磨粒形貌三维图像分析技术的发展趋势

磨损规律和机械故障诊断等研究都离不开磨粒分析技术。影响三维磨粒分析技术的两个重要因素是精确的磨粒表面形貌数据的获取和三给表面 纹理的数字描述，本文就是从这两方面着重介绍了当前国际上流行的磨粒形貌三维分析技术。描述了磨损颗粒的各种三维数据获取技术以及它们存在的优缺点，并对三维表面数字特征的提取方法进行了较为详细的综述，最后得到有关三维磨粒分析技术的四个结论。     

Scale-invariant analysis of wear particle morphology—a preliminary study

The importance of wear particle characterization is continuously growing, as the need for prediction and monitoring of wear increases. Accurate analysis of wear particles can, however, be limited by problems associated with particle characterization, especially of the wear particles' surface morphology. Since the shape and surface topography of wear particles often exhibit a fractal nature, fractal (scale-invariant) methods are, therefore, used in their characterization. However, the methods used to date ignore the fact that all fractal objects can be described by a small set of mathematical rules; although finding those rules which describe a particular fractal image is a difficult problem. No general solution exists to date and this paper attempts to redress this problem. A new analysis method, ‘scale-invariant analysis’, which is based on a partitioned iterated function system (PIFS), is proposed for the characterization of wear particle morphology. PIFS is a collection of contractive affine transformations. Each affine transformation transforms one part of a wear particle image onto another part of the same image. PIFSs were constructed for both computer generated and SEM images of wear particles. Results obtained in this study clearly demonstrate that the morphology of wear particles can effectively be characterized using the PIFS method.     

Automated classification of wear particles based on their surface texture and shape features

In this study, the automated classification system, developed previously by the authors, was used to classify wear particles. Three kinds of wear particles, fatigue, abrasive and adhesive, were classified. The fatigue wear particles were generated using an FZG back-to-back gear test rig. A pin-on-disk tribometer was used to generate the abrasive and adhesive wear particles. Scanning electron microscope (SEM) images of wear particles were acquired, forming a database for further analysis. The particle images were divided into three groups or classes, each class representing a different wear mechanism. Each particle class was first examined visually. Next, area, perimeter, convexity and elongation parameters were determined for each class using image analysis software and the parameters were statistically analysed. Each particle class was then assessed using the automated classification system, based on particle surface texture. The results of the automated particle classification were compared to both the visual assessment of particle morphology and the numerical parameter values. The results showed that the texture-based classification system was a more efficient and accurate way of distinguishing between various wear particles than classification based on size and shape of wear particles. It seems that the texture-based classification method developed has great potential to become a very useful tool in the machine condition monitoring industry.     

3D Surface Characterizations of Wear Particles Generated from Lubricated Regular Concave Cylinder Liners

Wear particle analysis can be developed as an effective method for assessment of the running conditions of concaved cylinder liners. The aim of this study was to numerically characterize the topographical features of wear particles generated from different surface textured cylinder liners and to investigate their changes with alternations in both rotational speeds and surface textures. To achieve this goal, cylinder liners with three different surface textures were prepared and tested in four different speeds. In addition to an untreated surface, concave cylinder liner surfaces with two different diameters (1 and 2 mm) and two different depths (200 and 300 μm) were investigated. Wear particles were extracted from the lubrication oil; three-dimensional images of the wear debris were acquired using laser confocal microscopy; and their topographical features were analyzed quantitatively. This study has revealed that running-in conditions and stable state can be detected using wear debris analysis techniques at a micrometer scale. It has also been discovered that concave B cylinder liner with a depth–diameter ratio of 0.1 always generated wear particles different to those from the other two cylinder liners on each rotational speed. It is believed that the quantitative surface topography characterization results obtained in this study provide a practical base for developing a new, non-intrusive tool for monitoring the operation conditions of cylinder liner–piston rings in diesel engines.     

Shape and texture features in the automated classification of adhesive and abrasive wear particles

In this study the automated classification system, developed previously by the authors, was used to classify wear particles. Two kinds of wear particles, adhesive and abrasive, were classified. The wear particles were generated using a pin-on-disk tribometer. Various operating conditions of load, sliding time and abrasive grit size were applied to simulate adhesive and abrasive wear of different severity. SEM images of wear particles were acquired, forming a database for further analysis. The particle images were divided into eight groups or classes, each class representing different wear test conditions. All eight particle classes were first examined visually. Next, area, perimeter and elongation parameters were determined for each class and the parameters were statistically analysed. The automated classification system, based on particle surface texture, was then applied to all particle classes. The results of the automated particle classification were compared to those based on either the visual assessment of particle morphology or numerical parameter values. It was shown that the texture-based classification system was a more efficient and accurate way of distinguishing between various wear particles than classification based on size and shape of wear particles. It seems that the texture-based classification method developed has great potential to become a very useful tool in the machine condition monitoring industry.     

