Condition monitoring of steam turbine-generator through contamination analysis of used lubricating oil

The objective of this paper is to present a systematic analysis of wear particles contained in used lubricant of steam turbine-generator of a thermal power station. The turbogenerator was condition-monitored over a period of two years through wear debris and particulate contamination analysis of the oil. Various sophisticated techniques such as automatic particle counter, ferrography, inductively coupled plasma atomic emission spectrometry (ICPAES), scanning electron microscopy and energy dispersive X-ray analysis (SEM/EDAX) have been employed to extract the relevant information about the health of the machine. Eventually, a correlation of different techniques of wear debris monitoring on the basis of current investigation ascertains the significance of the collective approach of various techniques to avoid catastrophic breakdowns and expensive component replacements.     

Condition monitoring techniques for an internal combustion engine

Various condition monitoring techniques were applied during a laboratory engine test in order to understand the wear processes occurring and to determine a suitable method which could be applicable to the detection and diagnosis of abnormal engine conditions in practice. Fuel consumption rates were measured in conjunction with chemical analysis of the used oil by Fourier Transform Infrared Spectroscopy, and the various contaminant inspection methods. For the contaminant inspection of the wear debris in the lubricating oil, the quantitative and qualitative techniques of the Rotary Particle Depositor (RPD), Particle Quantifier (PQ), Spectrometric Oil Analysis, and Image Analyser Systems were applied and the results compared. The effectiveness of each to be able to respond to wear failure was also compared. Evaluation showed that, of the methods examined, the combination of RPD and PQ was the most suitable technique for the detection of abnormal wear occurring in the engine combustion zone. It is, therefore, suggested that this combination can be directly applied to internal combustion engines as an effective condition monitoring method.     

盾构机主轴承油液监测数据分析与阈值确定

油液监测是盾构机主轴承状态监测与故障诊断的重要手段之一,然而其现行诊断阈值多为经验数据,且对监测所得的大量数据缺乏系统性分析。为制定盾构机主轴承油液监测故障诊断的合理阈值,设计盾构机主轴承油液监测方案,提出油液监测的取样方法、取样周期及特殊情况下的取样周期,并获取大量油液监测数据;采用统计方法分析得出监测项目的基准值、警告值、危险值,与现行通过经验值制定的监测阈值指标吻合,从理论证明了现行监测阈值的合理性;采用线性回归法对掘进里程与监测数据进行回归分析,探寻各监测数据与掘进里程的关系,更合理地确定了主轴承在全生命周期里各阶段的监测阈值,与统计法计算出的阈值互为补充;通过相关性分析,得出油液监测中各项数据变化的内在联系,指出油液中水分含量为状态监测中首要控制因素。     

油液污染度分析在船用舵桨装置状态监测中的应用

应用污染度检测仪对2条港口作业拖船的舵浆装置润滑油进行了污染度检测，通过对所测污染度数据的分析并辅以油液光谱分析和铁谱分析，对船用舵桨装置运动副的磨损状态及发展趋势进行了评价和预测。指出油液污染度分析不仅可对机械设备的磨损状态进行监测、评价和趋势预报，而且简单方便可行，是设备状态监测与故障诊断的一种重要技术手段。     

光纤油液污染监测传感器设计

基于Beer-Lambert定律，悬浮在油液中的污染颗粒对入射光有吸收和散射作用，因此可根据发光强度的变化来反映油液污染度的变化情况。设计了光纤传感器探头，分析了其关键技术。为了实现在线实时检测。提出了正交光路的检测方法，初步的实验数据表明该传感器可反映油液的污染度水平，尽管目前仍旧存在一些问题，但光纤检测技术以其具有的优点将会成为最具生命力和应用前景的新的检测方法。     

The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery

The monitoring of progressive wear in gears using various non-destructive technologies as well as the use of advanced signal processing techniques upon the acquired recordings to the direction of more effective diagnostic schemes, is the scope of the present work. For this reason multi-hour tests were performed in healthy gears in a single-stage lab scale gearbox until they were seriously damaged. Three on-line monitoring techniques are implemented in the tests. Vibration and acoustic emission recordings in combination with data coming from oil debris monitoring (ODM) of the lubricating oil are utilized in order to assess the condition of the gears. A plethora of parameters/features were extracted from the acquired waveforms via conventional (in time and frequency domain) and non-conventional (wavelet-based) signal processing techniques. Data fusion was accomplished in the level of integration of the most representative among the extracted features from all three measurement technologies in a single data matrix. Principal component analysis (PCA) was utilized to reduce the dimensionality of the data matrix whereas independent component analysis (ICA) was further applied to identify the independent components among the data and correlate them to different damage modes of the gearbox. Finally heuristic rules based on characteristic values of the resulted independent components were set, realizing thus a health monitoring scheme for gearboxes.The integration of vibration, AE and ODM data increases the diagnostic capacity and reliability of the condition monitoring scheme concluding to very interesting results. The present work summarizes the joint efforts of two research groups towards a more reliable condition monitoring of rotating machinery and gearboxes specifically.     

