ABSTRACT This article introduces Bayesian predictive monitoring of time-between-events using Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA) control charts with predictive control limits. It is shown that the proposed methodology not only overcomes the requirement of a large Phase-I data set to establish control limits, but also feasible for online process monitoring. In addition to Bayesian memory-type charts with dynamic control limits, a comparison of the frequentist sequential charts, designed by using the unbiased and biased estimator of the process parameter, is also given in this article. For the performance evaluation of the predictive TBE chart in the presence of practitioner-to-practitioner variability, we use the average of the in-control average run length (AARL) and the standard deviation of the in-control run length (SDARL).

中文翻译：

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用于事件间时间监控的 EWMA 和 CUSUM 图的预测贝叶斯方法

摘要 本文介绍了使用累积总和 (CUSUM) 和指数加权移动平均 (EWMA) 控制图和预测控制限的事件间时间的贝叶斯预测监控。结果表明，所提出的方法不仅克服了建立控制限制的大型第一阶段数据集的要求，而且对于在线过程监控也是可行的。除了具有动态控制限制的贝叶斯记忆型图外，本文还给出了使用过程参数的无偏和有偏估计量设计的频率论序列图的比较。对于在从业者之间存在差异的情况下对预测性 TBE 图表的性能评估，