Applications of Hurst orientation transform to the characterization of surface anisotropy

Fractal-based methods have been used in surface characterization with increasing success over the past years. These methods have been employed to characterize isotropic surfaces but, as yet, little quantitative consideration has been given to the characterization of anisotropic surfaces. In this work, the Hurst orientation transform (HOT) is used to characterize the surface anisotropy and directionality. The calculation of the HOT involves searching all pairs of pixels in a circular region to build a table of maximum differences. From this table, the Hurst coefficients are calculated in many directions and plotted as a function of orientation to reveal surface anisotropy. In this work, two new surface texture parameters, i.e. texture aspect ratio and texture direction, obtained from rose plots of the Hurst coefficients were used in the characterization of surface anisotropy and directionality. Applications of the HOT method to stereo and interferometric images of wear particles and X-ray images of healthy and osteoarthritic joints are also demonstrated.     

Two-dimensional fast Fourier transform and power spectrum for wear particle analysis

Statistical parameters, such as R_a and R_q, have been widely used to investigate the roughness of wear particle surfaces in the literature. It has been reported that wear particle analysis based only on numerical characterization is often insufficient to distinguish certain types of wear debris. In this study, two-dimensional fast Fourier transform, power spectrum and angular spectrum analyses are applied to describe wear particle surface textures in three dimensions. Laminar, fatigue chunk and severe sliding wear particles, which have previously proven difficult to identify by statistical characterization, have been studied. The results show that spectral analysis effectively identifies the surface texture pattern (e.g. isotropy or anisotropy) and can be applied to classify these three types of wear particles.     

Development of advanced quantitative analysis methods for wear particle characterization and classification to aid tribological system diagnosis

Classification of wear particles is performed in two steps, i.e. first a particle to be classified is characterized by surface feature parameters such as roughness, directionality, homogeneity, periodicity, etc. and then, the particle is assigned to a specific class using these parameters. However, a significant limitation of this approach is that surface parameters are often not unique to a specific surface topography and their values may change significantly with scale, orientation angle and position at which the particle data was acquired. Various attempts were made to overcome this limitation by selecting a core set of parameters which ensures that wear particles are accurately classified. However, the parameter selection is usually cumbersome and requires lengthy computation. Furthermore, there is no guarantee that parameters selected are sensitive enough to separate particles belonging to different classes. Thus, a new classification technique based entirely on dissimilarity measures (e.g. Euclidean, Baddeley's distances), calculated between surface images of an unclassified particle and classified particles, was developed. The classification process is based on assigning a particle to a class of particles with the smallest dissimilarity measure. This idea arises naturally from the two facts: (i) compactness, similar objects are in close proximity to each other in their representation space, while different objects are far apart, and (ii) true representation, if objects are close to each other in their representation space they belong to the same class. In this paper, an overview of recent advances and developments in the area of particle classification based on dissimilarity measures is presented with a particular emphasis on constructing a simple and accurate classifier.     

磨粒三维表面形貌的研究分析

磨粒三维表面形貌分析是针对磨粒二维分析技术的不足而提出的磨粒分析方法。分析了目前磨粒三维表面形貌获取技术特点和实用性,探讨了近年来磨粒表面形貌三维表征描述中采用的几种方法——灰度共生矩阵、分形维数法、方向测度法,并通过分析表明,方向测度法是一个比较好的磨粒表面形貌三维特征分析方法。     

Automatic wear‐particle classification using neural networks

Although the study of wear debris can yield much information on the wear processes operating in machinery, the method has not been widely applied in industry. The main reason is that the technique is currently time consuming and costly due to the lack of automatic wear particle analysis and identification techniques. In this paper, six common types of metallic wear particles have been investigated by studying three‐dimensional images obtained from laser scanning confocal microscopy. Using selected numerical parameters, which can characterise boundary morphology and surface topology of the wear particles, two neural network systems, i.e., a fuzzy Kohonen neural network and a multi‐layer perceptron with backpropagation learning rule, have been trained to classify the wear particles. The study has shown that neural networks have the potential for dealing with classification tasks and can perform wear‐particle classification satisfactorily. This revised version was published online in June 2006 with corrections to the Cover Date.     

