• CN:11-2187/TH
  • ISSN:0577-6686

›› 2008, Vol. 44 ›› Issue (9): 83-87.

• 论文 • 上一篇    下一篇

图像可视在线铁谱传感器的图像数字化处理技术

武通海;邱辉鹏;吴教义;毛军红;谢友柏   

  1. 西安交通大学润滑理论及轴承研究所;上海交通大学机械与动力工程学院
  • 发布日期:2008-09-15

Image Digital Processing Technology for Visual On-line Ferrograph Sensor

WU Tonghai;QIU Huipeng; WU Jiaoyi; MAO Junhong; XIE Youbai   

  1. Theory of Lubrication and Bearing Institute, Xi’an Jiaotong University School of Mechanical Engineering, Shanghai Jiaotong University
  • Published:2008-09-15

摘要: 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。

关键词: 磨粒, 图像处理, 在线铁谱

Abstract: Aiming at the wear debris automatic identifying for visual on-line ferrograph sensor, digital image capturing system is built and the ferrograph image processing methods are investigated. The image preprocessing is firstly investigated. Original image graying processing is performed with the 2 color models of YUV and RGB, and image sharpening with the differential moulds is followed. Furthermore, the wear debris segmentation of the preprocessed images is investigated with automatic threshold method and background subtracting method. Finally, a statistics description suitable for on-line ferrograph image is put forward. As main results, the luminance component of the YUV color model shows better effects in image graying processing over the RGB color model. The differential method with suitable model performs well in debris boundary sharpening without loss of image smoothness. The background subtracting method shows inconvenience in wear debris segmentation processing because of its artificial threshold selection, while the automatic threshold method performs well with the self-adapting of the threshold value. The index of particle coverage area performs well as an index of statistical wear debris content in the well segmented ferrograph images.

Key words: Image processing, On-line ferrograph, Wear debris

中图分类号: