高精度在线油液监测系统设计

在分析了油液污染产生的原因及其对机械设备的危害的基础上,根据油液中悬浮颗粒对光线的散射和吸收的特性,提出了利用AVR处理器控制激光的入射光强,采用双光路光纤传感器,通过检测激光透射过油液后的透射光强,并经光电转换后输入AVR进行处理,得到油液中磨粒信息的设计方法,从而实现了对设备的实时在线状态监测。     

高精度在线油液监测系统设计

在分析了油液污染产生的原因及其对机械设备的危害的基础上,根据油液中悬浮颗粒对光线的散射和吸收的特性,提出了利用AVR处理器控制激光的入射光强,采用双光路光纤传感器。通过检测激光透射过油液后的透射光强,并经光电转换后输入AVR进行处理,得到油液中磨粒信息的设计方法,从而实现了对设备的实时在线状态监测。     

高精度在线油液监测系统设计

在分析了油液污染产生的原因及其对机械设备的危害的基础上,根据油液中悬浮颗粒对光线的散射和吸收的特性 , 提出了利用AVR处理器控制激光的入射光强,采用双光路光纤传感器,通过检测激光透射过油液后的透射光强,并经光电转换后输入AVR进行处理,得到油液中磨粒信息的设计方法,从而实现对设备的实时在线状态监测.     

基于在线油液监测的海上钻井平台发动机故障报警预测

在线油液监测表征机器异常磨损状态信息是设备故障诊断技术的核心和瓶颈。针对中海油深圳分公司西江302海上钻井平台目前存在的油液离线检测方式不能及时反映润滑状态等问题,对海上钻井平台5台发动机组开展油液在线实时监测。介绍在线监测系统平台的组成和在线监测的实施方案。针对实时监测出现的油液中大磨粒超标故障报警,结合离线铁谱分析证实监测结果的准确性。     

油液在线监测传感器技术

油液在线监测是设备润滑磨损状态监测与实时诊断技术的重要发展方向,而监测仪器的开发与选择是这一故障诊断技术实现准确监测应用的关键。在对当前国内外油液磨损颗粒在线监测传感器、油质在线监测传感器、油液多信息集成在线监测仪器及应用技术介绍基础上,给予了当前油液在线监测传感器技术的相关评述,为油液在线监测技术的研究、应用及推广提供一定帮助。     

在线油液监测技术及其应用

在线监测是设备油液监测的重要发展方向。通过油液监测技术在各领域的应用状况与国内外在线传感器发展情况,提出了一种监测系统的构建模型,并讨论了在线油液监测未来的发展方向。     

串口和以太网通信技术在油液在线监测系统中的应用

油液在线监测是设备润滑磨损状态监测技术发展的重要方向,而通信技术在油液在线监测控制系统中有着举足轻重的作用.针对设备润滑磨损监控开发的油液在线监测系统由上位机和下位机组成,采用以太网技术实现上、下位机的通信.上位机实现对被监测润滑油参数的实时显示、趋势分析、数据存储、数据导出、故障报警及诊断等;下位机集磨损、黏度等多种传感器采用PC104实现被监测润滑油数据的采集与处理,并利用嵌入式处理器中的异步通信接口(串口技术)与PC104进行数据交换,从而实现该技术在设备油液在线监测系统中的应用.     

油液状态监测技术在液压设备管理中的应用

该文介绍了油液状态监测技术,阐明了对液压设备进行油液状态监测的分析方法,明确了应用油液状态监测技术对提高液压设备管理效能的作用,对相关单位开展油液状态监测工作有借鉴意义。     

在线监测和离线检测在油液实际应用中的互补性研究

近年来，随着计算机技术、人工智能、传感器技术的飞速发展，给工业领域带来了翻天覆地的变化。油液检测领域也同样如此，随着工业技术的发展，也为油液检测提供了许多新的解决方案。比如，通过在轴承中安装传感器来实时监测润滑油的温度和黏度等参数；通过对齿轮箱振动数据进行采集并进行处理分析来预测设备未来的故障；通过对发动机燃烧过程中燃烧温度的监测来实现对发动机内部燃烧情况进行评估。在线监测和离线检测都是工业领域中应用广泛的技术，两者结合使用能够弥补各自的缺陷。本文介绍了在油液检测中使用在线监测和离线检测2种方法来解决不同情况下的油液检测问题，并且对2种方法进行了对比分析。在线监测与离线检测的结合使用可以实现更有效、更经济的监测和管理设备，对于设备的安全运行、延长设备的使用寿命有着重要的意义。     

