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Real-time process monitoring using kernel distances

机译：使用内核距离进行实时过程监控

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摘要

Real-time monitoring is an important task in process control. It often relies on estimation of process parameters in Phase I and Phase II and aims to identify significant differences between the estimates when triggering signals. Real-time contrast (RTC) control charts use classification methods to separate the Phase I and Phase II data and monitor the classification probabilities. However, since the classification probability statistics take discretely distributed values, the corresponding RTC charts become less efficient in the detection ability. In this paper, we propose to use distance-based RTC statistics for process monitoring, which are related to the distance from observations to the classification boundary. We illustrate our idea using the kernel linear discriminant analysis (KLDA) method and develop three distance-based KLDA statistics for RTC monitoring. The performance of the KLDA distance-based charting methods is compared with the classification probability-based control charts. Our results indicate that the distance-based RTC charts are more efficient than the class of probability-based control charts. A real example is used to illustrate the performance of the proposed method.
机译：实时监控是过程控制中的重要任务。它通常依赖于阶段I和阶段II中过程参数的估计，目的是在触发信号时识别估计之间的显着差异。实时对比（RTC）控制图使用分类方法来分离阶段I和阶段II数据并监视分类概率。但是，由于分类概率统计采用离散分布的值，因此相应的RTC图在检测能力方面变得效率较低。在本文中，我们建议使用基于距离的RTC统计信息进行过程监控，这与从观察到分类边界的距离有关。我们使用核线性判别分析（KLDA）方法说明了我们的想法，并开发了三种基于距离的KLDA统计信息进行RTC监视。将基于KLDA距离的制图方法的性能与基于分类概率的控制图进行比较。我们的结果表明，基于距离的RTC图表比基于概率的控制图更为有效。一个真实的例子用来说明所提出方法的性能。

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