采煤机截割部内漏水的危害性及治理

基于油液分析与铁谱技术,在线监测诊断采煤机截割部的润滑磨损故障。介绍采用铁谱技术定性定量测定润滑油中水分含量的方法;揭示截割部传动系统润滑条件恶化造成严重磨损及故障状态下磨损磨粒的宏微观特征;阐述润滑失效机制。分析内漏水对采煤机润滑系统危害的严重性,找出采煤机截割部内漏水源结点,采取治理措施,实际应用取得明显效果。     

铁谱技术及其应用(一)

本文概述了铁谱技术的基本原理与特点,特别评价了其在机器状态监测方面的发展。简要介绍了铁谱仪的原理、结构以及磨损颗粒识别等方面的知识。并根据国内外有关资料及作者近年来的实践,结合典型实例分析,分别阐述了铁谱技术在摩擦学基础研究和摩擦学系统状态监测中的应用。主要内容包括:一、前言;二、铁谱技术的基本原理与特点;三、铁谱仪原理与结构;四、磨损颗粒识别与定量参数的选择;五、铁谱技术在摩擦学基础研究中的应用;六、铁谱技术在摩擦学系统状态监测中的应用;七、铁谱技术的发展趋势。     

沾染检测技术在机器工况监测中的应用

机器在运转中产生的磨损微粒记录着机器部件跑合和磨损的历史,对使用过的润滑油样作沾染检测分析,能检测和评定机器的磨损状况,对机器工况实行监测。因而沾染检测技术开拓了摩擦学中磨损微粒形态学的研究,实现了对机器工作状态的监测、诊断以及机件损坏的预报。本文主要评论了沾染检测技术中的磁塞检测、光谱油分析和铁谱技术在机器工况监测方面的应用。     

在线铁谱数据分析方法的研究

在线铁谱仪已成功地用于机械设备的磨损状态监测,如何根据铁谱数据来分析机器磨损状态和磨损规律一直被人们所重视。本文利用自组织特征映射神经网络对齿轮整个磨损过程的铁谱数据进行分类,提出了齿轮不同磨损状态下磨粒的分布规律,根据这种规律可以识别齿轮的不同磨损状态。这对监测机械设备运行状态具有十分重要的意义。     

炼化设备油液分析诊断软件开发

介绍炼化设备油液分析诊断软件,该软件由铁谱定量分析模块、自动铁谱显微图像分析模块、油液分析数据库模块、磨损状态评判模块和诊断报告输出模块组成,通过铁谱分析仪、铁谱显微镜获取油样磨损信息,监测大型炼化设备润滑油品质量,并对磨损状况智能分级,为设备维护和管理提供依据。     

磨损磨粒的BP神经网络识别

介绍了铁谱分析技术对设备状态监测与故障诊断的方法;通过机械润滑油或液压油中微观磨损颗粒的分析来判断机器当前的工作状态.铁谱的计算机图像分析技术是近年来研究的热点.基于BP神经网络对磨损磨粒进行识别,提出了磨粒的分步识别策略,并以磨粒样本对网络进行训练,取得了较好的识别效果.     

磨损磨粒的BP神经网络识别

介绍了铁谱分析技术对设备状态监测与故障诊断的方法;通过机械润滑油或液压油中微观磨损颗粒的分析来判断机器当前的工作状态。铁谱的计算机图像分析技术是近年来研究的热点。基于BP神经网络对磨损磨粒进行识别,提出了磨粒的分步识别策略,并以磨粒样本都对网络进行训练,取得了较好的识别效果。     

磨粒特征随滑动磨损进程的变化规律

为提高基于磨粒的机器状态监测的准确性,研究了磨粒特征随滑动磨损进程的变化规律.在球一盘磨损试验机上模拟可靠润滑和润滑不足2种工况下的摩擦磨损试验,分析了磨损过程中不同磨损阶段的磨粒尺寸分布和磨粒表面粗糙度,探讨了磨损进程中磨粒尺寸分布与磨粒表面粗糙度之间的关系.研究结果表明磨损进程中的磨粒特征的变化对机器状态监测极为有效.     

