Abstract Industry 4.0 vision is bringing more challenges to quality practitioners. With advances in data acquisition systems and lowered costs, it is not uncommon in production systems to have more quality characteristics to be monitored. Once a control chart design is given, monitoring of a quality characteristic can be simplified by using a computer program that does the control statistic calculations and plots these together with the control limits over time. Then, operators trained on operational use of control charts can look for root causes of alarms triggered and take corrective actions. Nevertheless, design of a control chart requires considerable amount of time and sound knowledge on statistical methods. To eliminate the labor and automate the design of a control chart, algorithmic approaches are required to be implemented as instructions for computer programs. To serve this purpose, Shewhart individuals (I-) chart with various design choices is used in the following to algorithmically detect and eliminate outliers in the Phase I sample for estimating unknown process parameters. Hence, our approach is not restricted with the assumption of exclusively in-control observations in the sample as typically considered in the literature for evaluating the effects of parameter estimation. Estimated parameters are then incorporated into the design of exponentially weighted moving average (EWMA) chart, which is used in Phase II for process monitoring. Average and standard deviation of average run length performances of the EWMA chart are used as metrics, these are computed by using integral equations, and the results are assessed by an algorithm proposed for systematically generating knowledge on how to estimate process parameters and design the EWMA chart. The approach is general and can be implemented with alternative Phase I and Phase II control charts.

中文翻译：

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用于设计阶段 II ewma 控制图的阶段 I 异常值检测和参数估计的算法方法

摘要 工业 4.0 愿景正在给质量从业者带来更多挑战。随着数据采集系统的进步和成本的降低，生产系统中需要监控更多质量特性的情况并不少见。一旦给出了控制图设计，就可以通过使用计算机程序来简化对质量特性的监控，该程序进行控制统计计算并将这些计算与控制限制一起绘制出来。然后，受过控制图操作使用培训的操作员可以查找触发警报的根本原因并采取纠正措施。然而，控制图的设计需要大量的时间和统计方法方面的扎实知识。为了消除劳动力并自动化控制图的设计，算法方法需要作为计算机程序的指令来实现。为达到此目的，以下使用具有各种设计选择的休哈特个体 (I-) 控制图以算法方式检测和消除阶段 I 样本中的异常值，以估计未知过程参数。因此，我们的方法不受样本中完全受控观察的假设的限制，如文献中通常考虑的用于评估参数估计的效果。然后将估计参数纳入指数加权移动平均 (EWMA) 图表的设计中，该图表在第二阶段用于过程监控。EWMA 图表的平均运行长度性能的平均值和标准偏差用作度量，这些是通过使用积分方程计算的，结果通过一种算法进行评估，该算法旨在系统地生成有关如何估计过程参数和设计 EWMA 图表的知识。该方法是通用的，可以使用替代的阶段 I 和阶段 II 控制图来实施。