综采工作面采煤机智能化技术研究现状

赵亦辉; 赵友军; 周展

doi:10.13272/j.issn.1671-251x.2021090024

综采工作面采煤机智能化技术研究现状

doi: 10.13272/j.issn.1671-251x.2021090024

西安煤矿机械有限公司, 陕西西安 710200

详细信息

作者简介:
赵亦辉(1981-),男,山西运城人,高级工程师,硕士,主要从事煤矿装备自动化、智能化方面的研发工作,E-mail:xamjzhaoyihui@163.com。

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出版历程
- 收稿日期: 2021-09-07
- 修回日期: 2022-02-11
- 网络出版日期: 2022-03-01

Research status of intelligent technology of shearer in fully mechanized working face

Xi'an Coal Mining Machinery Co., Ltd., Xi'an 710200, China

摘要

摘要: 介绍了国内外采煤机智能化技术研究现状：国外从20世纪90年代起，采煤机智能化技术进入成熟发展阶段，在采煤机记忆截割、煤岩识别、机载主控软件及远程监控等方面取得引领性创新突破；国内采煤机智能化发展从引进吸收到自主创新，基本实现初级智能化综采作业。按照采煤机功能的不同进行智能化横向分类，分为采煤机智能感知、智能控制、智能诊断及智能通信4类：智能感知关键技术包括位姿感知、运行环境状态感知、机载视频感知、人员临近识别、智能防碰撞检测、直线度感知、煤岩识别感知；智能控制关键技术包括滚筒自动调高控制、自适应调速控制、环境瓦斯联动控制、煤流负载平衡控制、俯仰导向控制；智能诊断关键技术包括实时在线诊断技术和采煤机全生命周期管理；智能通信关键技术包括有线通信技术和无线通信技术。根据采煤机割煤过程中的人为干预情况进行智能化纵向分级，分为辅助自动化、初级自动化、高级自动化、智能化4个等级。通过采煤机智能化分类分级，可以直观地查阅采煤机智能化功能，并可通过判断条件确定采煤机所处智能化等级，为智能化矿井建设评级提供量化参考，同时也更清晰地展现采煤机智能化发展的脉络。
- 智能综采 /
- 采煤机智能化 /
- 智能化分类 /
- 智能化分级 /
- 智能感知 /
- 智能控制 /
- 智能诊断 /
- 智能通信
Abstract: This paper introduces the research status of intelligent technology of shearer at home and abroad. Since the 1990s, the intelligent technology of shearers has entered a mature stage of development abroad, and leading innovations have been made in shearer memory cutting, coal and rock identification, airborne main control software and remote monitoring. The intelligent development of domestic shearers has shifted from introduction and absorption to independent innovation, basically realizing primary intelligent fully mechanized mining. According to the different functions of shearers, intelligent horizontal classification is divided into four categories, intelligent perception, intelligent control, intelligent diagnosis and intelligent communication. The key technologies of intelligent perception include posture perception, operating environment state perception, airborne video perception, personnel proximity identification, intelligent anti-collision detection, straightness perception and coal rock identification perception. The key technologies of intelligent control include drum automatic height adjustment control, adaptive speed adjustment control, environmental gas linkage control, coal flow load balance control, and pitch guidance control. The key technologies of intelligent diagnosis include real-time online diagnosis technology and the whole life cycle management of shearer. The key technologies of intelligent communication include wired communication technology and wireless communication technology. According to the human intervention in the coal cutting process of shearer, intelligent longitudinal gradation is divided into four grades, auxiliary automation, primary automation, advanced automation and intelligence. Through the intelligent classification and gradation of the shearer, the intelligent function of shearer can be visually consulted, and the intelligent grade of shearer can be determined by judging conditions, which provides quantitative reference for intelligent mine construction rating, and also shows the context of intelligent development of shearer more clearly.
- intelligent fully mechanized mining /
- intelligent shearer /
- intelligent classification /
- intelligent gradation /
- intelligent perception /
- intelligent control /
- intelligent diagnosis /
- intelligent communication

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参考文献(26)

