基于组件的工程机械智能维修系统模型库研究

以构建工程机械智能维修系统体系结构模型为基础，运用COM组件化技术研究了工程机械智能维修系统模型库的设计思想、原理、方法及关键技术，并结合编程语言对系统模型库、模型进行了设计与实现。实践表明该方法提高了工程机械智能的维修效率与维修能力。     

工程机械维修保障远程支援系统应用分析

提出了工程机械维修保障远程支援系统的功能结构、软硬件设备和计算机网络拓扑结构.讨论了系统实现的关键技术问题,分析了远程支援技术在工程机械维修保障方面的应用实例.通过使用该系统,可提高工程机械维修保障能力.     

基于BP神经网络的摊铺机智能故障诊断

针对大型复杂的工程机械，建立基于BP(逆向传播)神经网络的智能故障诊断系统。给出了该系统的诊断方法和故障诊断步骤，并提供了对工程机械液压系统故障诊断的调试实例。     

精益思想在工程机械维修中的应用

介绍了精益思想和精益维修的概念和内涵及其产生和发展情况,分析了当前工程机械维修中采用的维修思想及其存在的普遍性问题.在此基础上将精益思想应用于工程机械维修,研究了工程机械精益维修过程和持续改进路线,分析了开展精益维修工程的必要性和实现工程机械全面精益的重要作用和意义.     

运用蓝牙技术远程诊断工程机械的故障

建立基于网络平台的工程机械状态检测与故障诊断系统,可实现工程机械用户与维修或生产厂家的在线信息与技术交流,实现实时故障检测和诊断;确定各项诊断指标和最佳预防维修时间,改善工程机械运行状况,提高工程机械工作可靠性及使用寿命;维修或生产厂家也获得了宝贵的实际工况参数,为技术改进提供了数据储备。     

基于GPRS的工程机械远程监控系统设计

为了降低工程机械的故障发生频率并提高机器的维修效率,开发了基于GPRS网络的工程机械远程监控系统。系统以三菱PLC为控制器,GPRS DTU为通信工具,Delphi开发的软件为终端操作界面,实现了对现场作业工程机械关键参数的采集、分析、远程传输和报警。     

创新方法，提高工程机械维修的水平和质量

本文主要分析了工程机械维修工作，论述了如何创新方法，提高工程机械维修水平和质量，提出了一些个人的见解和看法，以及一些对策和措施，以期可以为今后工程机械维修工作提供参考。     

化工版工程机械类精品图书推荐

图解工程机械底盘构造与维修本书主要介绍了使用最为广泛的工程机械(推土机、装载机、平地机、挖掘机、叉车等)底盘的结构、工作原理及维护、维修等。内容上以我国生产的工程机械为主,同时适当介绍了国外较先进的同类机型,按照系统分章,主要包括传动系统、行驶系统、转向系统、制动系统。全书采用图解的方式,实用可靠,深入浅出,便于自学。     

其它工程机械

GJ20051155 工程机械野外修理车 [刊,中]/冯永保…//工程机械.—2004,35(6).—9～13介绍了工程机械修理车的总体构成,主要由运载车、检测维修方舱、自备发动机组、检测诊断仪器、维修设备、维修工具等组成。并阐述了工程机械发动机综合检测系统、液压系统的介入式和非介入式故障检测系统、多功能液压工作站等重     

虚拟仿真实验在工程机械结构原理与维修教学中的应用

针对工程机械结构原理与维修教学需要,分析虚拟仿真实验的基本原理,设计涵盖7种常用工程机械的虚拟维修训练系统。基于虚拟仿真技术的交互控制原理,完成了自主学习、虚拟拆装训练及考核评价3个主要功能模块的设计与开发。教学实践表明,基于虚拟仿真技术的工程机械虚拟维修训练系统互动性好、沉浸感强,可以有效改善工程机械结构原理与维修教学效果。     

Prediction of surface roughness in CNC end milling by machine vision system using artificial neural network based on 2D Fourier transform

This paper presents a system for automated, non-contact, and flexible prediction of surface roughness of end-milled parts through a machine vision system which is integrated with an artificial neural network (ANN). The images of milled surface grabbed by the machine vision system could be extracted using the algorithm developed in this work, in the spatial frequency domain using a two-dimensional Fourier transform to get the features of image texture (major peak frequency F ₁, principal component magnitude squared value F ₂, and the average gray level G _a). Since F1 is the distance between the major peak and the origin, it is a robust measure to overcome the effect of lighting of the environment. The periodically occurring features such as feed marks and tool marks present in the gray-level image can be easily observed from the principal component magnitude squared value F _2. The experimental machining variables speed S, feedrate F, depth of cut D, and the response extracted image variables F ₁, F ₂, and G _a could be used as input data, and the response surface roughness R _a measured by Surfcorder SE-1100 (traditional stylus method) could be used as output data of an ANN ability to construct the relationships between input and output variables. The ANN was trained using the back-propagation algorithm developed in this work due to its superior strength in pattern recognition and reasonable speed. Using the trained ANN, the experimental result had shown that the surface roughness of milled parts predicted by machine vision system over a wide range of machining conditions could be got with a reasonable accuracy compared with those measured by traditional stylus method. Compared with the stylus method, the constructed machine vision system is a useful method for prediction of the surface roughness faster, with a lower price, and lower environment noise in manufacturing process. Experimental results have shown that the proposed machine vision system can be implemented for automated prediction of surface roughness with accuracy of 97.53%. The results are encouraging that machine vision system can be extended to many real-time industrial prediction applications.     

