄蕾,郭超亚. 基于变差函数和局部方差图的煤岩图像纹理特征提取[J]. 工矿自动化,2018,44(4):62-68. HUANG Lei,GUO Chaoya. Texture feature extraction of coal-rock image based on variogram and local variance image[J]. Industry and Mine Automation,2018,44(4):62-68.
[9]	王超,张强. 基于LBP和GLCM的煤岩图像特征提取与识别方法[J]. 煤矿安全,2020,51(4):129-132. WANG Chao,ZHANG Qiang. Coal rock image feature extraction and recognition method based on LBP and GLCM[J]. Safety in Coal Mines,2020,51(4):129-132.
[10]	SI Lei,XIONG Xiangxiang,WANG Zhongbin,et al. A deep convolutional neural network model for intelligent discrimination between coal and rocks in coal mining face[J]. Mathematical Problems in Engineering,2020. DOI: 10.1155/2020/2616510.
[11]	高峰,殷欣,刘泉声,等. 基于塔式池化架构的采掘工作面煤岩图像识别方法[J]. 煤炭学报,2021,46(12):4088-4102. GAO Feng,YIN Xin,LIU Quansheng,et al. Coal-rock image recognition method for mining and heading face based on spatial pyramid pooling structure[J]. Journal of China Coal Society,2021,46(12):4088-4102.
[12]	孙传猛,王燕平,王冲,等. 融合改进YOLOv3与三次样条插值的煤岩界面识别方法[J]. 采矿与岩层控制工程学报,2022,4(1):81-90. SUN Chuanmeng,WANG Yanping,WANG Chong,et al. Coal-rock interface identification method based on improved YOLOv3 and cubic spline interpolation[J]. Journal of Mining and Strata Control Engineering,2022,4(1):81-90.
[13]	任洁,刘頔. 基于采煤机振动时域特性的煤岩识别方法研究[J]. 煤炭工程,2016,48(3):106-109. REN Jie,LIU Di. Recognition method of coal-rock interface based on time-domain vibration characteristics of coal cutter[J]. Coal Engineering,2016,48(3):106-109.
[14]	SI Lei,WANG Zhongbin,LIU Xinhua,et al. Identification of shearer cutting patterns using vibration signals based on a least squares support vector machine with an improved fruit fly optimization algorithm[J]. Sensors,2016,16(1). DOI: 10.3390/s16010090.
[15]	张强,刘志恒,王海舰,等. 基于截齿振动及温度特性的煤岩识别研究[J]. 煤炭科学技术,2018,46(3):1-9,18. ZHANG Qiang,LIU Zhiheng,WANG Haijian,et al. Study on coal and rock identification based on vibration and temperature features of picks[J]. Coal Science and Technology,2018,46(3):1-9,18.
[16]	张启志,邱锦波,庄德玉. 基于倒谱距离的采煤机煤岩截割振动信号识别[J]. 工矿自动化,2017,43(1):9-12. ZHANG Qizhi,QIU Jinbo,ZHUANG Deyu. Vibration signal identification of coal-rock cutting of shearer based on cepstral distance[J]. Industry and Mine Automation,2017,43(1):9-12.
[17]	路红蕊,童敏明,刘栋. 基于钻头振动特性的采煤机煤岩识别研究[J]. 煤炭技术,2018,37(3):242-245. LU Hongrui,TONG Minming,LIU Dong. Research on coal-rock recognition based on vibration characteristics of coal shearer drill[J]. Coal Technology,2018,37(3):242-245.
[18]	田立勇,毛君,王启铭. 基于采煤机摇臂惰轮轴受力分析的综合煤岩识别方法[J]. 煤炭学报,2016,41(3):782-787. TIAN Liyong,MAO Jun,WANG Qiming. Coal and rock identification method based on the force of idler shaft in shearer's ranging arm[J]. Journal of China Coal Society,2016,41(3):782-787.
