应用PMC进行数控机床故障诊断的方法

故障诊断是数控机床维修的前提,本文介绍了PMC在数控机床中的基本作用及其信号传递方式,并以FANUC系列数控机床的典型故障为例,阐述了应用PMC对数控机床故障进行诊断的方法。     

HNC-21MD加工中心PMC参数故障排除的方法

PMC参数及程序发生变化或改动,会直接影响数控机床的加工性能,是引起生产中数控机床加工精度异常故障的常见原因。本文详述了通过故障诊断方法来排除具体的华中数控系统HNC-21MD的PMC参数故障的过程。     

FANUC 0i系统外围故障报警的诊断

研究了数控机床报警的分类,给出了报警信息的查看方法.由PMC引起的外围故障是数控机床运行过程中最常见的故障.通过一个实例介绍了外围故障诊断和排除的基本思路.     

基于华中8系统PMC线圈无输出的研究

研究数控机床发生故障的原因及排查方法对提升数控机床维修能力和改善机床设计漏洞具有积极意义。以华中HNC-818BM数控系统为研究对象,探讨PMC条件满足、线圈无输出这一故障现象,结合企业维修实例分析得出双线圈、子程序调用、数据复位指令和赋值语句等多个因素均会导致数控机床发生PMC线圈无输出,并提出了故障解决方法。该研究对数控机床故障诊断和故障处理具有一定的参考价值。     

FANUC 0系统维修知识讲座 第11讲 FANUC系统的PMC控制程序指令（上）

本讲将对FANUC系统的PMC控制程序基本概 和逻辑“非”(也称“变反”,NOT),如表2所示。念和功能作简要介绍。 一、梯形图的基本概念 设计PMC控制程序是从编制梯形图开始的。梯形图由继电器接点、线圈符号及功能指令模块图组成。     

FANUC0iD系统数控机床进给轴故障诊断与排除

研究了数控机床进给伺服系统的故障形式,以进给轴运动故障为重点,分析了故障诊断与排除的流程。从伺服初始化设定和相关参数设置入手,详细介绍了伺服参数的设定方法。在伺服参数设定正确的基础上,又进一步介绍了与进给轴运动相关的PMC程序编制方法,从而系统地为进给轴故障诊断与排除打好基础。     

基于PMC在数控机床故障诊断与维修中应用的研究

PMC作为数控机床的重要组件,在实现高效自动化作业的同时,还能够为数控机床的故障诊断提供参考。本文从PMC的功能分析入手,以具体实例来探讨PMC在数据机床故障排除中的作用,并从提升数控机床的使用效率方面,快速准确锁定故障源,为维修工程师提供参考建议。     

S36数控外圆磨床润滑系统保养周期的研究

本文介绍了数控外圆磨床S36润滑系统的电气原理图,详细分析了其润滑控制梯形图程序,结合系统的电气原理图和FANUC系统相应的PMC梯形图,阐述了润滑系统故障产生的根源及排除的基本思路。     

基于串行数据通信的数控机床故障诊断与维修

阐述了数控系统和计算机串口通信接口的软件和硬件特性,从数控机床维修的角度系统地分析了机床数据备份、还原,系统参数调试、PLC通讯、梯形图诊断等方法,并以西门子802C/S系统为例提出软硬件故障的实际解决方案。最后介绍通信技术在故障诊断维修中的最新应用。     

FANUC PMC的K参数在数控机床改进中的应用

通过两台加工中心存在的具体问题,分析了PMC梯形图,通过修改K参数,完善了机床的性能。     

模糊Kohonen神经网络的旋转机械故障诊断

针对传统故障诊断方法不能解决旋转机械故障诊断的模糊性问题,提出一种基于模糊Kohonen神经网络的故障诊断模型,通过模糊量化处理故障样本模式和在Kohonen网络中使用邻域函数自动调整权重程度的改进学习算法,较大提高了网络的学习速度和聚类能力,能对具有模糊性的复合故障进行诊断,是一种适合于复杂旋转机械故障诊断的有效可行的方法。     

基于PLC的自动控制系统的故障检测方法探析

机电设备的自动控制系统,是以可编程控制器为核心的自动控制系统,为提高系统的可靠性和容错能力,在硬件设计的基础上,通过梯形图软件的设计,实现了对系统故障的自动检测与处理.介绍了该系统实现仪器功能自检、故障动态检测、故障处理功能的基本思想与方法.     

裂纹-碰摩转子-轴承系统周期运动稳定性

建立了含有裂纹-碰摩耦合故障转子轴承系统的动力学模型,利用求解非线性非自治系统周期解的延拓打靶法和F loquet理论,研究了系统周期运动的稳定性。发现碰摩转子-轴承系统在不同的转速下会发生鞍结分岔、倍周期分岔和Hopf分岔等现象,裂纹-碰摩耦合故障转子-轴承系统具有不同于单一故障的独特的动力学特性。研究结果为转子-轴承系统故障诊断和安全运行提供了一定的参考。     

Semi-supervised weighted kernel clustering based on gravitational search for fault diagnosis

