掘进机器人仿形截割建模与轨迹仿真研究

针对掘进机器人掘进过程中,尖峰负荷影响,及切割机构的非平稳振动,引起欠挖和超挖,机器损耗加剧等问题,以悬臂式掘进机巷道自动成形为研究对象,建立了掘进机仿形截割的动态数学模型,推导出截割头运动到半圆拱形巷道边界时,悬臂偏移角和油缸伸长量之间的通用公式,在使用数字液压缸按照所给巷道运动时,通过控制各个油缸伸长量来实现掘进机的自动控制机理。采用MATLAB软件仿真了截割头仿形截割运动过程,取得和实际工况相一致的结果,验证了模型的可行性和优越性,为掘进机器人研究奠定了通用的控制理论基础。     

掘进机器人巷道断面记忆截割控制策略研究

矿井巷道开采的过程中司机的视线和所处的环境受粉尘,水雾,光线等的影响,导致在巷道断面在截割的过程中出现超挖或欠挖的现象,使得断面的形状尺寸不规则,进而给后面的顶板支护工作也带来困难,严重影响着巷道掘进的进度;为了解决以上问题,提出掘进机器人巷道断面记忆截割控制策略;记忆截割就是司机对断面初次截割的过程中把截割数据包括截割头的位置、截割臂摆动的角度和速度等数据存储在专门的存储器中,作为之后截割的参考数据直接调用,省去司机频繁的操作,大大提高截割的效率;通过MATLAB仿真和EBZ160试验研究可知,截割头摆动误差可控制在5cm以内,表明掘进机器人巷道断面记忆截割控制策略可以更加有效地控制掘进机截割出整齐的断面,为掘进机巷道断面自动成型奠定了理论依据。     

悬臂式掘进机截割机构状态诊断策略研究

悬臂式掘进机工作中常会遇到煤层硬度突然变大,使截割机构发生异常,甚至导致截齿断裂、减速器齿轮受损、截割电机转子不平衡等故障.为了实现对掘进机截割机构运行状态的远程监控,在分析掘进机截割机构工作原理及重要部件频率特征的基础上,采用Hilbert-Huang分析方法,在MATLAB中仿真得到了四种不同状态下振动信号的频谱特征和能量特征,基于支持向量机算法设计了系统的多故障分类器,通过仿真测试验证了上述分类器能有效地诊断出系统的故障部件,为实现掘进机截割机构运行状态的远程监控奠定了基础.     

掘进机恒功率自适应调速与记忆截割系统研究

由于掘进机在进给过程中截割头工作面负载变化存在随机性且幅度很大,设计并开发一套以可编程序控制器为数据采集和控制核心,上位机采用组态软件构建远程监控系统的掘进机自适应控制系统,采用多信息融合技术综合分析负载的变化,实现恒功率自适应调速,同时采用记忆截割与自适应调节相结合的控制方法,在记忆截割的基础上根据反馈信号进行局部调节,更新记忆曲线,保证掘进机的恒功率进给,延长掘进机截割头的使用寿命.     

数字孪生驱动掘进机远程自动截割控制技术

目前掘进机远程截割控制方法大多基于视频监控与平面信息,巷道断面成形质量取决于人工操作经验,可靠性较低。针对该问题,研究了数字孪生驱动掘进机远程自动截割控制技术,从数据感知、数据驱动、数据传输、远程自动截割控制等方面介绍了数字孪生驱动掘进机自动截割的实现过程。采用视觉测量技术获取掘进机位姿数据、气体传感器等获取掘进环境数据,构成数字孪生驱动的数据来源;建立掘进机虚拟模型和虚拟场景并完成虚拟模型动作编程及虚拟模型与虚拟场景的耦合,通过求解掘进机运动学正逆解,实现掘进机与虚拟模型交互;基于三次多项式方法进行巷道断面"S"形截割轨迹规划,设计反馈线性积分滑模控制器实现截割轨迹跟踪控制;采用MySQL数据库作为数据交互媒介,通过井下工业以太环网及矿用5G技术实现数据远程传输。实验结果表明,掘进机截割头能稳定跟随规划轨迹,且同一时刻虚拟界面显示的掘进机截割头位姿与实际位姿一致。     