[1]	中国煤炭工业协会.2018煤炭行业发展年度报告[R].北京:中国煤炭工业协会,2019. China National Coal Association.2018 annual report on coal industry development[R].Beijing:China National Coal Association,2019.
[2]	国家能源局.关于促进煤炭工业科学发展的指导意见[N].中国煤炭报,2015-03-27(4). National Energy Administration.Guiding opinions on promoting the scientific development of the coal industry[N].China Coal News,2015-03-27(4).
[3]	国家发展改革委,国家能源局.能源技术革命创新行动计划(2016—2030年)[R/OL].[2021-08-02].http://www.gov.cn/xinwen/2016-06/01/5078628/files/d30fbe1ca23e45f3a8de7e6c563c9ec6.pdf. National Development and Reform Commission, National Energy Administration.Energy technology revolution innovation action plan (2016-2030)[R/OL].[2021-08-02].http://www.gov.cn/xinwen/2016-06/01/5078628/files/d30fbe1ca23e45f3a8de7e6c563c9ec6.pdf.
[4]	国家发展改革委,国家能源局.煤炭工业发展"十三五"规划[R/OL].[2021-08-02].http://www.gov.cn/xinwen/2016-12/30/5154806/files/358a4e8cd1ff4e3b8d5245a6b9246167.pdf. National Development and Reform Commission,National Energy Administration.The 13th five-year plan for the development of the coal industry[R/OL].[2021-08-02].http://www.gov.cn/xinwen/2016-12/30/5154806/files/358a4e8cd1ff4e3-b8d5245a6b9246167.pdf.
[5]	国家发展改革委,国家能源局,应急部,等.关于加快煤矿智能化发展的指导意见[N].中国煤炭报,2020-03-05(2). National Development and Reform Commission, National Energy Administration,Ministry of Emergency Management,et al.Guiding opinions on accelerating the intelligent development of coal mine[N].China Coal News,2020-03-05(2).
[6]	马宏伟.煤矿机电装备智能化[J].西安科技大学学报,2020,40(5):748. MA Hongwei.Intelligent coal mine electromechanical equipment[J].Journal of Xi'an University of Science and Technology,2020,40(5):748.
[7]	王国法,徐亚军,孟祥军,等.智能化采煤工作面分类、分级评价指标体系[J].煤炭学报,2020,45(9):3033-3044. WANG Guofa,XU Yajun,MENG Xiangjun,et al. Specification,classification and grading evaluation index for smart longwall mining face[J].Journal of China Coal Society,2020,45(9):3033-3044.
[8]	葛世荣,王忠宾,王世博.互联网+采煤机智能化关键技术研究[J].煤炭科学技术,2016,44(7):1-9. GE Shirong,WANG Zhongbin,WANG Shibo.Study on key technology of internet plus intelligent coal shearer[J].Coal Science and Technology,2016,44(7):1-9.
[9]	王国法.煤矿综采自动化成套技术与装备创新和发展[J].煤炭科学技术,2013,41(11):1-5. WANG Guofa.Innovation and development on automatic completed set technology and equipment of fully-mechanized coal mining face[J].Coal Science and Technology,2013,41(11):1-5.
[10]	Directorate-general for research and innovation. New mechanisation and automation of longwall and drivage equipment[R].Luxembourg:European Commission,2011.
[11]	唐恩贤.黄陵矿业公司智能化开采核心技术及其应用实践[J].中国煤炭,2019,45(4):13-18. TANG Enxian.Core technology of intelligent mining in Huangling Mining Company and its application practice[J].China Coal,2019,45(4):13-18.
[12]	刘振坚,邱锦波,庄德玉.天地科技上海分公司采煤机智能化技术现状与展望[J].中国煤炭,2019,45(7):33-39. LIU Zhenjian,QIU Jinbo,ZHUANG Deyu.Present situation and prospect of intelligent shearer technology of Shanghai Branch Company of Tiandi Co.,Ltd.[J].China Coal,2019,45(7):33-39.
[13]	索智文,赵亦辉,周展.超大采高采煤机智能诊断电气控制系统研究[J].煤炭工程,2020,52(6):15-19. SUO Zhiwen,ZHAO Yihui,ZHOU Zhan.Research on electrical control system of super high coal shearer[J].Coal Engineering,2020,52(6):15-19.
[14]	李森.基于惯性导航的工作面直线度测控与定位技术[J].煤炭科学技术,2019,47(8):169-174. LI Sen.Measurement & control and localisation for fully-mechanized working face alignment based on inertial navigation[J].Coal Science and Technology,2019,47(8):169-174.
