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.     

Watershed-Based Morphological Separation of Wear Debris Chains for On-Line Ferrograph Analysis

Separation and characterization of wear debris from ferrograph images are demanded for on-line analysis. However, particle overlapping issue associated with wear debris chains has markedly limited this technique due to the difficulty in effectively segmenting individual particles from the chains. To solve this bottleneck problem, studies were conducted in this paper to establish a practical method for wear debris separation for on-line analysis. Two conventional watershed approaches were attempted. Accordingly, distance-based transformation had a problem with oversegmentation, which led to overcounting of wear debris. Another method, by integrating the ultimate corrosion and condition expansion (UCCE), introduced boundary-offset errors that unavoidably affected the boundary identification between particles, while varying the corrosion scales and adopting a low-pass filtering method improved the UCCE with satisfactory results. Finally, together with a termination criterion, an automatic identification process was applied with real on-line wear debris images sampled from a mineral scraper gearbox. With the satisfactory separation result, several parameters for characterization were extracted and some statistics were constructed to obtain an overall evaluation of existing particles. The proposed method shows a promising prospect in on-line wear monitoring with deep insight into wear mechanism.     

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。     

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.     

A method for the quantitative recovery of polyethylene wear debris from the simulated service of total joint prostheses

The quantitative recovery of polyethylene wear debris is particularly important in the evaluation of the wear of total joint prostheses as it is the only feasible experimental wear parameter and also because of the clinical significance of the rate of release of the debris into the surrounding tissues. A method has been developed to recover the debris from the synovial fluid or serum in the joint; it consists in essence of chemical digestion, filtration and stereological evaluation of the debris volume. This method separates the polyethylene debris from all the other suspended solids present in the joint fluid and can be reliably and reproducibly calibrated. It has revealed that gross differences exist in the wear rates of commercially available total hip prostheses.     

大型汽轮发电机组轴承激光动态标高在线检测

介绍一种使用激光测量大型汽轮发电机组轴承动态标高的在线检测装置，叙述它的特点、测量原理和工作方法。检测装置已在２００ＭＷ机组上进行多次现场实机测试，获得机组轴承标高变化的重要数据。由启停机测试数据判断，装置运行稳定，数据可靠。     

On-line aircraft parameter identification using fourier transform regression with an application to NASA F/A-18 harv flight data

This paper applies a recently developed on-line parameter identification (PID) technique to sets of real flight data and compares the results with those of a state-of-the-art off-line PID technique. The on-line PID technique takes Linear Regression from Fourier Transformed equations and the off-line PID is based on the traditional Maximum Likelihood method. Sets of flight data from the NASA F/A-18 High Alpha Research Vehicle (HARV) aircraft, which has been recorded from specifically designed maneuvers and used for off line parameter estimation, are used for this study. The emphasis is given on the accuracy and on-line measure of reliability of the estimates. The comparison is performed for both longitudinal and lateral-directional dynamics for maneuvers at angles of attack ranging α=20δ through α=40δ. Results of the two estimation processes are also compared with baseline wind tunnel estimates whenever possible.     

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.     

Advances in research on a multi-channel on-line ferrograph

This paper introduces the basic principle, functions and test results of a multi-channel on-line ferrograph. The instrument catches wear debris with an electromagnet, detects wear debris with a photoelectric sensor, and controls sampling and data processing with an 8098 single-chip microprocessor which can communicate with a master computer. The instrument has four sampling channels, which can monitor not only one machine but also four machines one by one. The software of the instrument includes five modules which are a main program, a keyboard control program, a floating point operation program, a serial communication program and a self-checking program. The results of experiments on a gear box show that increasing average values detected by the instrument correspond to increasing load, so the instrument can meet the need for on-line monitoring of the wear condition of machines.     

在线铁谱仪性能测试分析

本文介绍了适用于监测煤矿机械设备循环润滑油中磨屑量连续读数的在线铁谱仪的工作原理,测试分析了在线铁谱仪的线性、重复性、沉积率等性能指标。     

图像可视在线铁谱传感器的图像数字化处理技术

为实现图像可视在线铁谱传感器的磨粒图像自动辨识,建立数字图像获取系统,探讨铁谱图像数字化处理方法。研究了铁谱图像的预处理方法,对比在RGB和YUV颜色空间对铁谱图像的灰度化处理效果,采用不同的微分模板对平滑后图像进行锐化处理;探讨减背景法和自动阈值法在铁谱图像磨粒分割中的应用效果;给出适用于在线铁谱图像的定量描述方法。研究表明,采用YUV颜色空间的明视度分量可以得到平滑的灰度图像,合理的模板选择可以使微分法在锐化磨粒边缘的同时保持整体图像的平滑;铁谱图像的磨粒分割结果表明,减背景法由于采用人工选取门限值而难以适用于在线铁谱图像的处理,而自动阈值法可以根据铁谱图像自动选取合适的阈值以达到良好的分割效果;采用磨粒百分覆盖面积作为定量指标可反应良好分割的铁谱图像中的磨粒统计质量分数。     