The development of the modified Hurst orientation transform for the characterization of surface topography of wear particles

A modified Hurst orientation transform (HOT) method for characterization of wear particle surfaces is proposed and described in this paper. The method involves the calculation of self-affine Hurst coefficients in all directions and displays the calculated coefficient values in a form of rose plot. The calculation of individual Hurst coefficients, H, is based on the rescale range (r/s) analysis (r(d)/s∼ d ^H ). The rose plot is then used to obtain three texture surface parameters, i.e.: texture aspect ratio, texture minor axis and texture direction. The effectiveness of this modified HOT and resulting surface texture parameters was evaluated. The method was first applied to computer-generated images of isotropic and anisotropic particle fractal surfaces and then to field emission scanning electron microscope images of wear particles found in synovial joints. The ability of the surface parameters to reveal surface isotropy or anisotropy, measure roughness and determine the dominant direction of surface texture was assessed. The effects of measurement conditions such as noise, gain variations and focusing on the surface parameters were also investigated. The results demonstrate that the HOT and surface texture parameters developed can successfully be used in the characterization of wear particle surface topography. This revised version was published online in August 2006 with corrections to the Cover Date.     

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.     

Hybrid Fractal-Wavelet Method for Characterization of Tribological Surfaces—A Preliminary Study

Many types of tribological surfaces have been observed to exhibit a multiscale nature, i.e. topographical features over different scales ranging from nanometers to hundreds of micrometers. Consequently, recent developments in the characterization of tribological surfaces have concentrated on wavelet transformation and fractal-based methods. The wavelet methods are used due to their ability to decompose surface data into different scale components and to characterize surface data at each individual scale. On the other hand, fractal methods are used due to their ability to characterize surface data in a scale-invariant manner. It has been shown that these two capabilities of (i) decomposition of surface data and (ii) scale-invariant analysis are essential in the characterization of 3-D surface topography. Thus, it is apparent that a characterization method which exhibits both of these capabilities would yield the best results. Developing such a method is not an easy task. This problem in the characterization of tribological surfaces is addressed in our paper. A new method, called a hybrid fractal-wavelet method, is proposed. This method is a combination of a partition iterated function system (PIFS) and a symmetric wavelet transform (SWT). The PIFS is used to scale-invariantly characterize the surface topography over a wide range of different scales, while the SWT is used to characterize the surface topography at each individual scale. This multiscale characterization ability of the newly developed method is demonstrated on a tribological surface example.     

采用去模糊图像处理的气/固两相流固体颗粒速度测量方法

在燃煤火力发电站的煤粉输送系统中,为了提高煤粉的燃烧效率,获得煤粉与空气混合物的参数就显得至关重要。本文提出一种基于图像处理技术的气/固两相流固体颗粒速度测量方法。通过控制CCD照相机的曝光时间获得颗粒的运动模糊图像,在此基础上利用图像处理方法估计出模糊图像的点扩展函数（PSF）,并利用透镜成像原理建立运动模糊长度与颗粒运动速度之间的联系,最终获得颗粒的运动速度。在此理论分析的基础上,本文利用该方法对颗粒自由落体形成的运动模糊图像进行了处理,仿真及实验结果表明该方法适用于气/固两相流颗粒速度的测量。     

铁谱磨粒图像多焦点融合算法研究

铁谱分析技术是一种常用的磨损监测技术。受限于高倍物镜下的景深限制,一张铁谱大磨粒图像往往只有局部聚焦清晰的特征。为了能够解决在高倍物镜下铁谱大磨粒图像的自动化清晰采集以及高质量图像融合问题,设计并构建一套自动化扫描显微系统,该系统可进行多焦点铁谱图像的自动扫描采集;同时,提出一种基于相位一致性的铁谱磨粒图像多焦点融合算法,对自动扫描的多焦点图像进行融合,得到清晰的磨粒图像。实验结果表明,设计的自动化扫描显微系统能快速完成多焦点铁谱图像的自动化采集流程,提出的图像融合算法相较于传统的小波图像融合算法具有更高的图像评价质量,并能获得更加清晰的图像边缘信息。     

Stereo pairs of bright-field micrographs via Wiener-type inverse filtering

In a three-dimensional (3-D) image data set obtained through optical sectioning, each two-dimensional (2-D) segment is blurred by out-of-focus information from neighbouring focal planes superimposed on the in-focus segments from that plane. Instead of attempting to remove this redundant information over the full 3-D data set, we have developed a technique for restoring stereoscopic views. In this paper we describe the implementation of a Wiener-type inverse filtering method for generating stereo pairs of bright-field micrographs. A theoretical optical transfer function valid under certain simplifying approximations has been used in implementing this filtering technique. In developing this method the slice theorem of computed tomography is used. In this way the image reconstruction problem is reduced to one of processing 2-D arrays rather than 3-D arrays and the problem of restoring missing Fourier components within the missing-cone region is circumvented. Limited experimentation with real micrographs shows that the approach provides images that display an effective increased depth of field and 3-D attributes of the specimen, even though some of the underlying assumptions on which this method is based are difficult to verify explicitly. The method can be implemented with a relatively fast execution time on 386-SX computers.