悬臂式掘进机截割机构振动特性研究

悬臂式掘进机工作中常会遇到煤层硬度突然变大,使截割机构发生异常,甚至导致截齿断裂、减速器齿轮受损、截割电机转子不平衡等故障,为了实现对掘进机截割机构运行状态的远程监控,在分析掘进机截割机构工作原理及重要部件频率特征的基础上,提出了采用Hilbert-Huang分析方法的掘进机截割机构异常振动特性研究,在MATLAB中进行了仿真,得到了截齿断裂、减速器齿轮断齿、截割电机转子不平衡状态下振动信号的频谱特征和能量特征,为实现掘进机截割机构运行状态的远程监控奠定了基础.     

纵轴式掘进机截割头辅助设计软件开发及应用

基于煤岩截割机理及截割头设计理论,采用MATLAB与EXCEL开发纵轴式掘进机截割头辅助设计及载荷计算软件。该软件可绘制截齿排列图、载荷图以及切屑图,同时生成载荷TXT文本,为截割头参数化建模以及掘进机动态仿真提供数据。使用该软件可快捷研究各个设计参数对掘进机截割性能的影响,找到最优的截割头设计方案。以EBZ220型掘进机截割头的设计为工程对象,以截割比能耗最低为目标,找到最适合该型号掘进机的截割头设计参数,生成截齿排列均匀、结构合理的截割头实体模型。为纵轴式掘进机截割头设计提供了一种新的方法,具有重要的理论意义及工程应用价值。     

悬臂式掘进机自动控制系统设计

针对目前悬臂式掘进机机身定位存在精度低、自动化控制性能差和工作效率低等问题,设计了悬臂式掘进机自动控制系统,详细介绍了系统硬件、软件的设计。该系统采用姿态检测技术和自动截割技术,实现了对悬臂式掘进机的精确截割与自动控制。     

基于模糊PID控制的掘进机恒功率控制系统的研究

针对悬臂式掘进机截割过程中负载变化剧烈、随机性大的问题,利用PLC和模糊PID控制实现对掘进机截割电机的恒功率控制,建立了仿真模型,并利用Simulink进行了仿真分析.结果表明:通过PLC和模糊PID控制的掘进机截割恒功率控制系统提高了系统的控制精度和稳定性,使掘进机能够根据不同的煤岩特性,调节摆动速度,提高了掘进机的工作效率.     

截割头载荷对掘进机机身偏向角的影响规律分析

为研究悬臂式掘进机截割过程中截割头载荷对机身偏向角的影响规律,建立了掘进机机身偏向角动力学模型,根据模型求解的输入问题,提出了一种截割头载荷计算方法,并通过仿真计算得到截割头载荷与驱动油缸压力及悬臂截割位置之间的关系。对掘进机截割过程中机身偏向角的变化进行了仿真分析,得到了不同工况下和不同悬臂截割位置时截割头载荷对掘进机机身偏向角的影响规律:横向截割工况下,机身偏向角随截割头载荷的增大而增大,且在悬臂水平摆角为14°时达到最大值;纵向截割工况下,机身偏向角随截割头载荷的增大而减小,且悬臂垂直摆角越大,机身偏向角越小。     

Multi-agent system for tunnel-settlement monitoring: A case study in Shanghai

Special soil environment and geographic location expose tunnels in Shanghai to a higher risk of asymmetry change and non-homogeneous settlement that could make a threat to passenger safety and tunnel structure stability. Considering work efficiency and costs, current monitoring methods and frequency are not suitable for tunnel monitoring present stage requirements. To make sure tunnel under condition of economic sustainability, environmental sustainability and engineering safety, new technology and instrument for monitoring should be designed to break through. Besides, asynchrony and retardation existing in data acquisition and analysis makes monitoring task cannot reflect tunnel real-time status. This can make people unable to timely detect the occurrence and risk of tunnel disaster. The purpose of this article is to develop a tunnel settlement monitoring system with several intelligent functions for comprehensive evaluating tunnel healthy, generating seasonable warnings and guaranteeing construction sustainability. To facilitate the expansion of the system and data structure, agent techniques and condition based monitoring standard have been applied to establish the system with 9 action parts according to 3 different geospatial. Moreover, web service has also been used to provide human–computer interface and realize visual function, and meanwhile, all the procedure of the system was completed automatically. Additionally, the system was tested with a task in Shanghai tunnel, and the result shows the monitoring system can describe tunnel conditions, prevent tunnel hazards and assist managers to make appropriate decisions with less people and reduced costs.     