电感式磨粒传感器中铁磁质磨粒特性仿真研究

针对机械装置的在线监测传感器,模拟了铁磁质磨粒通过传感器过程中传感器线圈的磁场和感应线圈的感应电压瞬态变化特性.考虑了线圈与铁磨粒的材料、线圈匝数和激励线圈的输入电压等因素,应用Jmag Designer I0.4软件建立了传感器的二维有限元模型.仿真结果揭示了磨粒运动过程中线圈磁场与感应线圈中感应电压的变化规律,获得了感应电压与球形磨粒的直径大小的立方成正比,与磨粒运行速度成正比.研究结果对于电感式磨粒传感器的开发具有重要的指导价值.     

零件表面粗糙度的在线检测方法研究

根据零件表面粗糙度形成的复杂性，建立了基于具有结构和参数学习的模糊神经网络逻辑系统，通过该模糊神经网络搜索最优推理规则，并且通过最优模糊推量规则来在线检测零件表面粗糙度。该检测方法可在线检测零件的附加表面粗糙度。     

冲击脉冲法评价滚动轴承故障的系数自修正方法

对机械工业中常用的评价滚动轴承故障的冲击脉冲法,提出了一种基于神经网络的故障评价系数自修正策略。这一修正系数模块已用于工业现场滚动轴承状态在线监测系统中,经长期运行的实践证明,自修正系数模块的引入极大地提高了滚动轴承运行状态判断的准确性。     

基于平面线圈的磨粒监测传感器

为了提高油液的在线监测水平,研制了一种基于电感测量的铁磁性颗粒传感器,用MEMS工艺制作了一种平面线圈型的传感器并分析了其工作原理.设计了信号处理电路,对传感器的性能进行了实验研究.结果表明:颗粒含量在10～ 100 mg时,传感器具有良好的线性度,灵敏度为7.145 5 V/g,该传感器可以分辨5 mg的铁屑含量,输出稳定,为油液的在线监测提供了一种新的检测元件.     

Experimental on-line identification of an electromechanical system

Identification of electromechanical systems operating in open-loop or closed-loop conditions has long been of prime interest in industrial applications. This paper presents experimental on-line identification of an electromechanical system represented by a digital input/output model. The paper also bridges the theory and practice gap for applied researchers. Studies are carried out by formulating the mathematical model using differential equations and experimental discrete-time identification using on-line plant input-output data. A recursive least-squares method is used to estimate the unknown parameters of the system. Discrete-time data for the parameter identification are obtained experimentally from a setup constructed in the laboratory. A root-mean-square error criterion is used for model validation. Results are presented which show variations in parameters of the electromechanical system. It is demonstrated that identified model output and actual system output match. All tests are performed with no previous results from finite element simulations.     

大型汽轮发电机组轴承负荷在线监测方法研究

提出大型汽轮发电机组轴承负荷在线监测的意义；对比分析目前大型汽轮发电机组轴承负荷的各种测试方法；指出现11轴承支承力法，即通过安装在轴承与轴承座间的测力传感器直接测量轴承负荷的方法，是对大型汽轮发电机组轴承负荷在线监测的一种较好方法；并提出测轴承支承力法的关键技术，即机组轴承负荷传感器的结构适应性、功能适应性、长期稳定性和可靠性要求；研究测轴承支承力法对大型汽轮发电机组轴承负荷在线监测的可行性。研究结果表明，测轴承支承办法是必要和可行的。     

基于虚拟仪器的滚动轴承滚动体疲劳试验机监控系统

轴承滚动体疲劳失效在线监控系统。除集成了传统测量仪器的所有功能外,还进行了功能扩展,提供了友好的人机界面、可在线显示轴承试验的工作参数并可自动控制试验机的运行与停止。该系统信号分析的实际结果显示其具有良好应用前景。     

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.