Monitoring debris in turbine generator oil

Debris from the lubricating systems of turbine generators has been examined with the aim of on-line monitoring of bearings and shaft seals.Chemical and X-ray analyses are among the techniques used to characterize the debris in “healthy” machines and hence to identify departures from the norm. An empirical condition parameter, the ratio of tin to iron in the debris, has been found useful in considering widely different generators and operational conditions. Using this parameter several instances of bearing damage have been suspected and subsequently confirmed by inspection.Possible methods for on-line debris analysis are discussed, including a continuous filtration technique which is the basis of our current work.     

基于电化学阻抗法的油液在线监测传感器应用特性研究

大型机械装备润滑系统油况的在线监测是润滑故障诊断的关键技术之一,难点在于准确监测水分异常和润滑油老化失效临界点。根据油液老化过程缓慢且复杂、各种因素导致润滑油阻抗值变化的特性,研究基于电化学阻抗谱技术的油况传感器;通过连续在线监测不同润滑油状态下传感器的体相阻抗、界面阻抗和高频阻抗的变化趋势,发现体相阻抗、界面阻抗和高频阻抗的数据趋势可以用来判断油的状况,具体表现为润滑油状态影响传感器体相阻抗数值和界面阻抗数值,添加剂状态影响界面阻抗数值;油液使用时间越长,老化程度越高,体相阻抗值越高;污染物和分解产物影响高频阻抗数值。介绍油液状态传感器的实现原理,通过实验和工程论证油液状态传感器监测水分和润滑油老化失效临界点的可行性。结果表明,在特定应用场景下,电化学阻抗谱油液传感器能区分不同油液状态,能准确监测润滑系统水分异常和老化失效临界点,为设备润滑安全运维策略提供依据。     

油液分析技术在压缩机故障诊断中的应用

为提高设备利用率和预防意外事故的发生,根据大连石化四酮苯车间机组情况制定压缩机润滑油监测方案,采用铁谱分析仪监测油液中的磨粒状况,进而预测和诊断压缩机工作状态。实践证明该方法可有效提高机组总体运行可靠性。     

油液在线监测系统中磨粒识别技术研究

针对磨损状态监测要求,构建了基于显微图像分析的油液在线监测系统。根据系统光路特点,对磨粒图像进行了基于彩色特征的转换,并通过与背景图像的差值处理来快速提取磨粒目标。基于最小二乘支持向量机设计了磨粒两类分类器,并利用粒子群优化算法对最小二乘支持向量机模型中的参数进行了优化选取;根据磨粒识别体系,设计了基于最小二乘支持向量机的磨粒综合分类器。最后,利用铁谱分析技术对系统性能和识别效果进行了检验,结果表明本系统具有较高的检测精度和识别效果。     

Wear Condition Monitoring and Working Pattern Recognition of Piston Rings and Cylinder Liners Using On-Line Visual Ferrograph

This study applies stepped loading and stepped velocity approaches to simulate the actual working conditions of gasoline engines. Accelerated wear tests were conducted for piston rings and cylinder liners under different lubricating conditions by using a self-made pin-on-disc wear machine equipped with an on-line visual ferrograph (OLVF) for wear monitoring. The wear coefficients for oil monitoring were extracted to distinguish between constant conditions and stepped changing conditions. A similarity model for oil monitoring was constructed and the monitoring data sets of similar working conditions were grouped together. Results show that the OLVF monitoring system can be used to obtain the real-time variation in debris concentration. The index of particle coverage area (IPCA) of OLVF increases abruptly after the load or speed changes. The similarity model can evaluate the similarity of the variation trend of IPCA under different operating conditions. The relationship between IPCA and working conditions was examined in this study and provides an essential support to wear monitoring and life prediction of engines.     