Development of decision support knowledge based system for tool wear diagnosis

This paper deals with the problems faced by small and medium sized metal cutting industries, with the perspective of tool monitoring. In a small or medium size metal cutting industry employing major metal cutting process, one of the primary problem is that of tool monitoring and wear diagnosis. The problem is of immediate concern especially in those industries where the processes or operations employed are flexible and production depends entirely on orders from customers. Due to a flexible manufacturing setup, almost all major metal cutting processers need to be carried out. However, it becomes increasingly difficult for such small or medium size metal cutting industries to employ skilled manpower for each operation as well as expert technicians to supervise the operation, and carry out fault diagnosis and tool monitoring. Also, the problem associated with tool monitoring is that human operator carrying out the monitoring has to rely either on observation such as ceasing of tool, rise in temperature, generation of fumes, noisy operation, vibrations, considerable change in shape etc, or by monitoring the quality of the finished product. Also, there can be instances where the operator does notice a symptom but does not have the expertise to identify the cause of the trouble. Errors in tool monitoring can lead to considerable damage both to the machine as well as the workpiece. On the other hand, if the tool is replaced before it reaches its useful life expectancy, it leads to unnecessary additional cost. A Decision Support Knowledge Based System (DSKBS) has therefore been developed in this paper with the above considerations. The DSKBS provides the user with a friendly environment to diagnose a particular tool wear and obtain the necessary repair or replacement instructions. The goal is to increase productivity, decrease cost of operation and enhance total quality and reliability of the operation.     

Calibration-based tool condition monitoring for repetitive machining operations

Reliable tool condition monitoring (TCM) system is essential for any machining process in mass production to control the part quality as well as reduce the machine tool downtime and maintenance costs. However, while various research studies have proposed their TCM systems, the complexity in setups with advanced decision-making algorithms and specificity in application to limited cutting conditions continue to complicate the implementation of these systems into practical scenarios. This study develops a very simple and flexible TCM system for repetitive machining operations. The proposed monitoring approach reduces the complexity of monitoring model by considering the important characteristic of repeatability in process which has been commonly found in the mass production scenario and implements the calibration procedure to improve the flexibility of the model application to actual machining processes with complex toolpath designs and variable cutting conditions. The selected cutting tools with specific tool conditions are used in the calibration phase to generate reference signals. In actual repetitive production, the collected signal generated by the cutting tool in each operation is compared with reference signals to identify the most similar condition of the reference tool through the proposed similarity analysis. To validate the performance, the current study demonstrates the application of proposed monitoring approach to monitor the tool wear in repetitive milling operations with complex toolpath, and the predicted tool wear progression is found to be in good agreement with experimental measurements during the machining of multiple parts over the entire tool life.     

超长引水隧洞安全监测系统组网研究

水工隧洞建设逐渐向长距离、大断面、大埋深方向发展,基于电测法的传统安全监测系统组网方式面临着信号传输衰减,高水头下绝缘保护难度大,工程成本高等难题,因此不再适用于此类隧洞。结合国内某超长引水隧洞安全监测系统的实际施工,通过:1)选用光纤光栅传感器;2)监测断面内传感器采用串联或并联组网;3)监测断面间通过G.652D光纤连接组网;4)各数据采集站通过接入主干公网或GPRS无线传输2种方式进行组网,提出一套完整的超长引水隧洞安全监测系统组网方案,并通过相关实验与实际工程运用,证明该组网方案在保证监测数据长距离可靠传输的同时,减少工程成本,为引水隧洞安全监测工作提供参考。     