基于在线铁谱图像的磨损机理智能辨识

基于状态的维护(Condition based maintenance,CBM)理念为机器健康状态维护提出了实时监测的新挑战。现有研究由于缺乏在线信息获取手段,磨损状态监测逐渐成为CBM的技术瓶颈。基于特征磨粒的磨损机理判断方法已经被广泛应用在离线磨损分析中,但是在线磨损机理的表征依然是一个很大的问题。针对基于在线铁谱图像的磨损机理开展研究。为了在一副在线铁谱图像中获得分离的磨粒图像,研究磨粒在在线铁谱传感器中的沉积机理。研究结果表明,磨粒链是图像中的主要形态,这是由于先前沉积的磨粒产生的局部磁场所致。设计一种依靠自适应调节沉积时间的在线磨粒沉积方法。运用该方法可以在在线铁谱图像中获得分离的磨粒,为特征磨粒的特征辨识提供了便利。参考分析铁谱知识,提取特征磨粒的4种形态学特征(当量尺寸、长径比、形状因子和分形维数)以综合表征4种典型磨损机理,包括正常、切削、疲劳、严重滑动磨损。采用反馈式人工神经网络构建自动磨损机理辨识模型。采用离线铁谱图像样本验证所建模型,结果表明该模型可以识别在线磨粒图像中的特征磨粒。对在线磨损机理表征方法进行了有意义的探索,所得研究成果将为在线磨损状态表征提供可行方法。     

某型涡轴航空发动机磨损状态及趋势预测研究

联合采用发射光谱和铁谱技术对某型航空涡轴发动机进行了磨损状态的分析研究,结果表明:发射光谱技术可准确预测发动机的损坏情况,铁谱技术通过分析磨粒特征,可判断发动机的运行状况、磨损机理及磨损类型,而发射光谱和铁谱技术的联合运用可以较准确地确定发动机润滑部件的磨损状态,诊断磨损故障及预测磨损趋势.     

Surface roughness evolutions in sliding wear process

Wear debris analysis is a technique for machine condition monitoring and fault diagnosis. One key issue that affects the application of wear debris analysis for machine condition monitoring is whether the morphology of the wear particles accurately depicts their original states and the surface morphology of the components from which the particles separate. This study aimed to investigate the evolution of the surface morphology of wear debris in relation to change in the surface morphology of wear components in sliding wear process. Sliding wear tests were conducted using a ball-on-disc tester under proper lubrication and improper lubrication conditions. The study of the particle size distribution and the surfaces of both the wear debris and the tested samples in relation to the wear condition and the wear rates of the wear components were carried out in this study. The evolutions of the surface topographies of both the wear debris and the wear components as wear progressed were investigated. This study has provided insight to the progress of material degradation through the study of wear debris. The results of this research have clearly demonstrated that: (a) there is a good correlation of the surface morphology of wear debris and that of the wear components, and (b) the surface morphology of wear debris contains valuable information for machine condition monitoring.     

Intelligent identification of wear mechanism via on-line ferrograph images

Condition based maintenance（CBM） issues a new challenge of real-time monitoring for machine health maintenance. Wear state monitoring becomes the bottle-neck of CBM due to the lack of on-line information acquiring means. The wear mechanism judgment with characteristic wear debris has been widely adopted in off-line wear analysis; however, on-line wear mechanism characterization remains a big problem. In this paper, the wear mechanism identification via on-line ferrograph images is studied. To obtain isolated wear debris in an on-line ferrograph image, the deposition mechanism of wear debris in on-line ferrograph sensor is studied. The study result shows wear debris chain is the main morphology due to local magnetic field around the deposited wear debris. Accordingly, an improved sampling route for on-line wear debris deposition is designed with focus on the self-adjustment deposition time. As a result, isolated wear debris can be obtained in an on-line image, which facilitates the feature extraction of characteristic wear debris. By referring to the knowledge of analytical ferrograph, four dimensionless morphological features, including equivalent dimension, length-width ratio, shape factor, and contour fractal dimension of characteristic wear debris are extracted for distinguishing four typical wear mechanisms including normal, cutting, fatigue, and severe sliding wear. Furthermore, a feed-forward neural network is adopted to construct an automatic wear mechanism identification model. By training with the samples from analytical ferrograph, the model might identify some typical characteristic wear debris in an on-line ferrograph image. This paper performs a meaningful exploratory for on-line wear mechanism analysis, and the obtained results will provide a feasible way for on-line wear state monitoring.     