[15]	苏秀平,李威,樊启高.采煤机滚筒调高滑模变结构控制策略[J].煤炭学报,2012,37(12):2107-2111. SU Xiuping,LI Wei,FAN Qigao.A shearer drum height adjusting strategy using sliding-mode variable structure control[J].Journal of China Coal Society,2012,37(12):2107-2111.
[16]	李文华,刘娇,柴博.薄煤层采煤机调高系统PID控制的研究与仿真[J].测控技术,2017,36(4):57-60. LI Wenhua,LIU Jiao,CHAI Bo.Research and simulation of height adjusting system with PID control for thin seam shearer[J].Measurement & Control Technology,2017,36(4):57-60.
[17]	王慧,赵国超,宋宇宁,等.采煤机调高过程的轨迹跟踪模糊PID控制[J].电子测量与仪器学报,2018,32(8):164-171. WANG Hui,ZHAO Guochao,SONG Yuning,et al. Trajectory tracking fuzzy PID control for height adjustment process of shearer[J].Journal of Electronic Measurement and Instrumentation,2018,32(8):164-171.
[18]	王忠宾,徐志鹏,董晓军.基于人工免疫和记忆切割的采煤机滚筒自适应调高[J].煤炭学报,2009,34(10):1405-1409. WANG Zhongbin,XU Zhipeng,DONG Xiaojun.Self-adaptive adjustment height of the drum in the shearer based on artificial immune and memory cutting[J].Journal of China Coal Society,2009,34(10):1405-1409.
[19]	刘东航.采煤机自动记忆截割控制系统的研究与设计[D].西安:西安科技大学,2018. LIU Donghang.Research and design of automatic memory cutting and cutting control system for shearer[D].Xi'an:Xi'an University of Science and Technology,2018.
[20]	袁亮.煤炭精准开采科学构想[J].煤炭学报,2017,42(1):1-7. YUAN Liang.Scientific conception of precision coal mining[J].Journal of China Coal Society,2017,42(1):1-7.
[21]	程建远,朱梦博,王云宏,等.煤炭智能精准开采工作面地质模型梯级构建及其关键技术[J].煤炭学报,2019,44(8):2285-2295. CHENG Jianyuan,ZHU Mengbo,WANG Yunhong,et al.Cascade construction of geological model of longwall panel for intelligent precision coal mining and its key technology[J].Journal of China Coal Society,2019,44(8):2285-2295.
[22]	赵友军,赵亦辉,张旭辉.采煤机数字化技术发展及展望[J].重型机械,2018(4):29-34. ZHAO Youjun,ZHAO Yihui,ZHANG Xuhui.Development and prospect of digital technology on shearer[J].Heavy Machinery,2018(4):29-34.
[23]	樊红卫,张旭辉,曹现刚,等.智慧矿山背景下我国煤矿机械故障诊断研究现状与展望[J].振动与冲击,2020,39(24):194-204. FAN Hongwei,ZHANG Xuhui,CAO Xiangang,et al.Research status and prospect of fault diagnosis of China's coal mine machines under background of intelligent mine[J].Journal of Vibration and Shock,2020,39(24):194-204.
[24]	曹现刚,狄航,杜青青,等.采煤机全生命周期服务系统研究[J].工矿自动化,2018,44(6):40-45. CAO Xiangang,DI Hang,DU Qingqing,et al.Research on whole life cycle service system of shearer[J].Industry and Mine Automation,2018,44(6):40-45.
[25]	李首滨.煤炭工业互联网及其关键技术[J].煤炭科学技术,2020,48(7):98-108. LI Shoubin.Coal Industry Internet and its key technologies[J].Coal Science and Technology,2020,48(7):98-108.
[26]	范京道,闫振国,李川.基于5G技术的煤矿智能化开采关键技术探索[J].煤炭科学技术,2020,48(7):92-97. FAN Jingdao,YAN Zhenguo,LI Chuan.Exploration of intelligent coal mining key technology based on 5G technology[J].Coal Science and Technology,2020,48(7):92-97.

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综采工作面采煤机智能化技术研究现状

doi: 10.13272/j.issn.1671-251x.2021090024

作者简介:
赵亦辉(1981-),男,山西运城人,高级工程师,硕士,主要从事煤矿装备自动化、智能化方面的研发工作,E-mail:xamjzhaoyihui@163.com。

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Research status of intelligent technology of shearer in fully mechanized working face

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留言板

综采工作面采煤机智能化技术研究现状

doi: 10.13272/j.issn.1671-251x.2021090024

作者简介: 赵亦辉(1981-),男,山西运城人,高级工程师,硕士,主要从事煤矿装备自动化、智能化方面的研发工作,E-mail:xamjzhaoyihui@163.com。

计量

出版历程

Research status of intelligent technology of shearer in fully mechanized working face

计量

出版历程

目录

作者简介:
赵亦辉(1981-),男,山西运城人,高级工程师,硕士,主要从事煤矿装备自动化、智能化方面的研发工作,E-mail:xamjzhaoyihui@163.com。