[19]	程诚,刘送永. 基于WPSV和BPNN的煤岩识别方法研究[J]. 煤炭工程,2018,50(1):108-112. CHENG Cheng,LIU Songyong. A coal-rock recognition method based on WPSV and BPNN[J]. Coal Engineering,2018,50(1):108-112.
[20]	田立勇,戴渤鸿,王启铭. 基于采煤机摇臂销轴多应变数据融合的煤岩识别方法[J]. 煤炭学报,2020,45(3):1203-1210. TIAN Liyong,DAI Bohong,WANG Qiming. Coal-rock identification method based on multi-strain data fusion of shearer rocker pin shaft[J]. Journal of China Coal Society,2020,45(3):1203-1210.
[21]	XU Jing,WANG Zhongbin,TAN Chao,et al. A cutting pattern recognition method for shearers based on improved ensemble empirical mode decomposition and a probabilistic neural network[J]. Sensors,2015,15(11):27721-27737. doi: 10.3390/s151127721
[22]	张强,张石磊,王海舰,等. 基于声发射信号的煤岩界面识别研究[J]. 电子测量与仪器学报,2017,31(2):230-237. ZHANG Qiang,ZHANG Shilei,WANG Haijian,et al. Study on identification of coal-rock interface based on acoustic emission signal[J]. Journal of Electronic Measurement and Instrumentation,2017,31(2):230-237.
[23]	XU Jing,WANG Zhongbin,TAN Chao,et al. Cutting pattern identification for coal mining shearer through sound signals based on a convolutional neural network[J]. Symmetry,2018,10(12). DOI: 10.3390/sym10120736.
[24]	XU Jing,WANG Zhongbin,TAN Chao,et al. Cutting pattern identification for coal mining shearer through a swarm intelligence–based variable translation wavelet neural network[J]. Sensors,2018,18(2). DOI: 10.3390/s18020382.
[25]	董玉芬,杜洪贵,任伟杰,等. 煤岩的红外信息随应力变化的实验研究[J]. 辽宁工程技术大学学报(自然科学版),2001(4):495-496. DONG Yufen,DU Honggui,REN Weijie,et al. Experimental research on infrared information varying with stress[J]. Journal of Liaoning Technical University (Natural Science Edition),2001(4):495-496.
[26]	张强,王海舰,王兆,等. 基于红外热像检测的截齿煤岩截割特性与闪温分析[J]. 传感技术学报,2016,29(5):686-692. doi: 10.3969/j.issn.1004-1699.2016.05.011 ZHANG Qiang,WANG Haijian,WANG Zhao,et al. Analysis of coal-rock's cutting characteristics and flash temperature for peak based on infrared thermal image testing[J]. Chinese Journal of Sensors and Actuators,2016,29(5):686-692. doi: 10.3969/j.issn.1004-1699.2016.05.011
[27]	张强,王海舰,郭桐,等. 基于截齿截割红外热像的采煤机煤岩界面识别研究[J]. 煤炭科学技术,2017,45(5):22-27. ZHANG Qiang,WANG Haijian,GUO Tong,et al. Study on coal-rock interface recognition of coal shearer based on cutting infrared thermal image of picks[J]. Coal Science and Technology,2017,45(5):22-27.
[28]	张强,孙绍安,张坤,等. 基于主动红外激励的煤岩界面识别[J]. 煤炭学报,2020,45(9):3363-3370. ZHANG Qiang,SUN Shao'an,ZHANG Kun,et al. Coal and rock interface identification based on active infrared excitation[J]. Journal of China Coal Society,2020,45(9):3363-3370.
[29]	曹庆春,刘帅,王怀震,等. 基于渐变信号的HHT−PCA−MRVM煤岩辨识算法[J]. 传感器与微系统,2017,36(8):138-140,144. CAO Qingchun,LIU Shuai,WANG Huaizhen,et al. HHT-PCA-MRVM coal and rock identification algorithm based on gradient signal[J]. Transducer and Microsystem Technologies,2017,36(8):138-140,144.
