基于多传感信息融合的采煤机煤岩截割状态识别技术研究.pdf

硕士学位论文 基于多传感信息融合的采煤机煤岩 截割状态识别技术研究 Research on Recognition Technology of Shearer Coal-rock Cutting Status Based on Multi-sensor Ination Fusion 作 者蒋 干 导 师王忠宾 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 TD421 学校代码 10290 UDC 621 密 级 公开 中国矿业大学 硕士学位论文 基于多传感信息融合的采煤机煤岩 截割状态识别技术研究 Research on Recognition Technology of Shearer Coal-rock Cutting Status Based on Multi-sensor Ination Fusion 作 者 蒋干 导 师 王忠宾 教授 申请学位 工学硕士学位 培养单位 机电工程学院 学科专业 机械制造及其自动化 研究方向 煤矿机电装备自动化 答辩委员会主席 韩正铜教授 评 阅 人 二○一九年五月 万方数据 致谢致谢 时光如白驹过隙，转眼间，三年的硕士研究生学习生活即将结束。在此，谨 向研究生期间对我的学业和生活方面给予关心和帮助的人们， 致以最衷心的感谢 和最美好的祝福 首先感谢我的导师王忠宾教授，本文是在王忠宾教授的悉心指导下完成的。 师从三载，导师的一言一行、言传身教，一直影响着我，让我不断成长、不断进 步，他将是我一生追逐奋斗的榜样导师在我的学业上尤其是在论文的撰写过程 中，给予了我诸多指导和鼓励，这将使我终生受益。除此之外，导师还在生活方 面给予了我许多关怀和帮助。我将更加努力，不辜负恩师的期望 感谢课题组谭超、司垒、刘新华、姚新港、闫海峰、韩振铎、杨寅威和黄嘉 兴等老师在课题研究和科研实践过程中给予的热忱鼓励和悉心指导。 他们脚踏实 地的作风、平和谦虚的品格、团结奋进的精神风貌为我树立了良好的榜样，这将 对我今后的工作、学习产生深远的影响。在这里，同样向他们致以深深的敬意 感谢许静、路绪良、魏东、樊凯、满溢桥、孙君令、熊祥祥、李沛阳、武子 清、万淼、赵欣和吴虹霖等课题组同窗在论文撰写期间给予的大力支持和无私帮 助，在此致以最真挚的谢意。同窗之谊，我将终生难忘 感谢我生活学习了三年的母校中国矿业大学， 非常有幸能够进入矿大这 所百年学府，聆听老师们的教诲，结识新的朋友，让我不断汲取新知识，充实自 己。毕业之后，我也必将牢记母校教诲，认真踏实，报效社会 需要特别感谢的是我的父母和我的两个姐姐。父母的养育之恩，姐姐的照顾 之情，我无以为报。今后，我将努力工作，为家庭分忧，努力去撑起这个为我遮 风挡雨的港湾 最后，感谢各位专家和学者在百忙之中审阅我的论文，并给予宝贵的指导， 在此谨向各位专家学者表示深深的谢意 万方数据 I 摘摘 要要 采煤机是综采工作面的核心装备之一， 采煤机的智能控制是实现智能化综采 工作面的必要条件， 而准确识别出采煤机煤岩截割状态是实现采煤机智能控制的 关键。因此，有必要对采煤机煤岩截割状态识别技术进行研究，进而提高采煤机 的智能控制水平。 本文以采煤机截割声音信号和摇臂振动信号为信号源， 以获取采煤机煤岩截 割状态为目标，设计信号的特征提取算法，研究基于改进 RBF 神经网络和 D-S 证据理论的多传感信息融合识别方法，实现采煤机煤岩截割状态的准确识别。论 文的主要工作及研究成果如下 （1）分析了采煤机结构与工作原理，并结合截割煤岩的具体过程，选取了 截割声音信号和摇臂振动信号作为煤岩截割状态识别依据， 利用截割声音信号和 摇臂两轴振动信号， 搭建了基于多传感信息融合的采煤机煤岩截割状态识别系统 架构，并设计了识别流程。 （2）研究了针对截割声音信号和摇臂两轴振动信号的小波阈值去噪方法， 提出了基于改进引力搜索算法优化的信号变分模态分解方法， 并基于包络熵和峭 度信息提取了分解信号的高维特征向量，通过主成分分析对高维特征向量降维， 进而获取了信号中表征煤岩截割状态的关键特征信息。 （3）设计了基于多传感信息融合的采煤机煤岩截割状态识别模型，提出了 基于改进果蝇优化算法优化 RBF 神经网络的模式识别方法，实现了单一传感信 号下的煤岩截割状态识别，利用基于证据关联系数的 D-S 证据理论对三个神经 网络的识别结果进行了决策级融合，解决了冲突证据融合效果差的问题。 为了验证本文的研究成果， 基于课题组在江苏省矿山智能采掘装备协同创新 中心搭建的采煤机煤岩截割实验平台开展了相关实验，结果表明基于本文所提 方法可以有效提取出截割声音信号和摇臂两轴振动信号中表征煤岩截割状态的 关键特征信息，实现了基于多传感信息融合的采煤机煤岩截割状态识别，识别结 果准确率为 96.5，验证了所提方法的正确性和有效性。本课题的研究对于提高 采煤机智能化水平，推动综采工作面的“少人化”或“无人化”发展具有重要意义。 该论文有图 54 幅，表 27 个，参考文献 110 篇。 关键词关键词采煤机；声音信号；振动信号；多传感信息融合；煤岩截割状态识别 万方数据 II Abstract The shearer is one of core equipments in the fully mechanized mining face, accurate recognition of shearer coal-rock cutting status is the key to realize the intelligent fully mechanized mining face. Therefore, it is necessary to study the recognition technology of shearer coal-rock cutting status. This can improve the intelligent control level of the shearer. This thesis take the cutting sound signal and rocker arm vibration signal as the signal sources to identify the shearer coal-rock cutting status. To realize the accurate recognition of the shearer coal-rock cutting status, it is necessary to design feature extraction algorithm of signals, and study the multi-sensor ination fusion recognition which is based on the improved RBF neural network and D-S evidence. The main work and research results of the thesis are as follows 1 The structure and working mechainism of the shearer are analyzed. Combined with the specific coal-rock cutting process, the cutting sound signal and the rocker arm vibration signal are selected as the basis to identify coal-rock cutting status. By using the cutting sound signal and two-axis vibration signal of the rocker arm, the structure of the shearer coal-rock cutting status recognition system based on multi-sensor ination fusion technology is established, and designed the recognition flow. 2 The wavelet threshold denoising for the cutting sound signal and the two-axis vibration signal of the rocker arm is studied. A variational mode decomposition for signal based on improved gravity search algorithm is proposed. The high-dimensional eigenvectors of the decomposed signals are extracted based on envelope entropy and kurtosis. The principal component analysis is used to reduce the dimension of the high-dimensional eigenvectors, and then extracted the key feature ination to represent the coal-rock cutting status. 3 The recognition model of sheaer coal-rock cutting status based on multi-sensor fusion is designed. To realize the recognition of coal-rock cutting status under single sensing signal, the pattern recognition based on RBF neural network which is optimized by improved fruit fly optimization algorithm is proposed. The D-S evidence theory which is based on the evidence correlation coefficient, is used to make the decision-level fusion of recognition results obtained by three neural networks. The poor effect of the conflict evidence fusion is solved. 