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基于反相灰度图二值化修正的铁谱图像磨粒特征提取
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国家绿色制造系统集成项目(工信部节函[2017]327号1);陕西省自然科学基础研究计划项目(2017JQ5105);国家自然科学基金重点项目(51834006).


Abrasive Particle Feature Extraction in Ferrography Based on Binary Correction of Inverse Grayscale Image
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    摘要:

    为准确获取铁谱图像中磨粒几何形状和特征参数,提出一种图像处理算法并对其进行验证。针对铁谱图像底色特征及磨粒特征提取精度要求,提出基于反相操作的铁谱图像灰度图转化方案,得到边缘清晰的铁谱灰度图;提出一种三段式阈值分割方案,利用腐蚀和膨胀操作解决二值化对铁谱图像有效磨粒区域的影响,讨论油污等干扰因素的消除策略;确定磨粒特征参数及磨粒识别方案,完成标准的正常滑动磨损图像处理和某实际的齿轮箱磨粒铁谱图像处理验证。结果表明:所提出的算法能够准确提取铁谱图像中磨粒所在区域的几何特征,通过磨粒标定计算得到了8个特征参数值,证明齿轮箱正处于滑动磨损状态。

    Abstract:

    In order to obtain the geometric shape and characteristic parameters of abrasive particles in ferrography images accurately,an image processing algorithm was proposed and verified.For the background color characteristics of ferrography images and in order to meet the image extraction precision requirements,a scheme for converting ferrography images into grayscale images based on inversephase operation was proposed,and a clear edge grayscale image was obtained.Then a threestage threshold segmentation scheme was proposed,and the corrosion and expansion operations were used to eliminate the effect of binarization on the effective area of abrasive particle in ferrography images.The characteristic parameters of abrasive particles and abrasive recognition scheme were determined,and the image processing for the standard normal sliding wear and an actual gear box testing was carried out to verify the proposed algorithm.The results show that the proposed algorithm can accurately extract the region of sabrasive particles in ferrography images,and eight characteristic parameters by calibration calculation are obtained,which verifies the gear box is in a state of sliding wear.

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樊红卫,丁骁,高烁琪,邵偲洁,杨一晴,马宏伟,张旭辉.基于反相灰度图二值化修正的铁谱图像磨粒特征提取[J].润滑与密封,2019,44(6):66-71.
. Abrasive Particle Feature Extraction in Ferrography Based on Binary Correction of Inverse Grayscale Image[J]. Lubrication Engineering,2019,44(6):66-71.

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  • 在线发布日期: 2020-03-12
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