[30]	王元军,王明松,田山军,等. 基于卡尔曼滤波与随机森林的煤岩识别研究[J]. 煤炭技术,2021,40(12):208-211. WANG Yuanjun,WANG Mingsong,TIAN Shanjun,et al. Study on recognition of coal and rock based on Kalman filter and random forest[J]. Coal Technology,2021,40(12):208-211.
[31]	李亮,王昕,胡克想,等. 探地雷达探测煤岩界面的方法与试验[J]. 工矿自动化,2015,41(9):8-11. LI Liang,WANG Xin,HU Kexiang,et al. Coal-rock interface detection method using ground penetrating radar and its experiment[J]. Industry and Mine Automation,2015,41(9):8-11.
[32]	刘万里,马修泽,张学亮. 基于探地雷达的特厚煤层厚度动态探测技术[J]. 煤炭学报,2021,46(8):2706-2714. LIU Wanli,MA Xiuze,ZHANG Xueliang. Dynamic detection technology of extra-thick coal seam thickness based on ground penetrating radar[J]. Journal of China Coal Society,2021,46(8):2706-2714.
[33]	苗曙光,刘晓文,李淮江,等. 基于探地雷达的煤岩界面探测数据解释方法[J]. 工矿自动化,2019,45(1):35-39. MIAO Shuguang,LIU Xiaowen,LI Huaijiang,et al. Data interpretation method of coal-rock interface detection based on ground penetrating radar[J]. Industry and Mine Automation,2019,45(1):35-39.
[34]	王增才,富强. 自然γ射线穿透煤层及支架顶梁衰减规律[J]. 辽宁工程技术大学学报,2006(6):804-807. WANG Zengcai,FU Qiang. Attenuation of natural γ ray passing throughcoal seam and hydraulic support[J]. Journal of Liaoning Technical University,2006(6):804-807.
[35]	YANG Zengfu,WANG Zengcai,YAN Ming. Performance analysis of natural γ-ray coal seam thickness sensor and its application in automatic adjustment of shearer's arms[J]. Journal of Electrical and Computer Engineering,2020. DOI: 10.1155/2020/5986013.
[36]	WU Fangwei,HUANG Shuliang,LIU Junjie,et al. Optimized fuzzy C-means clustering algorithm for the interpretation of the near-infrared spectra of rocks[J]. Spectroscopy Letters,2017,50(5):270-274. doi: 10.1080/00387010.2017.1317271
[37]	YANG En,GE Shirong,WANG Shibo. Characterization and identification of coal and carbonaceous shale using visible and near-infrared reflectance spectroscopy[J]. Journal of Spectroscopy,2018. DOI: 10.1155/2018/2754908.
[38]	向阳,王世博,葛世荣,等. 粉尘环境下典型煤岩近红外光谱特征及识别方法[J]. 光谱学与光谱分析,2020,40(11):3430-3437. XIANG Yang,WANG Shibo,GE Shirong,et al. Study on near-infrared spectrum features and identification methods of typical coal-rock in dust environment[J]. Spectroscopy and Spectral Analysis,2020,40(11):3430-3437.
[39]	王赛亚,王世博,葛世荣,等. 综放工作面煤岩近红外光谱特征与机理[J]. 煤炭学报,2020,45(8):3024-3032. WANG Saiya,WANG Shibo,GE Shirong,et al. Near-infrared spectrum characteristics and mechanism of coal and rock in mechanized caving face[J]. Journal of China Coal Society,2020,45(8):3024-3032.
[40]	WANG Xin,HU Kexiang,ZHANG Lei,et al. Characterization and classification of coals and rocks using terahertz time-domain spectroscopy[J]. Journal of Infrared,Millimeter and Terahertz Waves,2017,38(2):248-260. doi: 10.1007/s10762-016-0317-2
[41]	YU Jing,WANG Xin,DING Enjie,et al. A novel method of on-line coal-rock interface characterization using THz-TDs[J]. IEEE Access,2021(9):25898-25910.
