基于多源数据融合的采煤机截割载荷识别与预测研究.pdf

博士学位论文博士学位论文 基于多源数据融合的采煤机截割载荷 识别与预测研究 Research on Identification and Prediction of Shearer Cutting Load Based on Multi-source Data Fusion 作者姓名田立勇 导师姓名毛君 教授 学科专业机械设计及理论 研究方向机械系统建模与仿真 完成日期2020 年 8 月 28 日 辽宁工程技术大学 Liaoning Technical University 万方数据 基 于 多 源 数 据 融 合 的 采 煤 机 截 割 载 荷 识 别 与 预 测 研 究 田 立 勇 辽 宁 工 程 技 术 大 学 万方数据 万方数据 中图分类号TD421.6学校代码10147 UDC621密级公 开 辽宁工程技术大学 博士学位论文博士学位论文 基于多源数据融合的采煤机截割载荷 识别与预测研究 Research on Identification and Prediction of Shearer Cutting Load Based on Multi-source Data Fusion 作者姓名田立勇学号47111019 导师姓名毛君（教授）副导师姓名李晓竹（教授） 申请学位工学博士学位培养单位机械工程学院 学科专业 机械设计及理论研究方向机械系统建模与仿真 二○二○年八月 万方数据 致 谢致 谢 本论文是在尊敬的导师毛君教授、李晓竹教授的亲切关怀和精心指导下完成的。在博 士的学习和本文研究过程中，始终得到导师的热情帮助、关心和严格指导，导师渊博的学 识、敏锐的思维、严谨求实的治学态度、忘我的工作作风深深地感染着我，使论文工作得 以顺利完成，谨此向导师表示深深的敬意和衷心的感谢。 在本文研究过程中，得到了陈洪月教授的大力支持和帮助，谨此表示深深的谢意。 感谢张强教授、张东升教授、郝志勇教授、谢苗教授、朴明波副教授等团队成员以及 中煤装备公司和张家口煤机厂 “国家能源煤矿采掘机械装备研发中心实验室” 的袁智院长、 孙鹏亮所长、王力军所长，感谢西安煤矿机械有限公司的张永权主任，北京必创科技有限 公司的邓延卿，沈唯真经理等各位朋友，在我的本文研究，尤其是设备调试和现场试验过 程中所给予的大力帮助。 感谢杨新乐教授、王慧教授、李文华教授、刘旭南博士，在博士学习、培养和生活方 面的关心和帮助。 感谢师弟王鑫博士在学习和本文研究、尤其是在试验数据分析、处理等方面所给予课 题的大力帮助。感谢赵国超博士、张一辙硕士、李文政硕士、戴渤鸿硕士、孙业新硕士、 李志垚硕士、葛津铭硕士、赵建军等同学给我的许多帮助。 感谢一直支持我的家人和所有关心、帮助我的人们 万方数据 I 摘要摘要 滚筒载荷识别与预测是实现采煤机煤岩识别、自动截割及截割部传动系统故障诊断的 关键问题。本文通过理论分析、仿真模拟及实验测试相结合的方法，设计发明基于多传感 器的滚筒载荷感知方法，构建了多传感器数据特征提取与降噪模型，研究了基于多传感器 信息融合的滚筒载荷辨识策略，实现了采煤机滚筒载荷实时感知与精确预测，具体如下 （1）针对采煤机滚筒截割载荷无法获取的问题，制定了基于多传感器融合的采煤机 滚筒载荷感知系统总体方案，设计发明截齿载荷测试方法、滚筒扭矩测试方法、摇臂连接 销轴测试方法、摇臂变形量测试方法，研究基于多传感器的多参量数据同步采集与传输方 法，为滚筒载荷的精确感知奠定基础。 （2）针对截割部摇臂壳体和多级齿轮传动系统结构复杂，传感器安装位置无法确定 的问题，建立了采煤机截割部传递系统刚柔耦合动力学模型，分析了研究摇臂壳体变形与 滚筒载荷间的相互影响关系，通过对比摇臂壳体关键位置的变形规律，得到摇臂前后两侧 12 个应变传感器最佳安装位置； 分析研究了截割部多级齿轮传递系统与滚筒载荷间的相互 影响关系，确定了距滚筒端距离最近的齿轮轴 6 是滚筒扭矩感知传感器的最佳安装位置。 （3）针对缩小比例采煤机滚筒截割实验测试结果误差大、精度底的问题，根据采煤 机实际结构，研制了截齿三向力、惰轮轴载荷、摇臂连接销轴载荷及摇臂应变测试传感器 及数据采集、传输平台，并在张家口煤机厂国家能源煤矿采掘机械装备研发实验中心进 行 11 模拟井下工况的采煤机滚筒截割实验，获取滚筒工作过程中各传感器的实验测试数 据，为多传感器融合滚筒载荷辨识与预测提供支撑。 （4）针对滚筒实验数据中包含大量噪声干扰信号问题，构建了基于独立成分和小波 分析滚筒测试特征数据提取模型与方法，完成了对各传感器的测试数据进行时域和频域分 析， 时域分析结果表明 各传感器所得到的测量结果均能体现出滚筒截割载荷的变化规律； 频域分析结果表明各传感器数据的 1 阶波峰频率均为 0.467Hz，为滚筒的回转频率，通 过各传感器的各阶频率峰值大小可描述滚筒截割载荷变化。 （5）针对单一传感器对滚筒载荷识别测试精度低、稳定性差的问题，以截齿载荷直 接测试的滚筒载荷为输出样本，以惰轮轴传感器、摇臂连接轴传感器、摇臂变形传感器测 试数据为输入样本，建立基于深度神经网络的滚筒载荷辨识与预测模型，并通过实验数据 对预测模型进行验证，验证结果表明预测模型对滚筒三向截割载荷的预测精度达到了 83 以上，对滚筒扭矩预测精度可达到 95，说明预测模型具有较高的精度。 该论文有图 104 幅，表 16 个，参考文献 156 篇。 关键词关键词采煤机；摇臂；截割载荷；载荷识别；载荷预测；感知方法；神经网络 万方数据 II Abstract Drum load identification and prediction are the key problems to realize the coal-rock identification, the cutting automation and the fault diagnosis of transmission system of cutting unit. In this paper, by combining the theoretical analysis, the computer simulation and the experimental test, the drum load sensing based on multi-sensor is designed, the model for multi-sensor data characteristics extraction and noise reduction is established, the drum load identification strategy based on multi-sensor ination fusion is researched, the real-time perception and accurate prediction of shearer drum load is realized. The content of the paper are as follows 1 Aiming at the problem that the cutting load of shearer drum can not be obtained, the system overall scheme for sensing the cutting load of shearer drum based on multi-sensor fusion is designed, the s for pick load measuring, for drum torque measuring, for connecting pin shaft measuring and for rocker arm deation measuring are invented, the multi-parameter synchronous acquisition and transmission based on multi-sensor is researched. The content mentioned above provides the basis for accurate perception of drum load. 2 Aiming at the problem that sensor installation positions can not be determined due to the complexity of the transmission system with multi-stage gears and rocker arm shell of cutting unit, the rigid-flexible coupling dynamics model of transmission system of shearer cutting unit is constructed, the interaction between the deation of rocker arm shell and drum load is analyzed. By comparing the deation law for key positions of rocker arm shell, the optimal installation positions for 12 strain sensors located in front and rear sides of rocker arm are obtained. The interaction between the transmission system for multi-stage gears of cutting unit and drum load is studied, the optimal installation position of drum torque sensor is determined to be the gear shaft 6 which is nearest to drum end. 3 Aiming at the problem that the large error and low precision in the experiment of shearer drum cutting with lessen ratio, according to the practical structure of shearer, the pick 3-dimensional force sensor, the idler gear shaft load sensor, the load sensor of connecting pin shaft of rocker arm, the rocker arm strain sensor are developed, and the plat for data acquisition and transmission is constructed. The 11 shearer drum cutting experiment simulating underground work condition is pered in the Research and Development Center for National Energy Mine Machinery in Zhangjiakou Mine Machinery Co. Ltd. The experiment data of sensors in drum work process are obtained, which provides the basis for the identification and prediction of shearer cutting load based on multi-sensor data fusion. 