Department of Mathematics and Statistics, King Fahad University of Petroleum and Minerals, Dhahran, Saudi Arabia.
Department of Statistics Government College University Faisalabad, Pakistan.
PLoS One. 2019 Nov 21;14(11):e0225330. doi: 10.1371/journal.pone.0225330. eCollection 2019.
Control charts play a significant role to monitor the performance of a process. Nonparametric control charts are helpful when the probability model of the process output is not known. In such cases, the sampling mechanism becomes very important for picking a suitable sample for process monitoring. This study proposes a nonparametric arcsine exponentially weighted moving average sign chart by using an efficient scheme, namely, sequential sampling scheme. The proposal intends to enhance the detection ability of the arcsine exponentially weighted moving average sign chart, particularly for the detection of small shifts. The performance of the proposal is assessed, and compared with its counterparts, by using some popular run length properties including average, median and standard deviation run lengths. The proposed chart shows efficient shift detection ability as compared to the other charts, considered in this study. A real-life application based on the smartphone accelerometer data-set, for the implementation of the proposed scheme, is also presented.
控制图在监测过程性能方面起着重要作用。当过程输出的概率模型未知时，非参数控制图很有帮助。在这种情况下，抽样机制对于为过程监测选择合适的样本非常重要。本研究提出了一种非参数反正弦指数加权移动平均符号图，使用一种有效的方案，即顺序抽样方案。该提案旨在提高反正弦指数加权移动平均符号图的检测能力，特别是对于小偏移的检测。通过使用一些流行的运行长度属性，包括平均、中位数和标准偏差运行长度，评估了该提案的性能，并与其他同类方案进行了比较。与本研究中考虑的其他图表相比，该图表显示出了有效的偏移检测能力。还提出了一个基于智能手机加速度计数据集的实际应用，以实现该方案。
