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Identifying time of a monotonic change in the fraction nonconforming of a high-quality process

机译:确定高质量过程中不合格分数的单调变化的时间

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When a signal is detected by control charts, a search begins to identify and eliminate the sources of this signal. Knowing when a process has changed is very helpful for this purpose. The unknown special point that the process changed for the first time is referred to as change point. In this paper, we propose a maximum-likelihood estimator for the behavior model of the process fraction nonconforming in a high-quality process monitored with a cumulative count of conforming (CCC) control chart. We estimate the time of change without requiring the prior knowledge of the change type rather than we assume the type of change present belongs to a family of monotonic changes. Then, we compare the performance of the proposed change point estimator relative to estimators for the process fraction nonconforming change point derived under a single step and a linear trend change assumption. We do this for a number of monotonic change types following a signal from a CCC control chart. Finally, the application of the proposed change point estimator is shown through a real case.
机译:当控制图检测到信号时,将开始搜索以识别并消除该信号的来源。知道何时更改了流程对此非常有帮助。流程第一次更改的未知特殊点称为更改点。在本文中,我们提出了一个最大似然估计器,该模型用于在合格过程的累积计数(CCC)控制图监视下的高质量过程中,不合格过程分数的行为模型。我们估计变更时间而无需事先了解变更类型,而不是假设当前的变更类型属于单调变更族。然后,我们比较在单个步骤和线性趋势变化假设下得出的过程分数不合格变化点的拟议变化点估算器相对于估算器的性能。我们根据来自CCC控制图的信号对许多单调变化类型执行此操作。最后,通过实际案例展示了所提出的变化点估计器的应用。

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