油液综合监测数据管理及分析系统

针对全寿命周期设备的视情维护目标,建立了基于在线监测和离线分析的二级监测数据管理以及分析系统.该系统包含在线监测数据管理模块和离线分析数据管理模块.在线监测数据管理模块通过网络将在线监测数据存储在本地数据库,实现了在线数据的自动化管理,采用GM(1,1)等维新息预测模型实现了对在线油液指标数据发展趋势的实时预测,提取在线特征指标建立了支持向量机状态识别模型,实现了磨损状态的异常判断并给出离线分析建议.离线数据管理模块实现油液离线分析数据(理化、光谱、红外、分析铁谱、直读铁谱)的录人和维护,通过参考油液标准库评判油品性能的好坏,最终结合在线监测和离线分析结果做出视情维护决策的建议.     

A New On-Line Visual Ferrograph

An on-line visual ferrograph (OLVF) characterized by direct reading and on-line analysis was developed based on magnetic deposition and image analysis. A digital sensor was integrated with a CMOS image sensor to obtain images of deposited wear debris under illumination conditions. An electromagnetic instrument was designed to deposit the wear debris flowing through an oil flow channel. The oil flow channel, fixed on the electromagnet, was arranged parallel to the magnetic flux in the air gap between two electromagnet poles. The deposition effect on wear debris was analyzed theoretically. The result shows that the wear debris in different sizes can be deposited in the same zone by controlling the oil flow rate and magnet field intensity. Corresponding application software for image sampling and processing was developed. An index of relative wear debris concentration, IPCA (Index of Particle Coverage Area), is given as an output in addition to wear debris images. Finally, two kinds of experiments were specified to assess the effect and validity of the OLVF. The results show that the OLVF has effective deposition and identification for both relatively large and small wear debris with rational control parameters. The validity examinations with the commercial particle quantifier (PQ) and direct reading ferrograph (DR) show that the OLVF has an approaching trend to the reference instruments in both heavily and lightly contaminated oil.     

全视场在线图像可视铁谱磨粒显微成像特性分析

为了评估全视场在线图像可视铁谱磨粒显微成像特性,提出了一种反射光显微成像模型。首先,基于朗伯余弦与小角度散射理论建立了全视场(OLVF)的反射光辐照度模型,实现了磨粒显微成像清晰度定量评价。然后,仿真计算磨粒显微成像的反差透视比,确定了最优光学倍率和适用于全视场OLVF探测的油液衰减系数范围,明确了光学参数对磨粒显微成像质量的影响规律。结果显示：光学倍率为2.0×且油液衰减系数≤2.0条件下,磨粒沉积于物方视场的主光轴附近,全视场OLVF可获得最佳磨粒显微成像清晰度。最后,开展了磨粒显微成像实验测试,结果表明：全视场OLVF能够从油液衰减系数小于2.0的在用液压油和齿轮油中获取反射光谱片图像,并提取磨粒视觉信息进行磨损在线监测。     

Enhancement of oil debris sensor capability by reliable debris signature extraction via wavelet domain target and interference signal tracking

On-line oil debris monitoring is an effective approach to detecting machine component wear through estimating the size and the quantity of metallic debris in the lubricating oil. However, oil debris (particle) signatures are often contaminated by background noise and vibration interference during the operation of the oil debris sensor. As such, the accuracy of debris measurement and counting depends largely on the authenticity of the extracted debris signature. Considering characteristics of both target and interference signals obtained by the oil debris sensor, we propose a novel debris signature extraction technique to improve the oil debris measurement capability based on the wavelet domain information. In each wavelet scale of the oil debris sensor output signal, the debris coefficients are detected based on the singularity of the debris signal. The interference coefficients are estimated by adaptive linear prediction. The overlapped debris and interference coefficients are separated by a new prediction strategy involving alternating applications of forward and backward predictors. The differences between the mixture and the estimated interference coefficients are employed to reconstruct the debris signature. The proposed technique is evaluated using both uni- and bi-excitation experimental data and compared with a recently reported method. The experimental results and comparisons indicate that the proposed new method can extract the debris signature more truthfully, and thus improve the oil debris monitoring accuracy in real applications.     

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.     

