Change-point design-based charting schemes for monitoring process variability

ABSTRACT

Most of the recommended nonparametric control charts apply to monitor location parameter. In this paper, we propose a nonparametric exponentially weighted moving average (EWMA) control chart for monitoring scale parameter, which combines the Ansari–Bradley test and the framework of change point detection. As with most nonparametric control charts, a large number of historical observations are used to motivate the control chart, and we propose a nonparametric EWMA control chart that achieves the same purpose with a very small number of historical observations. Simulation results demonstrate that our control chart is effective and robust for monitoring scale parameter shifts. For illustration of our main results, an applied example demonstrates the practicality of this control chart for monitoring scale parameter.

KEYWORDS:

• Nonparametric method
• scale parameter
• Ansari-Bradley test
• change point detection

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Supported by National Natural Science Foundation of China (12171391), Shaanxi Province Science and Technology, Program (2018JQ1075)

Notes on contributors

Mengjie Tang

Mengjie Tang is currently a graduate student in Department of Statistics, School of Mathematics, Northwest University, Xi’an, China. Her research interest is in Statistical Process Control.

Dan Wang

Dan Wang is an Associate Professor in Department of Statistics, School of Mathematics, Northwest University, Xi’an, China. Her research interest is in Statistical Process Control.