万方数据 III In order to verify the research results of this thesis, the related experiments are carried out based on the shearer coal-rock cutting experimental plat, which is constructed by the research group in the Collaborative Innovation Center of Mine Intelligent Mining Equipment in Jiangsu Province. The experimental results obtained by the proposed in the thesis show that the key characteristics of the coal-rock cutting status in the cutting sound signal and the two-axis vibration signal of the rocker arm can be effectively extracted, the recognition of shearer coal-rock cutting status based on multi-sensor ination fusion is realized, and the accuracy rate of recognition results is 96.5, this verified the correctness and effectiveness of the proposed . The research of this subject have great significance for improving the intelligent level of shearer, and promoting the development of “less people“ or “unmanned“ in fully mechanized mining face. In this thesis, there are 54 figures, 27 tables and 110 references. Key words shearer; sound signal; vibration signal; multi-sensor ination fusion; recognition of coal-rock cutting status 万方数据 目目录录 IV 摘摘 要要 ........................................................................................................................... I 目目 录录 ................................................................................................ ........................ IV 图清单图清单 ................................................................................................ ......................VIII 表清单表清单 ................................................................................................ ........................ XI 变量注释表变量注释表 ................................................................ ............................................. XIII 1 绪论绪论 ............................................................................................................................ 1 1.1 课题来源及背景...................................................................................................... 1 1.2 课题研究现状及存在问题...................................................................................... 2 1.3 课题研究内容与方法.............................................................................................. 5 1.4 课题研究意义.......................................................................................................... 6 1.5 论文结构.................................................................................................................. 6 2 采煤机煤岩截割状态识别系统设计采煤机煤岩截割状态识别系统设计 ........................................................................ 8 2.1 采煤机结构与工作原理.......................................................................................... 8 2.2 采煤机截割煤岩过程............................................................................................ 12 2.3 采煤机煤岩截割状态的表征信号选取................................................................ 14 2.4 基于多传感信息融合的采煤机煤岩截割状态识别系统设计............................ 18 2.5 本章小结................................................................................................................ 20 3 采煤机截割声音信号与摇臂振动信号特征提取方法研究采煤机截割声音信号与摇臂振动信号特征提取方法研究 .................................. 21 3.1 信号的小波阈值去噪............................................................................................ 21 3.2 基于改进引力搜索算法优化的信号变分模态分解............................................ 28 3.3 信号的特征向量提取与降维................................................................................ 40 3.4 本章小结................................................................................................................ 46 4 基于多传感信息融合的采煤机煤岩截割状态识别方法研究基于多传感信息融合的采煤机煤岩截割状态识别方法研究 .............................. 47 4.1 采煤机煤岩截割状态的多传感信息融合识别模型............................................ 47 4.2 基于单一信号的采煤机煤岩截割状态识别方法................................................ 50 4.3 采煤机煤岩截割状态识别结果决策级融合方法................................................ 60 4.4 本章小结................................................................................................................ 67 5 实验研究实验研究 .................................................................................................................. 68 5.1 实验平台搭建与实验方案设计............................................................................ 68 万方数据 V 5.2 实验结果分析........................................................................................................ 72 5.3 本章小结............................................................................................................... 78 6 总结与展望总结与展望 .............................................................................................................. 79 6.1 总结........................................................................................................................ 79 6.2 展望........................................................................................................................ 80 参考文献参考文献 ..................................................................................................................... 