Supervised learning method, like support vector machine (SVM), has been widely applied in diagnosing known faults, however this kind of method fails to work correctly when new or unknown fault occurs. Traditional unsupervised kernel clustering can be used for unknown fault diagnosis, but it could not make use of the historical classification information to improve diagnosis accuracy. In this paper, a semi-supervised kernel clustering model is designed to diagnose known and unknown faults. At first, a novel semi-supervised weighted kernel clustering algorithm based on gravitational search (SWKC-GS) is proposed for clustering of dataset composed of labeled and unlabeled fault samples. The clustering model of SWKC-GS is defined based on wrong classification rate of labeled samples and fuzzy clustering index on the whole dataset. Gravitational search algorithm (GSA) is used to solve the clustering model, while centers of clusters, feature weights and parameter of kernel function are selected as optimization variables. And then, new fault samples are identified and diagnosed by calculating the weighted kernel distance between them and the fault cluster centers. If the fault samples are unknown, they will be added in historical dataset and the SWKC-GS is used to partition the mixed dataset and update the clustering results for diagnosing new fault. In experiments, the proposed method has been applied in fault diagnosis for rotatory bearing, while SWKC-GS has been compared not only with traditional clustering methods, but also with SVM and neural network, for known fault diagnosis. In addition, the proposed method has also been applied in unknown fault diagnosis. The results have shown effectiveness of the proposed method in achieving expected diagnosis accuracy for both known and unknown faults of rotatory bearing.     

CNC-implemented fault diagnosis and web-based remote services

Recently, the conventional controller of machine-tool has been increasingly replaced by the PC-based open architecture controller, which is independent of the CNC vendor and on which it is possible to implement user-defined application programs. This paper proposes CNC-implemented fault diagnosis and web-based remote services for machine-tool with open architecture CNC. The faults of CNC machine-tool are defined as the operational faults occupied by over 70% of all faults. The operational faults are unpredictable as they occur without any warning. Two diagnostic models, the switching function and the step switching function, were proposed in order to diagnose faults efficiently. The faults were automatically diagnosed through the fault diagnosis system using the two diagnostic models. A suitable interface environment between CNC and developed application modules was constructed for the internal function of CNC. In addition, a suitable web environment was constructed for remote services. The web service functions, such as remote monitoring and remote control, were implemented, and their operability was tested through the web. The results obtained through this research could be a model of fault diagnosis and remote servicing for machine-tool with open architecture CNC.     

盾构液压系统故障的现场检测与诊断探究

在全面分析盾构液压系统常见故障的基础上,针对施工现场所采用的简单故障诊断方法的不足,依据液压系统故障检测诊断技术的发展趋势,并结合已有的成熟技术,提出了一套盾构液压系统故障智能诊断系统.以螺旋输送机后闸门液压系统故障为例,进行了应用说明,能够为盾构施工现场的液压系统故障诊断提供指导.     

MODEL-BASED FAULT DIAGNOSIS OF INDUCTION MOTORS USING NON-STATIONARY SIGNAL SEGMENTATION

Effective detection and diagnosis of incipient faults is desirable for on-line condition assessment, product quality assurance and improved operational efficiency of induction motors running off the power supply mains. In this paper, an empirical model-based fault diagnosis system is developed for induction motors using recurrent dynamic neural networks and multiresolution signal processing methods. In addition to nameplate information required for the initial set-up, the proposed diagnosis system uses measured motor terminal currents and voltages, and motor speed. The effectiveness of the diagnosis system is demonstrated through staged motor faults of electrical and mechanical origin. The developed system is scalable to different power ratings and it has been successfully demonstrated with data from 2.2, 373 and 597 kW induction motors. Incremental tuning is used to adapt the diagnosis system during commissioning on an new motor, significantly reducing the system development time.     

滚动轴承外圈多点故障特征分析

滚动轴承发生多点故障时会产生复杂的振动特征,影响轴承故障诊断的准确性。针对滚动轴承外圈多点故障诊断特征的变化规律,考虑滚动轴承外圈故障数量、故障间隔和载荷分布对故障特征的影响,采用五自由度动力学模型进行仿真分析。通过龙格库塔法对动力学方程进行数值求解,分析了滚动轴承外圈单点故障和多点故障的诊断特征。结果表明:当外圈具有多点故障时,随着故障数量和故障间隔的变化,故障特征频率各谐波的幅值会发生变化;当多点故障满足载荷相等和一定的间隔关系时,故障特征频率值与故障数量之间呈现对应的倍数关系。通过滚动轴承多点故障模拟试验验证了结论的正确性。     

Multiple sensor fault diagnosis for dynamic processes

Modern industrial plants are usually large scaled and contain a great amount of sensors. Sensor fault diagnosis is crucial and necessary to process safety and optimal operation. This paper proposes a systematic approach to detect, isolate and identify multiple sensor faults for multivariate dynamic systems. The current work first defines deviation vectors for sensor observations, and further defines and derives the basic sensor fault matrix (BSFM), consisting of the normalized basic fault vectors, by several different methods. By projecting a process deviation vector to the space spanned by BSFM, this research uses a vector with the resulted weights on each direction for multiple sensor fault diagnosis. This study also proposes a novel monitoring index and derives corresponding sensor fault detectability. The study also utilizes that vector to isolate and identify multiple sensor faults, and discusses the isolatability and identifiability. Simulation examples and comparison with two conventional PCA-based contribution plots are presented to demonstrate the effectiveness of the proposed methodology.     

基于等角度采样的列车频带变化类故障诊断方法研究

列车轮对部件故障常会受到频带变化因素的影响,如采用一般旋转机械故障监测方法,因谱线整体迁移模糊,可能导致误诊或漏诊。针对这一问题,研究了各影响因素与故障特征之间的动态响应,基于故障机理建立了频带变化类故障的动力学模型,分析了影响因素下的故障特征。提出了基于等角度采样的监测诊断方法,结合列车轮对组件实际情况制定了监测诊断方案,并通过实例应用对该诊断方法的准确性进行了验证。 