论文标题
通过特征向量扰动监视的配置文件监视
Profile Monitoring via Eigenvector Perturbation
论文作者
论文摘要
在统计过程控制中，控制图通常用于检测依次观察到的质量特征的不良行为。设计具有较低误报率（FAR）和检测延迟（$ arl_1 $）的控制图表是一个重要的挑战，尤其是当采样率较高并且控制图具有控制图中的平均值，称为$ arl_0 $，称为$ arl_0 $ 200，为200或更多，通常是在实践中发现的。不幸的是，任意减少通常会增加$ arl_1 $。在特征向量扰动理论的启发下，我们提出了计算快速非参数概况监测的特征向量扰动控制图。我们的仿真研究表明，它表现优于竞争，并实现$ arl_1 \大约1 $和$ arl_0> 10^6 $。
In Statistical Process Control, control charts are often used to detect undesirable behavior of sequentially observed quality characteristics. Designing a control chart with desirably low False Alarm Rate (FAR) and detection delay ($ARL_1$) is an important challenge especially when the sampling rate is high and the control chart has an In-Control Average Run Length, called $ARL_0$, of 200 or more, as commonly found in practice. Unfortunately, arbitrary reduction of the FAR typically increases the $ARL_1$. Motivated by eigenvector perturbation theory, we propose the Eigenvector Perturbation Control Chart for computationally fast nonparametric profile monitoring. Our simulation studies show that it outperforms the competition and achieves both $ARL_1 \approx 1$ and $ARL_0 > 10^6$.