油液在线监测系统中的磨粒识别

针对机械设备磨损状态监测要求,构建了基于显微图像分析的油液在线监测系统.根据系统的光路特点,对磨粒图像进行了基于彩色特征的转换,并通过与背景图像的差值处理来快速提取磨粒目标.基于最小二乘支持向量机设计了两类磨粒分类器,并利用粒子群优化算法对最小二乘支持向量机模型中的参数进行了优化选取.在此基础上,根据磨粒识别体系,设计了磨粒综合分类器.最后,利用铁谱分析技术对系统性能和识别效果进行了检验,结果表明,系统的识别精度达到95%以上,满足磨粒在线监测要求.     

Effect of volatile organic contamination on head-disk interface tribology and a method for its reduction

Volatile organic contamination is known to be one of the factors to cause the failure of head-disk interface (HDI). Therefore, reduction of its harmful effects and improvement of the stability and reliability of HDI is becoming an important issue. In this study, the effects of some model compounds of volatile organic contamination on the tribological characteristics of HDI were systematically investigated using a contact start/stop (CSS) tester. The slider surface after the CSS tests was analyzed using Time-of-Flight Secondary Ion Mass Spectroscopy (TOF-SIMS). Transfer of lubricating oil onto the slider surface was detected after the CSS tests. The organic contaminants promoted the transfer and resulted in high and unstable friction force. Fluorinated self-assembled monolayers (SAMs) were applied on the slider surface for reducing the transferred amount of the lubricating oil. Tribological performance of the slider coated with the SAMs and the transfer amount of lubricating oil onto the slider surface in the presence of contaminant was investigated. The friction force was low and stable in the case of the SAMs coated slider even under environmental contaminant. This result could be explained by the reduction of the transferred lubricating oil because the SAMs that coated on the slider surface were low surface energy.     

液压油污染实验与在线监测模型

液压油污染物主要包括水和固体颗粒等。介绍了水污染液压油衰变模型(S-B模型)和固体颗粒污染液压油衰变模型(G-S)模型,以这两个模型为基础,建立了衰变液压油含水率在线监测模型和固体颗粒质量分数在线监测模型,并对其进行了实验验证。结果表明:两个模型能较好地实现衰变液压油含水率和铁粉质量分数的在线监测,有利于降低煤矿维修成本和保证液压系统正常运转。     

淤积原理在液压系统在线污染监测中的应用

采用淤积原理的在线污染监测方法，通过检测流过滤膜某一固定体积时的压差来测量液压系统的油液污染度。研制出在线监测装置，采用电磁铁推动活塞吸油和获得压降，步进电机带动自制金属滤膜旋转实现过滤与反向冲洗功能，利用虚拟仪器软件LabVIEW设计监控平台，并对在线污染监测方法进行了理论分析与实验研究。实验结果表明，在线污染监测方法能给出ISO、NAS与SAE油液污染等级，可满足生产现场的在线监测要求。     

Integral indicator for monitoring the condition of lubricating materials in tribocouples

A miniature integral detector is presented that enables in-line monitoring of the lubricating material in trybosystems by measuring and evaluating four diagnostic parameters simultaneously: chemical disintegration, total contamination content in the oil, water content, and temperature. The detector comprises a self-test system, self-tuning controlled by a microcontroller. The diagnostic parameters of the detector and the standard parameters, such as viscosity and general acid number of mineral and synthetic oils, are compared for their analysis. The validity of the detector’s output data is confirmed, which assures its broad application for in-line assessment of the performance of the oil and condition of the tribosystem in general given the high information content and the level of computerization.     

基于振动监测和支持向量机的海洋石油离心泵智能预警技术研究

目前海洋石油正在建设智能油田、数字油田,提升设备管理的数字化和智能化水平。该文针对如外输泵、注水泵等重要性较强的A类关键离心泵,分析常见故障并采用基于振动监测技术实现离心泵在线监测系统,保障了状态数据的实时采集。梳理常见故障的特征情况,建立基于支持向量机优化概率密度函数的智能预警模型,通过数据测试抓取特征参数,嵌入在线监测系统。通过现场应用,可实现对海洋石油离心泵的有效智能预警,提升了海洋石油的关键设备的数字化监测管理水平。     

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.     

船舶柴油机状态监测及预测性维护研究及应用

从热工、油液、振动、缸压4个维度开展数据采集和特征提取工作,提出了OCSVM异常检测算法联合Fisher判别分析进行柴油机状态监测的一种方法,采用累计和/参数均值比进行参数变点检测,采用D-S证据理论及证据加权分配合成方法进行多源信息融合,并利用LSTM对柴油机滑油的剩余使用寿命进行预测,解决了传统柴油机状态监测方法早期预警率低、动态工况下模型适应性差等问题。