设备磨损智能评价技术在石化大机组的应用

为提高石化转动设备磨损故障诊断自动化及智能化程度,探讨基于石化转动设备磨损监测信息挖掘技术的磨损智能分析与评价技术在乙烯压缩机运维中的应用。通过磨损智能分析与评价技术对机组运行状态的全面监测与评价,结果表明,磨损智能分析与评价技术能够准确反映设备磨损及润滑状态的劣化趋势,对机组的油品在线置换升级、机组运行工况调整、油品净化等主动性维护提供了及时准确的技术支持,保障了设备的长周期稳定运行。     

A high throughput inductive pulse sensor for online oil debris monitoring

A high throughput inductive pulse sensor based on inductive Coulter counting principle for detecting metallic wear debris in lubrication oil is presented. The device detects the passage of metallic debris by monitoring the inductance change of a two-layer planar coil with a meso-scale fluidic pipe crossing its center, which is designed to attain high throughput without sacrificing the sensitivity. The testing results using iron and copper particles ranging in size from 50 to 150 μm have demonstrated that the device is capable of detecting and distinguishing ferrous and non-ferrous metallic debris in lubrication oil with a high throughput.     

基于油液分析和时间序列模型的内燃机磨损状态监测方法

提出了一种基于油液分析和时间序列模型的内燃机磨损状态监测方法。将整个内燃机看作一个摩擦学系统,利用时间序列模型对摩擦学系统进行动态建模,建立能够表征内燃机磨损状态的数学模型。采用该模型对内燃机润滑油中各元素的变化趋势进行趋势分析,并应用实例对模型进行了检验。结果表明,该方法准确判断内燃机的磨损状态,为内燃机磨损状态监测提供了一种新的途径。     

Investigations on the Relationship between Wear Debris Residence Time in Lubrication Systems and Online Oil Sampling Interval

Lubrication systems have significant effects on the residence time of wear debris (RTWD), which limits the monitoring efficiency of the online debris sensor. The focus of this work is to investigate the relationship between RTWD in lubrication systems and online oil sampling interval. Firstly, a vector representation for the attenuation function of wear debris (AFWD) is introduced in order to depict the removal of wear debris with different sizes. Based on this, a mathematical model is developed to calculate the RTWD. A setting criterion for the online oil sampling interval is also proposed. Thereafter, RTWD in different size ranges was investigated experimentally, in which the concentration of wear debris larger than the micrometer rating of the filter decays to 10% of the initial concentration within 5?min. The results were consistent with the proposed model results. Moreover, we find that the online oil sampling interval must be determined by the residence time of large wear debris caused by abnormal wear. Otherwise, the online distortion monitoring results can result in false conclusions.     

Monitoring of Wind Turbine Gearbox Condition through Oil and Wear Debris Analysis: A Full-Scale Testing Perspective

Despite the wind industry's dramatic development during the past decade, it is still challenged by premature turbine subsystem/component failures, especially for turbines rated above 1 MW. Because a crane is needed for each replacement, gearboxes have been a focal point for improvement in reliability and availability. Condition monitoring (CM) is a technique that can help improve these factors, leading to reduced turbine operation and maintenance costs and, subsequently, lower cost of energy for wind power. Although technical benefits of CM for the wind industry are normally recognized, there is a lack of published information on the advantages and limitations of each CM technique confirmed by objective data from full-scale tests. This article presents first-hand oil and wear debris analysis results obtained through tests that were based on full-scale wind turbine gearboxes rated at 750 kW. The tests were conducted at the 2.5-MW dynamometer test facility at the National Wind Technology Center at the National Renewable Energy Laboratory. The gearboxes were tested in three conditions: run-in, healthy, and damaged. The investigated CM techniques include real-time oil condition and wear debris monitoring, both inline and online sensors, and offline oil sample and wear debris analysis, both onsite and offsite laboratories. The reported results and observations help increase wind industry awareness of the benefits and limitations of oil and debris analysis technologies and highlight the challenges in these technologies and other tribological fields for the Society of Tribologists and Lubrication Engineers and other organizations to help address, leading to extended gearbox service life.