Automated classification of wear particles based on their surface texture and shape features
Automated classification of wear particles based on their surface texture and shape features
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DOI：
10.1016/j.triboint.2007.04.004
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发表时间：
2008
影响因子：
6.2
通讯作者：
G. Stachowiak;G. Stachowiak;P. Podsiadło
中科院分区：
文献类型：
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作者：
G. Stachowiak;G. Stachowiak;P. Podsiadło
在本研究中，使用作者先前开发的自动分类系统对磨损颗粒进行分类。将磨损颗粒分为疲劳、磨料和黏合剂三种。在FZG背靠背齿轮试验台上产生疲劳磨损颗粒。用针盘式摩擦计测量磨粒和粘着磨损颗粒。获得磨损颗粒的扫描电镜（SEM）图像，形成进一步分析的数据库。将颗粒图像分为三组或三类，每一类代表不同的磨损机制。首先对每个粒子类进行视觉检查。其次，利用图像分析软件确定每一类的面积、周长、凸度和伸长率参数，并对参数进行统计分析。然后使用基于颗粒表面纹理的自动分类系统对每个颗粒类别进行评估。将自动颗粒分类的结果与颗粒形态的视觉评价和数值参数值进行了比较。结果表明，基于纹理的分类系统比基于磨损颗粒大小和形状的分类系统更有效、准确地区分了各种磨损颗粒。基于纹理的分类方法在机器状态监测领域具有很大的应用潜力。
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.