大型矿井主通风机智能监控系统研究.pdf

硕士学位论文 大型矿井主通风机智能监控系统研究 Research on Intelligent Monitoring System of Main Ventilator in Large Mine 作者刘磊 导师雷汝海 副教授 中国矿业大学 二○一九年五月 万方数据 学位论文使用授权声明学位论文使用授权声明 本人完全了解中国矿业大学有关保留、使用学位论文的规定，同意本人所 撰写的学位论文的使用授权按照学校的管理规定处理 作为申请学位的条件之一，学位论文著作权拥有者须授权所在学校拥有学 位论文的部分使用权，即①学校档案馆和图书馆有权保留学位论文的纸质版 和电子版，可以使用影印、缩印或扫描等复制手段保存和汇编学位论文；②为 教学和科研目的，学校档案馆和图书馆可以将公开的学位论文作为资料在档案 馆、图书馆等场所或在校园网上供校内师生阅读、浏览。另外，根据有关法规， 同意中国国家图书馆保存研究生学位论文。 （保密的学位论文在解密后适用本授权书）。 作者签名导师签名 年月日年月日 万方数据 中图分类号TP273学校代码10290 UDC621.3密级公开 中国矿业大学 硕士学位论文 大型矿井主通风机智能监控系统研究 Research on Intelligent Monitoring System of Main Ventilator in Large Mine 作者刘磊导师雷汝海副教授 申请学位工学硕士培养单位 信息与控制工程学院 学科专业控制科学与工程研究方向智能控制 答辩委员会主席李明评 阅 人 二○一九年五月 万方数据 致谢致谢 光阴荏苒，硕士研究生学习生涯即将结束，期间的学习生活经历让我受益 匪浅，值此毕业论文完成之际，我谨向所有关心和帮助我的人们表示最诚挚的 感谢与美好的祝愿。 首先我要衷心地感谢我的导师雷汝海副教授在这三年里对我的关怀和教 导，从论文的选题、构思、撰写到最终的定稿，雷老师都给了我悉心的指导和 热情的帮助，使我的毕业论文能够顺利的完成。雷老师严谨细致的治学态度、 一丝不苟的工作作风一直是我工作学习的榜样，借此机会再次向雷老师表示诚 挚的谢意并祝愿雷老师身体健康，万事如意。 感谢黄友鹤、李雨涵和杨婷在我撰写论文期间提供的帮助，感谢我所有的 亲人，正是由于他们的理解和帮助，我才能安心学习，并顺利完成我的学业。 最后感谢各位评阅论文及负责答辩的专家和老师。 万方数据 I 摘摘要要 主通风机有着“矿井肺腑”之称，承担着向井下输送新鲜空气的重任，是 矿井安全生产的前提与保障，其重要性不言而喻。大型矿井井下工作面情况复 杂，不同时期需风量差异较大，主通风机应根据工作面情况调整供风量以满足 生产的需要，但是国内多数大型矿井主通风机风量调节方式较为落后，不能实 现风量的精确调节，有着“大马拉小车”的现象，造成了电能的浪费，另外， 大型矿井井下工作人员众多， 这对主通风机运行的可靠性就提出了更高的要求。 因此进行大型矿井主通风机智能监控系统研究对于实现大型矿井的安全节能生 产具有重要意义。 针对大型矿井主通风机监控系统中存在的问题，本课题以平煤五矿主通风 机为背景，对大型矿井主通风机智能监控系统进行研究。首先，本课题对比了 主通风机常用风量调节方式后，采用变频调速法进行风量调节，使用模糊 PID 控制器对风量调节系统进行智能控制，并采用遗传算法对模糊 PID 控制器的量 化因子与比例因子进行离线寻优，优化了模糊 PID 控制器的控制效果；然后， 对主通风机机械故障诊断算法进行了研究，采用 EEMD 算法对振动信号进行分 解，通过计算 IMF 分量的能量熵来提取故障信号的特征参数，使用 SOM 神经 网络实现主通风机机械故障的智能诊断；最后，结合现场实际情况，采用传感 器检测技术与 PLC 控制技术进行了智能监控系统的硬件设计，使用 TIA portal 与组态王等软件进行了智能监控系统的软件设计。 本课题设计的大型矿井主通风机智能监控系统对其他矿井主通风机监控系 统的改造具有借鉴意义，对实现大型矿井主通风机监控系统的智能化、信息化 发展产生积极影响。 该论文有图 60 幅，表 16 个，参考文献 85 篇。 关键词关键词模糊 PID 控制；Hilbert-Huang 变换；SOM 神经网络；PLC 万方数据 II Abstract The main ventilator is known as “the lung of the mine“, it bears the heavy responsibility of delivering fresh air to the underground, it is the premise and guarantee of the safe production, and its importance is self-evident. The situation of working faces is complex in large mine, and the air demand varies greatly in different periods, the main ventilator should adjust the air supply according to the condition of working face to meet the needs of production, but in most large mines, the adjustment of air volume is relatively backward, and it can not achieve accurate adjustment of air volume, there is a phenomenon of “big horse trolley“, which results in the waste of electric energy. in addition, there are many workers in large mine, which puts higher requirements for the reliability of main ventilator. Therefore, the research on intelligent monitoring system of main ventilators is very important for realizing safe and energy-saving production in large mine. Aiming at the problem existing in the monitoring system of main ventilator in large mine, taking the main ventilator in Pingmei No. 5 Coal Mine as the background of this subject, the intelligent monitoring system of main ventilator in large mine is studied. Firstly, the adjustment of air volume is compared, and frequency conversion is adopted to regulate the air volume. fuzzy PID controller is designed for the air volume regulation system, and genetic algorithm is used to optimize the control effect of the fuzzy PID controller. Secondly, the fault diagnosis algorithm of main ventilator is studied in this subject, the vibration signal is decomposed by EEMD algorithm, the characteristic ination of fault signal is extracted by calculating the energy entropy of IMF, the intelligent fault diagnosis of main ventilators is realized by SOM neural network. Finally, combining with the actual situation on the spot, the hardware design of the intelligent monitoring system is carried out by using sensor detection technology and PLC control technology, and the software design of the intelligent monitoring system is carried out by using TIA portal and Kingview. The intelligent monitoring system of main ventilators designed in this subject can be used for reference in the re of the monitoring system of main ventilator in other mine, and it has a positive impact on the intelligent and inational development of the monitoring system of main ventilator in large mine. There are 60 papers, 16 tables and 85 references in this paper. 