基于聚类的轮廓数据质量监控方法研究
发布时间：2019-06-15 19:33
【摘要】：轮廓线的变点识别是质量管理的研究热点之一,当前研究多以轮廓整体变化为识别对象,而对局部变化问题研究相对较少,且更少有在发现变异时间的同时能够寻找到变化区域在个体轮廓曲线上位置的系统方法。本文针对轮廓线局部变化识别问题,提出基于小波变换和聚类分析的方法。通过仿真性能评价,并与现有方法进行比较,结果显示本方法能够在更小的差异度检测出变化并准确定位变化区域。在文章的末尾,本文采用了一个实例对该方法的效果进行验证。
[Abstract]:The variable point recognition of contours is one of the research hotspots of quality management. At present, most of the research focuses on the overall change of contours, but there are relatively few studies on the local change problems, and there are few systematic methods to find the position of the changing regions on the individual contours at the same time as the variation time is found. In this paper, a method based on wavelet transform and clustering analysis is proposed to solve the problem of local change recognition of contours. The simulation performance is evaluated and compared with the existing methods. The results show that this method can detect the change and accurately locate the change area at a smaller degree of difference. At the end of the paper, an example is used to verify the effect of this method.
【作者单位】： 天津大学管理与经济学部;
【分类号】：O213.1
本文编号：2500456
[Abstract]:The variable point recognition of contours is one of the research hotspots of quality management. At present, most of the research focuses on the overall change of contours, but there are relatively few studies on the local change problems, and there are few systematic methods to find the position of the changing regions on the individual contours at the same time as the variation time is found. In this paper, a method based on wavelet transform and clustering analysis is proposed to solve the problem of local change recognition of contours. The simulation performance is evaluated and compared with the existing methods. The results show that this method can detect the change and accurately locate the change area at a smaller degree of difference. At the end of the paper, an example is used to verify the effect of this method.
【作者单位】： 天津大学管理与经济学部;
【分类号】：O213.1
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，本文编号：2500456
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