81 作者简历作者简历 .....................................................................................................................88 学位论文原创性声明学位论文原创性声明 .................................................................................................89 学位论文数据集学位论文数据集 .........................................................................................................90 万方数据 Contents Abstract....................................................................................................................... II Contents..................................................................................................................... VI List of Figures..........................................................................................................VIII List of Tables.............................................................................................................XI List of Variables.....................................................................................................XIII 1 Introduction ............................................................................................................... 1 1.1 Origin and Background ............................................................................................ 1 1.2 Research Status and Problems ................................................................................. 2 1.3 Research Contents and s .............................................................................. 5 1.4 Research Significance .............................................................................................. 6 1.5 Structure of Thesis ................................................................................................... 6 2 Design of Shearer Coal-rock Cutting Status Recognition System .................................. 8 2.1 Structure and Working Mechanism of Shearer ........................................................ 8 2.2 Coal-rock Cutting Process of Shearer .................................................................... 12 2.3 Characterization Signal Selection of Shearer Coal-rock Cutting Status ................ 14 2.4 Design of Shearer Coal-rock Cutting Status Recognition System Based on Multi- sensor Ination Fusion ........................................................................................... 18 2.5 Summary ................................................................................................................ 20 3 Research on Feature Extraction of Shearer Cutting Sound Signal and Rocker Arm Vibration Signal ..................................................................................... 21 3.1 Signal Wavelet Threshold Denoising ..................................................................... 21 3.2 Signal Decomposition Based on Variational Mode Decomposition Optimized by Improved Gravitational Search Algorithm ................................................................... 28 3.3 Signals Feature Vector Extraction and Dimensionality Reduction ........................ 40 3.4 Summary ................................................................................................................ 46 4 Research on Recognition of Shearer Coal-rock Cutting Status Based on Multi- sensor Ination Fusion ............................................................................................ 47 4.1 Recognition Model of Shearer Coal-rock Cutting Status Based on Multi-sensor Ination Fusion ....................................................................................................... 47 4.2 Recognition of Shearer Coal-rock Cutting Status Based on Single Signal VI 万方数据 VII ...................................................................................................................................... 50 4.3 Recognition Results Decision-level Fusion of Shearer Coal-rock Cutting Status ............................................................................................................................ 60 4.4 Summary ................................................................................................................ 67 5 Experimental Research .......................................................................................... 68 5.1 Construction of Experimental Plat and Design of Experimental Scheme ..... 68 5.2 Analysis of Experimental Results .......................................................................... 72 5.3 Summary ........................................................................................