万方数据 III Keywords Fuzzy PID control; SOM neural network; Hilbert-Huang Trans; PLC 万方数据 IV 目目录录 摘摘要要............................................................................................................................ I 目目录录..........................................................................................................................IV 图清单图清单...................................................................................................................... VIII 表清单表清单........................................................................................................................XII 1 绪论绪论............................................................................................................................1 1.1 选题的背景及意义.................................................................................................1 1.2 国内外研究现状.....................................................................................................3 1.3 课题主要研究内容及论文结构.............................................................................7 2 主通风机智能监控方案主通风机智能监控方案............................................................................................9 2.1 概述.........................................................................................................................9 2.2 主通风机风量调节系统分析与建模.....................................................................9 2.3 主通风机机械故障机理分析...............................................................................13 2.4 主通风机监控系统监测参数及结构...................................................................15 2.5 本章小结...............................................................................................................18 3 主通风机风量调节智能控制算法研究主通风机风量调节智能控制算法研究..................................................................19 3.1 概述.......................................................................................................................19 3.2 模糊 PID 控制器设计...........................................................................................19 3.3 基于遗传算法优化的模糊 PID 控制器设计.......................................................29 3.4 本章小结...............................................................................................................34 4 主通风机机械故障智能诊断算法研究主通风机机械故障智能诊断算法研究..................................................................35 4.1 概述.......................................................................................................................35 4.2 Hilbert-Huang 变换原理研究................................................................................36 4.3 EEMD 分解算法研究............................................................................................41 4.4 基于 SOM 神经网络的故障诊断........................................................................ 46 4.5 本章小结...............................................................................................................51 5 主通风机智能监控系统设计主通风机智能监控系统设计..................................................................................52 5.1 概述.......................................................................................................................52 5.2 智能监控系统硬件设计.......................................................................................52 5.3 智能监控系统软件设计.......................................................................................57 5.4 本章小结...............................................................................................................65 万方数据 V 6 总结与展望总结与展望..............................................................................................................66 6.1 总结.......................................................................................................................66 6.2 展望.......................................................................................................................66 参考文献参考文献......................................................................................................................68 作者简历作者简历......................................................................................................................73 学位论文原创性声明学位论文原创性声明..................................................................................................74 学位论文数据集学位论文数据集..........................................................................................................75 万方数据 VI Contents Abstract........................................................................................................................II Contents......................................................................................................................VI List of Figures..........................................................................................................VIII List of Tables.............................................................................................................XII 1 Introduction............................................................................................................... 1 1.1 Background and Significance...................................................................................1 1.2 Present Situation of Domestic and Foreign..............................................................3 1.3 Main Research Content and Structure of the Subject...............................................7 2 Intelligent Monitoring Scheme for Main ventilator...............................................9 2.1 Introduction..............................................................................................................9 2.2Analysis and Modeling ofAir Volume Regulation of Main Ventilator....................9 2.3Analysis of Mechanical Failure Mechanism of Main Ventilator............................13 2.4 Monitoring Parameters and Structure of Monitoring System of Main Ventilator..15 2.5 Summary................................................................................................................ 