Abstract
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.
Highlights
Statistical process control (SPC) is a collection of tools for the monitoring of process parameters
The simplicity and ease of interpretation make Shewhart charts more common in use, but they are relatively insensitive to small shifts in process parameters, whereas, cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts are mostly used for the detection of smaller shifts in process parameters
We found that no study as of yet, utilizes the sequential sampling (SS) scheme for increasing the efficiency of the nonparametric control charts
Summary
Statistical process control (SPC) is a collection of tools for the monitoring of process parameters. If the distribution of the process production is unknown, the traditional control limits no longer remain effective and the detection ability of parametric control charts can be negatively affected. In SPC literature, various sampling techniques are used to improve the performance of the parametric and nonparametric control charts. We found that no study as of yet, utilizes the SS scheme for increasing the efficiency of the nonparametric control charts. To fill this gap, we propose a nonparametric EWMA sign chart, based on arcsine transformation, using the SS scheme, for efficient monitoring of process location.
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