基于人工神经网络的变切削条件下钻头磨损监控

基于人工神经网络变切削条件下的钻头磨损监控系统，以机床主轴和进给轴的电机功率（电流）信号的为监控信号，并通过机床的速度向量识别机床的加工状态；通用对监控信号的提取和预处理，得到人工神经网络模型的输入（有效切削功率和切削用量）用３层ＢＰ网络对钻头的磨损量进行预报。     

基于“电气特征值分析”的数控机床维修技术

从机床控制系统的典型电气特征参数检查的角度出发,举例说明了基于“电气特征值分析”的维修原理和方法。维修人员可以根据实际测得的机床控制系统的相关参数量（如：电压、电流、电阻及各类开关的状态等数值）与系统特征值进行比对,即可快速地找到故障原因,并排除故障,其关键是对数控机床电气特征值的恰当选取、特征值库的建立以及准确获得特征值。有效地利用这种方法对于提高维修人员的工作效率有很大的帮助。     

基于量子框架的汽车电控系统软件总线研究

鉴于汽车电控系统属于嵌入式系统领域硬件种类繁多,系统软件的开发、更新和维护工作困难,采用层次状态机（HSM）建模的方法,并结合量子框架（QF）的实现技术,建立了一种汽车电控系统的“轻量级”软件总线;在此基础上开发应用软件,缩短了开发周期,提高了应用软件的开放性、可靠性和可维护性。     

人工神经网络及其在机械工程领域中的应用

在论述人工神经网络模型的特点和性质的基础上，综述国内机械工程领域研究与应用人工神经网络技术的现状与发展，介绍最常用的模型种类，举例说明这类模型在机械制造和机械设计各环节中的实际应用，并阐述了完善模型结构，改进建模方法方面的基础性研究工作，从内容，深度，实际效果和软硬件方面评述当前在人工神经网络模型研究与应用中的不足之处，指出今后努力的方向和潜在应用领域。     

工程机械的绿色维修

工程机械绿色维修是指综合考虑在资源利用率最高和对环境污染最小的条件下,使工程机械保持或恢复到规定状态的维修活动。其目标是保持和恢复装备的规定状态和实现对环境影响最小。文中给出了绿色维修的概念、绿色维修的系统模型,论述了绿色维修和绿色维修性的有关内容、绿色维修实施的主要环节及技术,并对促进实施应采取的措施做了简要阐述。     

Hybrid modified evolutionary particle swarm optimisation-time varying acceleration coefficient-artificial neural network for power transformer fault diagnosis

In power transformer fault diagnosis, dissolved gas analysis (DGA) has been widely used to identify the type of the fault. The common methods of DGA are IEC 60599 method, Doenenberg’s ratio method and Roger’s ratio method. The accuracy of the DGA diagnosis will determine the cost, duration and workload of the maintenance since it can influence the error in the maintenance. Although DGA methods have been used widely, sometimes they still yield incorrect diagnosis results. Thus, many works on transformer fault diagnosis have been proposed previously, which include artificial intelligence methods, to improve the accuracy of transformer fault diagnosis. However, the accuracy of the previously reported works is believed to have rooms for improvement. Therefore, in this work, hybrid modified evolutionary particle swarm optimisation-time varying acceleration coefficient (MEPSO-TVAC)-artificial neural network (ANN) was proposed for transformer fault diagnosis based on dissolved gas data. This is due to these two methods have never been proposed for transformer fault diagnosis in the past. The performance of the ANN was optimised through the proposed MEPSO-TVAC. The superiority of the proposed method was demonstrated through comparison with the existing DGA methods, unoptimised ANN and previously reported methods in literatures. The comparison shows that the proposed hybrid MEPSO-TVAC-ANN obtained the highest accuracy among all methods, which can then be used for power transformer fault diagnosis.     

数控机床伺服系统故障诊断五例

数控机床的伺服系统是机床主体和数控装置CNC的联系环节,是数控机床的重要组成部分,本文结合工作中数控机床的故障现象,分析伺服系统在实际加工过程中所出现故障现象的判断及形成的原因,并根据多年的经验,总结出了一些适合现场维修的检查手段及故障排除方法,提出了有针对性的解决措施,对数控工程技术人员有一定的参考指导意义。     

基于大量故障实例的故障诊断系统设计

一些具有组分结构的系统（如机载火力控制系统）相当复杂,要想获得全套系统精确的故障诊断数学模型是非常困难的。利用维修人员在基层级和中继级获得的大量故障实例,采用神经网络和专家系统技术,则可以设计出一种基于症状知识的故障诊断系统。该故障诊断系统主要由知识库、专家规则诊断单元和神经网络诊断单元组成,其神经网络诊断单元随着故障实例训练的增加而不断成熟,专家规则诊断单元则可以通过神经网络对故障实例的训练与仿真来完善和扩充,具有很强的自学习功能和专家规则不能包容的新故障诊断能力。     

Integrated approach for diagnostics and prognostics of HP LNG pump based on health state probability estimation

Effective machine fault prognostic technologies can lead to elimination of unscheduled downtime and increase machine useful life and consequently lead to reduction of maintenance costs as well as prevention of human casualties in real engineering asset management. This paper presents a technique for accurate assessment of the remnant life of machines based on health state probability estimation technique and historical failure knowledge embedded in the closed loop diagnostic and prognostic system. To estimate a discrete machine degradation state which can represent the complex nature of machine degradation effectively, the proposed prognostic model employed a classification algorithm which can use a number of damage sensitive features compared to conventional time series analysis techniques for accurate long-term prediction. To validate the feasibility of the proposed model, the five different level data of typical four faults from High Pressure Liquefied Natural Gas (HP-LNG) pumps were used for the comparison of intelligent diagnostic test using five different classification algorithms. In addition, two sets of impeller-rub data were analysed and employed to predict the remnant life of pump based on estimation of health state probability using the Support Vector Machine (SVM) classifier. The results obtained were very encouraging and showed that the proposed prognostics system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications.