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首页> 外文期刊>International journal of quality, statistics, & reliability >A One-Class Classification-Based Control Chart Using the K-Means Data Description Algorithm
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A One-Class Classification-Based Control Chart Using the K-Means Data Description Algorithm

机译：使用K均值数据描述算法的基于分类的一类控制图

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This paper aims to enlarge the family of one-class classification-based control charts, referred to as OC-charts, and extend their applications. We propose a new OC-chart using the K-means data description (KMDD) algorithm, referred to as KM-chart. The proposed KM-chart gives the minimum closed spherical boundary around the in-control process data. It measures the distance between the center of KMDD-based sphere and the new incoming sample to be monitored. Any sample having a distance greater than the radius of KMDD-based sphere is considered as an out-of-control sample. Phase I and II analysis of KM-chart was evaluated through a real industrial application. In a comparative study based on the average run length (ARL) criterion, KM-chart was compared with the kernel-distance based control chart, referred to as K-chart, and the k-nearest neighbor data description-based control chart, referred to as KNN-chart. Results revealed that, in terms of ARL, KM-chart performed better than KNN-chart in detecting small shifts in mean vector. Furthermore, the paper provides the M ATLAB code for KM-chart, developed by the authors.
机译：本文旨在扩大一类基于分类的控制图（称为OC图）的范围，并扩展其应用范围。我们使用K-means数据描述（KMDD）算法提出了一种新的OC-chart，称为KM-chart。拟议的KM图给出了控制中过程数据周围的最小闭合球形边界。它测量基于KMDD的球体中心与要监视的新传入样本之间的距离。任何距离大于基于KMDD的球体的半径的样本都被视为失控样本。通过实际的工业应用评估了KM图的第一阶段和第二阶段分析。在基于平均游程长度（ARL）标准的比较研究中，将KM-chart与基于核距离的控制图（称为K-chart）和基于k最近邻数据描述的控制图（称为K-chart）进行了比较。以KNN图表的形式显示。结果表明，就ARL而言，KM图在检测均值矢量的微小变化方面比KNN图更好。此外，本文提供了由作者开发的用于KM图的M ATLAB代码。

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