18 3 Research on Intelligent Control Algorithms forAir Volume Regulation of Main Ventilator...........................................................................................................19 3.1 Introduction............................................................................................................19 3.2 Design of Fuzzy PID Controller.............................................................................19 3.3 Design of Fuzzy-PID Controller Based on GeneticAlgorithms............................29 3.4 Summary................................................................................................................ 34 4 Research on Intelligent DiagnosisAlgorithms for Mechanical Faults of Main Ventilator.....................................................................................................................35 4.1 Introduction............................................................................................................35 4.2 Research on Hilbert-Huang Trans.................................................................. 36 4.3 Research on EEMDAlgorithms.............................................................................41 4.4 Fault Diagnosis Based on SOM Neural Network...................................................46 4.5 Summary................................................................................................................ 51 5 Design of Intelligent Monitoring System for Main Ventilator.............................52 5.1 Introduction............................................................................................................52 万方数据 VII 5.2 Hardware Design of Intelligent Monitoring System..............................................52 5.3 Software Design of Intelligent Monitoring System................................................57 5.4 Summary................................................................................................................ 65 6 Summary and Prospect...........................................................................................66 6.1 Summary................................................................................................................ 66 6.2 Prospect..................................................................................................................66 References................................................................................................................... 68 Author’s Resume........................................................................................................73 Declaration of Thesis Originality..............................................................................74 Dissertation Date Collection......................................................................................75 万方数据 VIII 图清单图清单 图序号图名称页码 图 1-1主通风机现场图1 Figure 1-1Field map of main ventilator1 图 2-1主通风机结构图9 Figure 2-2Structure of main ventilator9 图 2-2交-直-交变频器结构图10 Figure 2-1Structure ofAC-DC-AC Converter10 图 2-3主通风机工作特性曲线11 Figure 2-3Working characteristic Curve of main ventilator11 图 2-4转子不对中示意图14 Figure 2-4Schematic diagram of rotor misalignment14 图 2-5风量计算原理图16 Figure 2-5Principle of air volume calculation16 图 2-6振动传感器安装位置17 Figure 2-6Installation position of vibration sensor17 图 2-7智能监控系统结构图18 Figure 2-7Structure of intelligent monitoring system18 图 3-1PID 控制器结构20 Figure 3-1Structure of PID Controller20 图 3-2模糊控制器结构图21 Figure 3-2Structure of fuzzy controller21 图 3-3风量调节系统结构图22 Figure 3-3Structure of air volume regulating system22 图 3-4输入变量隶属度函数23 Figure 3-4Membership function of Variables23 图 3-5输出变量隶属度函数23 Figure 3-5Membership function of output Variables23 图 3-6模糊工具箱界面26 Figure 3-6Interface of fuzzy toolbox26 图 3-7曲面观测器27 Figure 3-7Surface viewer27 图 3-8MATLAB/SIMULINK 仿真模型27 Figure 3-8Simulation model in MATLAB/SIMULINK27 图 3-9阶跃信号仿真结果28 Figure 3-9Simulation results of step signal28 图 3-10抗干扰能力对比结果28 Figure 3-10Comparison of anti-jamming capability28 图 3-11鲁棒性对比结果29 万方数据 IX Figure 3-11Robustness comparison29 图 3-12遗传算法寻优流程图30 Figure 3-12Optimizing flow chart of genetic algorithm30 图 3-13适应度值变化曲线31 Figure3-13Curve of fitness change31 图 3-14遗传算法优化模糊 PID 控制器仿真模型32 Figure3-14 Simulation model of Genetic Algorithms for optimizing Fuzzy-PID controller 32 图 3-15阶跃信号仿真结果32 Figure 3-15Simulation results of step signal32 图 3-16抗干扰能力对比结果33 Figure 3-16Comparison of anti-jamming capability33 图 3-17鲁棒性对比结果33 Figure 3-17Robustness comparison33 图 4-1EMD 算法流程图38 Figure 4-1Flow chart of EMD38 图 4-2仿真信号的时域波形和频谱39 Figure 4-2Time domain wave and spectrum of simulated signal