铁谱技术中非铁磁性磨损颗粒的监测研究

在使用铁谱技术进行机械工况监测过程中，由于铁谱仪的磁场很难收集到摩擦副产生的非铁磁性磨损颗粒，往往会导致监测结果产生偏差或不正确。以钢一铜试件对磨后获得的油样为例进行铁谱对比实验，研究了磁流体在非铁磁性磨粒监测方面的应用。结果表明，在磁流体的作用下，油液中的非铁磁性磨粒将被磁化而有效沉积到铁谱片上，从而使油液的监测分析结果更为全面和准确。此外，介绍了磁流体的组成，分析了磁流体使非铁磁性磨粒磁化的机理。     

Condition monitoring of aircraft by quantitative filter debris analysis

Various condition monitoring techniques are used collectively to monitor the health of aircraft engines and transmission, a concept known as Integrated Health Monitoring (IHM). A well-established quantitative technique is Aircraft Oil Analysis (AOA), in which spectroscopic techniques such as Rotating Disk Electrode Atomic Emission Spectroscopy (RDE-AES) are employed to analyse periodic oil samples for wear debris. Usually, no sample preparation is undertaken, as the oil sample containing both dissolved and suspended metallic wear debris is analysed directly. AOA works well for oil-lubricated systems with relatively coarse filtration that allow circulation of the debris and its subsequent abrasive contact with moving components. To avoid this secondary wear, finer filtration is employed on both new and older aircraft. Less wear debris, and thus information, is available in the oil. A technique that quantitatively analyses the wear debris caught on the filter has been developed and is termed Quantitative Filter Debris Analysis (QFDA). Actual oil filters from CF88 Challenger ALF 502L-2C and Hornet F404 engines were obtained in sequence, when possible, prepared by the developed procedure, and analysed with AOA instrumentation. With sufficient results, both normal and abnormal levels of wear rates emerged, as has been recorded and used for AOA. Moreover, trending of the data for sequential samples has demonstrated the capability of GFDA for condition monitoring.     

Quantitative estimation of wear amounts by real time measurement of wear debris in lubricating oil

A diagnostic technique that can estimate quantitatively wear amounts under lubricated condition was developed using our developed on-line particle counter. Wear tests were carried out by rubbing a bearing metal against a carbon steel in paraffin oil. The size and number of wear debris in the circulating oil could be measured in real time. The volume of each debris was calculated and, additionally, the total wear amount during a given duration was calculated by accumulating all debris volume. The wear amounts obtained by the quantitative estimation were fairly similar to the measured values of mass loss of the specimen.     

Application of magnetization measurements to wear debris analysis

Spectrometric oil analysis for total wear metal amounts is often used to monitor lubricated machinery. In many cases iron is the dominant wear metal, and spectrometric analysis gives good results for total iron. However, iron may be present in several forms such as metal particles, oxides, carbides, sulfates and dissolved ions. A breakdown of total iron into the amounts and forms of iron present should give further valuable insight into wear phenomena taking place inside the machine.In this paper the use of magnetization measurements on samples of used oils and oil filters to obtain information on the forms of iron present in wear debris from lubricated machines is introduced. The measurement technique is described and the magnetic properties of some iron species that might be expected in wear debris are reviewed. This technique provides information on both ferromagnetic and paramagnetic species through the observation of various magnetic properties. Several illustrative examples are included. The ability of this technique to detect the paramagnetic as well as the ferromagnetic components is important because it is often found that the majority of the iron in wear debris is paramagnetic. A correspondence between the measured ferromagnetic component and ferrography is also shown for some cases.     

基于油样分析技术的钻井泵寿命预测

通过跟踪3台不同运行时间钻井泵的油样的铁谱和光谱分析数据,发现随着使用时间的增多,设备磨损加大,油样中Al、Cu、Fe等金属元素含量明显增加,在泵接近损坏时,铁谱显示油液中存在严重滑动磨粒和球形磨粒,磨粒链加密并伴有疲劳剥离磨屑;光谱显示铁质含量陡增。分析了磨损产生的机制并提出了减少磨损的方法,提出了将金属磨屑增长的速率作为状态考察的重要判断指标。分析结果表明,通过油样检测分析可预报泵动力端轴承等摩擦副的运行状态,为泵的大修时间的确定以及提高泵的工作寿命提供了科学的依据。     

Decision of lubricated friction conditions for materials of automobile transmission gear using neural network

It is hard to inspect the state of lubrication of an automobile transmission visually. Thus, it is necessary to develop a new inspection method. Wear debris can be collected from the lubricants of an operating transmission of an automobile, and its morphology will be directly related to the friction condition of the interacting materials from which the wear debris originated in the lubricated transmission. In this study, wear debris in lubricating oil are extracted by membrane filter (0.45 μm), and the quantitative values of shape parameters of wear debris are calculated by digital image processing. These shape parameters are studied and identified by an artificial neural network algorithm. The results of the study may be applicable to the prediction and diagnosis of the operating condition of transmission gear.