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Study of Grey Relational Grade Identification for Ferrography Based on Characteristic Analysis of Wear Debris

机译：基于磨损碎片特征分析的铁谱灰色关联度识别研究

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Attention has been focused on how to achieve intelligent automation in ferrographic diagnosis in order to overcome the subjectivity of the diagnosis process. The present paper reports on a technique of characteristic measurement developed on the basis of the VC++ 6.0 programming platform, with characteristic parameters such as area, roundness, and aspect ratio being extracted from images of wear debris based on digital image analysis. However, the extraction of characteristic parameters from a ferrographic image is not the ultimate purpose of ferrographic diagnosis. The wear particles should be classified into several pre-decision categories and their statistical distribution should also be calculated. The grey relational grade theory is introduced in this paper as a way to recognise wear debris and a new software system has been developed to deal with the problems occurring in the automation of ferrographic diagnosis. It is shown that the identification rules can be used to treat some real wear debris images with generally satisfactory results.
机译：为了克服诊断过程的主观性，注意力已经集中在如何在铁素体学诊断中实现智能自动化。本文报告了一种基于VC ++ 6.0编程平台开发的特征测量技术，该特征测量技术基于数字图像分析从磨损碎片的图像中提取了诸如面积，圆度和纵横比之类的特征参数。但是，从铁素体图像中提取特征参数并不是铁素体诊断的最终目的。磨损颗粒应分为几个预先确定的类别，还应计算其统计分布。本文介绍了灰色关联度理论作为一种识别磨损碎片的方法，并开发了一种新的软件系统来解决铁素体诊断自动化中出现的问题。结果表明，该识别规则可用于处理一些真实磨损的碎片图像，效果总体令人满意。

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