Abstract

Time between events (TBE) control charts have been widely used to monitor high yield processes. Traditionally, an estimated in-control occurrence rate from a Phase I dataset is used to calculate the control limits when the rate is unknown. However, when Phase I analysis is time consuming or costly, the traditional Phase I/Phase II approach is not feasible. A self-starting method that sequentially updates the occurrence rate can be integrated to overcome this difficulty and leverages the use of TBE chart for cases of lack of in-control data. The motivation behind this study is to compare different self-starting TBE charts to investigate the contribution of such a sequential parameter update method. Our results indicate the potential of these schemes as they provide satisfactory performance when moderate and large sizes of base period before the shift observed. Additionally, time weighted schemes provided promising performance for relatively small sample sizes in the base period. However, our results also show a significant adverse effect in performance when the base periods are contaminated with out-of-control data.

摘要

事件间隔时间 (TBE) 控制图已广泛用于监控高产量过程。传统上，当发生率未知时，使用来自第一阶段数据集的估计受控发生率来计算控制限。但是，当 I 期分析耗时或成本高时，传统的 I 期/II 期方法就不可行了。可以集成一种顺序更新发生率的自启动方法来克服这一困难，并利用 TBE 图来处理缺乏控制数据的情况。本研究背后的动机是比较不同的自启动 TBE 图，以研究这种顺序参数更新方法的贡献。我们的结果表明这些方案的潜力，因为它们在观察到班次前的中等和大型基期时提供令人满意的性能。此外，时间加权方案在基期内为相对较小的样本量提供了有前途的性能。然而，我们的结果也表明，当基期被失控数据污染时，会对绩效产生重大不利影响。