Directional change‐point detection for process control with multivariate categorical data
Directional change‐point detection for process control with multivariate categorical data
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DOI：
10.1002/nav.21525
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发表时间：
2013-03
期刊：
影响因子：
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通讯作者：
Jun Yu Li;F. Tsung;Changliang Zou
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文献类型：
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作者：
Jun Yu Li;F. Tsung;Changliang Zou
大多数现代过程涉及多个质量特性，这些特性都是在属性级别上测量的，并且它们的总体质量同时由这些特性决定。特征因子之间往往存在相关性，因此必须采用多变量分类控制技术。我们研究了多变量分类过程（MCP）的I期分析，以识别参考数据集中是否存在变点。提出了一种基于对数线性模型的方向变点检测方法。该方法利用方向移位信息，并将MCP集成到多元二项和多元多项分布的统一框架中。还提出了一种用于识别变点位置和换挡方向的诊断方案。进行数值模拟以证明检测有效性和诊断准确性。© 2013 Wiley Periodicals，Inc.海军研究后勤，2013年
Most modern processes involve multiple quality characteristics that are all measured on attribute levels, and their overall quality is determined by these characteristics simultaneously. The characteristic factors usually correlate with each other, making multivariate categorical control techniques a must. We study Phase I analysis of multivariate categorical processes (MCPs) to identify the presence of change‐points in the reference dataset. A directional change‐point detection method based on log‐linear models is proposed. The method exploits directional shift information and integrates MCPs into the unified framework of multivariate binomial and multivariate multinomial distributions. A diagnostic scheme for identifying the change‐point location and the shift direction is also suggested. Numerical simulations are conducted to demonstrate the detection effectiveness and the diagnostic accuracy.© 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013