Research progress and development trends in adaptive cutting technology for shearers
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摘要:
自适应截割技术是实现采煤机智能化的核心技术，对提升煤矿开采效率、提高安全性和资源利用率具有重要作用。因此，开展了自适应截割技术的综述研究，重点探讨了其技术原理及应用现状。根据核心功能和技术目标，将采煤机自适应截割技术划分为记忆截割、透明地质、煤岩识别和自适应控制4个研究内容。记忆截割通过记录历史数据来优化采煤路径，透明地质利用综合探测技术获取实时地质信息，煤岩识别技术根据不同的识别原理，可以分为基于物理参数的间接法、基于视觉的直接法、以及探地雷达和超声波等基于波动特性的探测法，以实现煤岩界面或煤岩性质的精确识别，自适应控制则通过自动化调节采煤机的运行参数。这些技术从多个角度提升了采煤机的智能化水平。然而，由于煤层地质条件及恶劣开采环境的影响，现有技术在适应性和经济性方面存在一些局限性。因此，针对未来采煤机自适应截割技术的发展趋势，提出了以下建议：促进记忆截割、透明地质与煤岩识别技术的融合，以实现更高效的煤层信息获取；采用多传感器融合技术，以提高煤岩识别的准确度和可靠性；发展基于大数据分析的智能决策支持系统，优化采煤机的运行策略，同时研究多领域协同仿真控制策略，以应对技术瓶颈并增强系统性能。
Abstract:Adaptive cutting technology is crucial for enabling intelligent shearers, significantly improving mining efficiency, safety, and resource utilization. Therefore, a comprehensive review of adaptive cutting technology has been conducted, focusing on its technical principles and current applications. Based on core functions and technical objectives, adaptive cutting technology is categorized into four primary research areas: memory cutting, transparent geology, coal-rock identification, and adaptive control. Memory cutting enhance cutting paths by recording historical data, while transparent geology leverages integrated detection technologies to acquire real-time geological information. Coal-rock identification techniques are classified according to recognition principles: indirect methods based on physical parameters, direct methods relying on visual information, and wave-based detection methods such as ground-penetrating radar and ultrasound. Adaptive control automates the adjustment of shearer operating parameters. Collectively, these technologies advance the intelligence of coal mining machines from various perspectives. Nevertheless, due to geological complexities and challenging mining environments, existing technologies face limitations in adaptability and cost-effectiveness. Therefore, future development of adaptive cutting technology should focus on integrating memory cutting, transparent geology, and coal-rock identification technologies to enhance coal seam data acquisition. Implementing multi-sensor fusion technology to improve the accuracy and reliability of coal-rock identification. Developing intelligent decision-support systems based on big data analytics to optimize mining operations and researching multi-domain collaborative simulation control strategies to address technical challenges and improve system performance.
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图 2 工作面综合探测示意
Figure 2. Schematic diagram of the comprehensive detection of the working face
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图 3 基于透明地质的协同规划控制系统
Figure 3. Collaborative planning and control system based on transparent geology
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图 6 预测截割顶底板岩层位置原理
Figure 6. Principle of identification position of cutting top and bottom slate
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图 13 不同煤岩比例下的截齿红外图像
Figure 13. Infrared thermal image of picks under different coal-rock cutting ratio
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图 15 煤岩红外图像采集与识别
Figure 15. Infrared image acquisition and recognition result of coal rock
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图 17 节点式雷达探测与随采动态探测系统
Figure 17. Nodal GPR detection system and accompanying dynamic detection system
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图 18 基于超声波技术煤岩界面识别原理
Figure 18. Coal-rock interface identification principle based on ultrasonic technology
表 1 有量纲时域统计参量对比
Table 1 Comparison of dimensional time-domain statistical parameters
有量纲指标 割煤状态（dm·s−2） 割顶状态（dm·s−2） 绝对值增幅/% 均方值 71.40 281.28 295 方差 71.40 281.28 295 方根幅值 5.73 8.69 52 平均幅值 6.76 11.27 67 有效值 8.45 16.77 98 
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表 2 无量纲时域统计参量对比
Table 2 Comparison of dimensionless time-domain statistical parameters
无量纲指标 割煤状态 割顶状态 绝对值增幅/% 波形指标 1.25 1.49 24 峰值指标 4.96 9.20 85 脉冲指标 6.20 13.69 120 裕度指标 7.31 17.76 143 峭度指标 0.04 0.03 −25
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表 3 采煤机自适应截割技术对比分析
Table 3 Comparison and Analysis of Adaptive Cutting Technology for shearers
单点技术 核心内容 技术特点 应用场景 优劣势对比 记忆截割 利用历史截割数据生成最优截割路径以提升作业效率 基于历史数据、具备路径优化能力和较高的重复精确度 地质条件相对稳定且重复作业较多的工况 优点：路径优化、提高效率
缺点：对地质变化的适应性较差，需配合其他技术以应对动态变化透明地质 通过地质数据采集、处理和透明化展示，使得地质特征更加清晰 实时展现地质特征、数据可视化强 复杂地质结构下的采矿场景 优点：清晰展示地质特征，辅助决策
缺点：依赖高精度传感器和实时处理系统，可能存在成本高的问题煤岩识别 利用传感器技术实时识别煤岩分界，实现精准截割 识别精度高、适应复杂地质环境 煤岩混杂、地质变化显著的工况 优点：精确识别煤岩分界，减少误割率
缺点：对传感器性能和数据处理要求高，在数据缺失或误差大的条件下可能失效自适应控制 通过实时反馈控制算法，自动调节截割参数以适应不同工况 智能化高，实时调整截割速度、力度等关键参数 工况实时变化明显、需要灵活调整的场景 优点：灵活应对复杂工况，提升作业稳定性和安全性
缺点：对控制算法和实时计算要求高，依赖大量实时数据以保证精度和响应速度
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