万方数据 III 4 Aiming at the amount noise interference included in drum experiment data, the model and for characteristics extraction of drum measured data are proposed based on independent components and wavelet analysis. The analysis of sensor data in time-domain and frequency-domain is accomplished. The time-domain analysis result shows that the variation law of drum cutting load can be expressed by the sensor data. The frequency -domain analysis result shows that the first-order wave peak frequency of sensor data is 0.467Hz, which is exactly the rotary frequency of the drum. The drum cutting load variation can be described by the frequency peaks of each sensor. 5 Aiming at the low measuring precision and poor stability of drum load identification with a single sensor, taking the drum load directly measured from pick load as the output sample, taking the measured data including the data of the idler gear shaft sensor, the data of the rocker arm connecting shaft sensor, the rocker arm deation sensor as the sample, the model for drum load identification and predication based on the deep neural network is established, and the model is verified by the experiment data. The verification result shows that the prediction accuracy of the model for drum 3-dimensional cutting load is over 83, for drum torque is 95, which shows the prediction model is of high accuracy. There are 104 diagrams,16 tables and 156 references. Keywordsrocker arm; cutting load; load identification; load prediction; perception ; neural network 万方数据 IV 目录目录 摘要摘要............................................................................................................................................. I 目录 I 目录........................................................................................................................................... IV 图清单 IV 图清单....................................................................................................................................... VIII 表清单 VIII 表清单......................................................................................................................................... XIV 变量注释表 XIV 变量注释表................................................................................................................................... XV 1 绪论 XV 1 绪论............................................................................................................................................. 1 1 1.1 课题来源及背景..................................................................................................................... 1 1.2 采煤机截割载荷识别研究发展与应用................................................................................. 1 1.3 采煤机载荷识别存在问题..................................................................................................... 8 1.4 论文主要研究内容和技术路线............................................................................................. 8 1.5 论文研究的目的和意义....................................................................................................... 11 1.6 本章小结............................................................................................................................... 11 2 基于多传感器的滚筒截割载荷感知方法研究2 基于多传感器的滚筒截割载荷感知方法研究........................................................................1212 2.1 采煤机摇臂滚筒载荷传动模型构建.................................................................................... 12 2.2 多传感器滚筒载荷感知方法研究....................................................................................... 21 2.3 多传感器数据采集与传输方案确定................................................................................... 33 2.4 本章小结............................................................................................................................... 37 3 面向滚筒载荷感知的摇臂应变和齿轮轴敏感位置分析3 面向滚筒载荷感知的摇臂应变和齿轮轴敏感位置分析.......................................................3838 3.1 采煤机截割部结构与参数分析............................................................................................ 38 3.2 采煤机摇臂刚柔耦合模型建立与仿真................................................................................ 39 3.3 采煤机摇臂应变特性分析与应变计粘贴位置确定............................................................ 42 3.4 齿轮轴受力分析与传感器安装位置确定........................................................................... 52 3.5 本章小结............................................................................................................................... 55 4 基于多传感器融合的滚筒载荷试验测试研究4 基于多传感器融合的滚筒载荷试验测试研究.......................................................................5656 4.1 采煤机滚筒载荷测试平台................................................................................................... 56 4.2 截齿截割载荷感知测试与分析........................................................................................... 57 4.3 滚筒实时转速感知测试与分析........................................................................................... 