ABSTRACT

A self-starting process mean monitoring scheme is needed in applications with short production runs or processes subject to degradation. The major challenge in implementing a self-starting monitoring scheme is that there exists little or no historical in-control data to accurately estimate in-control process parameters. In this paper, we propose a new Bayesian self-starting monitoring scheme to detect on-line whether a process mean has exceeded a pre-determined critical threshold. We assume the process is subject to various types of random drift and random jumps prior to exceeding a critical threshold. In comparison with existing self-starting Bayesian schemes in the literature, our model is more flexible in capturing various types of trends and requires less knowledge of process parameters. In addition, the proposed monitoring scheme is much more computationally efficient, rendering it much more applicable for numerous practical situations where model parameter information is limited and timely detection of a critical event is crucial. Numerical studies based on simulated signals and several real data sets are used to evaluate the performance of the proposed method and compare with existing methods in the literature. The proposed method is shown to be less sensitive to parameter misspecification, more flexible in capturing various trends in the data, and much more computationally efficient.

摘要

自启动过程意味着在生产周期短或过程容易退化的应用中需要监视方案。实施自启动监控方案的主要挑战在于，几乎没有或没有历史控制数据来准确估计控制过程参数。在本文中，我们提出了一种新的贝叶斯自启动监视方案，以在线检测过程均值是否已超过预定的临界阈值。我们假设该过程在超出临界阈值之前会经历各种类型的随机漂移和随机跳跃。与文献中现有的自启动贝叶斯方案相比，我们的模型在捕获各种趋势方面更加灵活，并且对过程参数的了解较少。此外，所提出的监视方案具有更高的计算效率，使其更适用于模型参数信息有限且关键事件的及时检测至关重要的许多实际情况。基于模拟信号和几个实际数据集的数值研究用于评估该方法的性能，并与文献中的现有方法进行比较。所提出的方法显示出对参数错误指定不太敏感，在捕获数据中的各种趋势时更加灵活，并且计算效率更高。基于模拟信号和几个真实数据集的数值研究用于评估该方法的性能，并与文献中的现有方法进行比较。所提出的方法显示出对参数错误指定不太敏感，在捕获数据中的各种趋势时更加灵活，并且计算效率更高。基于模拟信号和几个实际数据集的数值研究用于评估该方法的性能，并与文献中的现有方法进行比较。结果表明，所提出的方法对参数错误指定不太敏感，在捕获数据的各种趋势时更灵活，并且计算效率更高。