基于多传感器的刀具状态监测系统

以铣削加工为对象,研究了多刃切削加工过程的刀具状态监测问题,从系统的角度分析了刀具状态的多传感器监测原理,并以此为依据确定了采用声发射（ＡＥ）传感器和动态切削力传感器可有效地监测加工过程。文中提出了一种多传感器信号的特征提取方法,该方法利用偏最小二乘法计算样本矩阵的本征值,根据置信因子确定特征维数。为验证该方法的有效性,建立了一个铣削加工实验系统,实验结果表明,该方法可在多种切削条件下获得较高的识别率。     

Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

One of the big challenges in machining is replacing the cutting tool at the right time. Carrying on the process with a dull tool may degrade the product quality. However, it may be unnecessary to change the cutting tool if it is still capable of continuing the cutting operation. Both of these cases could increase the production cost. Therefore, an effective tool condition monitoring system may reduce production cost and increase productivity. This paper presents a neural network based sensor fusion model for a tool wear monitoring system in turning operations. A wavelet packet tree approach was used for the analysis of the acquired signals, namely cutting strains in tool holder and motor current, and the extraction of wear-sensitive features. Once a list of possible features had been extracted, the dimension of the input feature space was reduced using principal component analysis. Novel strategies, such as the robustness of the developed ANN models against uncertainty in the input data, and the integration of the monitoring information to an optimization system in order to utilize the progressive tool wear information for selecting the optimum cutting conditions, are proposed and validated in manual turning operations. The approach is simple and flexible enough for online implementation.     

Zig Bee技术在隧道施工监测中的应用

设计了一种基于Zig Bee无线传感器网络的隧道施工无线监测系统,系统主要用于监测隧道内的应力和位移变化等相关参数,给隧道施工提供实时监控.介绍了系统组成原理,给出了硬件和软件的设计原理和框图.通过实现上位机软件对Zig Bee传输来的传感器数据发送到上位机处理、显示、报警.测试结果表明:该监控系统无线传输可靠、传输距...     

基于Profibus总线的公路隧道监控系统的数据集成

针对公路隧道监控系统中现场设备种类多、分布零散等问题,本文设计了基于Profibus总线的公路隧道监控系统。文中从硬件集成、通讯方式、数据集成和监控界面四个方面详细介绍了系统数据集成的过程。     

多协议标签交换流量工程隧道实时监测方法——MTRM

针对现有工作无法实时动态监测多协议标签交换(MPLS)流量工程(TE)隧道状态变化的问题,提出了一种MPLS TE隧道实时监测方法--MTRM。在网络中植入被动采集探针以采集OSPF-TE信令,以此为基础构建网络模型,使用隧道路径实时监测算法进行实时的隧道路径计算,最终实现动态监测。仿真实验在15个节点的MPLS网络中进行。结果表明,MTRM能够在5s之内监测到隧道变化,准确率超过90%。这种MPLS TE隧道实时监测方法,大大降低了MPLS网络管理和流量工程实施的难度,具有广阔的应用前景。     

On-line tool condition monitoring system with wavelet fuzzy neural network

In manufacturing systems such as flexible manufacturing systems (FMS), one of the most important issues is accurate detection of the tool conditions under given cutting conditions. An investigation is presented of a tool condition monitoring system (TCMS), which consists of a wavelet transform preprocessor for generating features from acoustic emission (AE) signals, followed by a high speed neural network with fuzzy inference for associating the preprocessor outputs with the appropriate decisions. A wavelet transform can decompose AE signals into different frequency bands in the time domain. The root mean square (RMS) values extracted from the decomposed signal for each frequency band were used as the monitoring feature. A fuzzy neural network (FNN) is proposed to describe the relationship between the tool conditions and the monitoring features; this requires less computation than a back propagation neural network (BPNN). The experimental results indicate the monitoring features have a low sensitivity to changes of the cutting conditions and FNN has a high monitoring success rate in a wide range of cutting conditions; TCMS with a wavelet fuzzy neural network is feasible.