[42]	刘颖,梁楠楠,李大湘,等. 基于光谱距离聚类的高光谱图像解混算法[J]. 计算机应用,2019,39(9):2541-2546. LIU Ying,LIANG Nannan,LI Daxiang,et al. Hyperspectral image unmixing algorithm based on spectral distance clustering[J]. Journal of Computer Applications,2019,39(9):2541-2546.
[43]	SHAO Hui,CHEN Yuwei,YANG Zhirong,et al. A 91-channel hyperspectral LiDAR for coal/rock classification[J]. IEEE Geoscience and Remote Sensing Letters,2019,17(6):1052-1056.
[44]	张旭辉,张楷鑫,张超,等. 基于CARS与PCA的高光谱煤岩特征信息检测方法[J]. 西安科技大学学报,2020,40(5):760-768. ZHANG Xuhui,ZHANG Kaixin,ZHANG Chao,et al. Coal and rock feature detection method based on CARS and PCA[J]. Journal of Xi'an University of Science and Technology,2020,40(5):760-768.
[45]	韦任,徐良骥,孟雪莹,等. 基于高光谱特征吸收峰的煤岩识别方法[J]. 光谱学与光谱分析,2021,41(6):1942-1948. WEI Ren,XU Liangji,MENG Xueying,et al. Coal and rock identification method based on hyper spectral feature absorption peak[J]. Spectroscopy and Spectral Analysis,2021,41(6):1942-1948.
[46]	LIU Guanhua,LIU Zhentang,FENG Junjun,et al. Experimental research on the ultrasonic attenuation mechanism of coal[J]. Journal of Geophysics & Engineering,2017,14(3):502-512.
[47]	WEI Wei,LI Li,SHI Wanfa,et al. Ultrasonic imaging recognition of coal-rock interface based on the improved variational mode decomposition[J]. Measurement,2021,170(1):1-12.
[48]	李力,欧阳春平. 基于超声相控阵的煤岩界面识别研究[J]. 中国矿业大学学报,2017,46(3):485-492. LI Li,OUYANG Chunping. Research on coal-rock interface recognition based on ultrasonic phased array[J]. Journal of China University of Mining & Technology,2017,46(3):485-492.
[49]	张强,王海舰,井旺,等. 基于模糊神经网络信息融合的采煤机煤岩识别系统[J]. 中国机械工程,2016,27(2):201-208. doi: 10.3969/j.issn.1004-132X.2016.02.010 ZHANG Qiang,WANG Haijian,JING Wang,et al. Shearer's coal-rock recognition system based on fuzzy neural network information fusion[J]. China Mechanical Engineering,2016,27(2):201-208. doi: 10.3969/j.issn.1004-132X.2016.02.010
[50]	SI Lei,WANG Zhongbin,JIANG Gan. Fusion recognition of shearer coal-rock cutting state based on improved RBF neural network and D-S evidence theory[J]. IEEE Access,2019,7(7):122106-122121.
[51]	LIU Yanbing,DHAKAL S,HAO Binyao. Coal and rock interface identification based on wavelet packet decomposition and fuzzy neural network[J]. Journal of Intelligent Fuzzy Systems:Applications in Engineering and Technology,2020,38(4):3949-3959. doi: 10.3233/JIFS-179620
[52]	王海舰,黄梦蝶,高兴宇,等. 考虑截齿损耗的多传感信息融合煤岩界面感知识别[J]. 煤炭学报,2021,46(6):1995-2008. WANG Haijian,HUANG Mengdie,GAO Xingyu,et al. Coal-rock interface recognition based on multi-sensor information fusion considering pick wear[J]. Journal of China Coal Society,2021,46(6):1995-2008.