60 4.4 滚筒截割扭矩感知测试与分析........................................................................................... 60 4.5 摇臂连接销轴感知测试与分析........................................................................................... 62 4.6 摇臂变形感知测试与分析.................................................................................................... 65 万方数据 V 4.7 本章小结............................................................................................................................... 71 5 基于多传感器的滚筒载荷特征识别研究5 基于多传感器的滚筒载荷特征识别研究................................................................................7272 5.1 基于独立成分和小波分析的信息提取方法........................................................................ 72 5.2 滚筒载荷与各传感器信息特征间的关系研究.................................................................... 80 5.3 本章小结............................................................................................................................... 87 6 基于多传感器数据融合的滚筒载荷智能识别策略研究6 基于多传感器数据融合的滚筒载荷智能识别策略研究.......................................................8888 6.1 深度信念网络的改进........................................................................................................... 88 6.2 改进后的深度信念网络训练............................................................................................... 93 6.3 基于多传感器数据融合的滚筒载荷预测........................................................................... 95 6.4 本章小结............................................................................................................................... 99 7 结论、创新点及展望7 结论、创新点及展望.............................................................................................................100100 7.1 论......................................................................................................................................... 100 7.2 创新点................................................................................................................................. 101 7.3 展望..................................................................................................................................... 101 参考文献参考文献..................................................................................................................................... 102 查新结论 102 查新结论..................................................................................................................................... 110 作者简历 110 作者简历..................................................................................................................................... 111 论文原创性声明 111 论文原创性声明.........................................................................................................................113 学位论文数据集 113 学位论文数据集.........................................................................................................................114114 万方数据 VI Contents Abstract...........................................................................................................................................I Contents........................................................................................................................................IV Dragram List............................................................................................................................ VIII Table List...................................................................................................................................XIV Variable Comment List..............................................................................................................XV 1 Introduction.................................................................................................................................1 1.1 Project Source and Background.................................................................................................1 1.2 Research Development and Application of Shearer Cutting Load Identification..................... 1 1.3 Problem of Shearer Cutting Load Identification........................................................................8 1.4 Research Contents and Technical Route....................................................................................8 1.5 Purpose and Significance.........................................................................................................11 1.6 Summary..................................................................................................................................11 2 Research on Perception of Drum Cutting Load Based on Multi-sensor...............12 2.1 Construction of Drum load Transmission Model for Shearer Rocker Arm.............................12 2.2 Perception Research of Drum Cutting Load Based on Multi-sensor.........................21 2.3 DataAcquisition and Transmission of Multi-sensor................................................................33 2.4 Summary.................