基于T2的PCA混合控制图的性能与KDE控制极限用于监控变量和属性特征
Muhammad Ahsan1, Muhammad Mashuri2, Dedy Dwi Prastyo2
1Deparment of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia. muh.ahsan@its.ac.id.
Scientific reports
|March 29, 2024
概括
本研究评估了混合多变量T2控制图,使用内核密度来检测异常值和过程转移. 图表显示,在识别混合异常值和流程转移方面,准确度有所提高,尽管不平衡数据仍然存在挑战.
科学领域:
- 统计过程控制 统计过程控制
- 质量工程 质量工程
- 数据挖掘 数据挖掘
背景情况:
- 多变量控制图表对于监控复杂过程至关重要.
- 现有的方法可能会在混合数据类型和异常值检测方面遇到困难.
- 主要组件分析 (PCA) 经常用于多变量数据的维度减少.
研究的目的:
- 为了评估混合多变量T2控制图的性能.
- 使用PCA评估图表在检测异常值和流程转移方面的有效性.
- 调查数据特征 (异常值比例,属性平衡) 对图表表现的影响.
主要方法:
- 开发一个结合PCA的混合多变量T2控制图.
- 使用核密度估计方法计算控制极限.
- 通过模拟研究进行绩效评估,使用不同的异常值百分比和属性平衡.
主要成果:
- 拟议的控制图表显示,在识别混合异常值方面,即使增加了30%,也表现出一致的准确性.
- 对于平衡的属性品质,观察到高错误报警率,导致错误检测.
- 掩盖效应成为不平衡的属性和过度比例的一个重要问题.
- 图表显示了检测过程转移的性能改善.
结论:
- 混合多变量T2控制图,利用PCA和内核密度,提供强大的异常值和过程转移检测功能.
- 数据不平衡和高异常值比例带来了需要进一步调查的挑战.
- 该图表显示了加强复杂工业过程中的质量控制的前景.
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