Most robust control charts in the literature are for monitoring process location parameters, such as mean or median, rather than process dispersion parameters. This paper develops a new robust control chart by integrating a two-sample nonparametric test into the effective change-point model. Our proposed chart is easy in computation, convenient to use, and very powerful in detecting process dispersion shifts. Statistical process control SPC has been widely used in various industrial processes. Most SPC applications assume that the quality of a process can be adequately represented by the distribution of a quality characteristic, and the in-control IC and out-of-control OC distributions are the same with only differing parameters.
An adaptive sample size CUSUM control chart
Application of Cusum Control Chart for Monitoring HIV/AIDS Patients in Nigeria
In this paper, ranked set sampling is used for developing a non-parametric location chart which is developed on the basis of Wilcoxon signed rank statistic. The average run length and some other characteristics of run length are used as the measures to assess the performance of the proposed scheme. It has been observed that the proposed scheme shows superior shift detection ability than some of the competing counterpart schemes covered in this study. Moreover, the proposed control chart is also implemented and illustrated with a real data set. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
A Robust Control Chart for Monitoring Dispersion
Annadi, J. Keats, Douglas Montgomery , George Runger. The Cumulative Sum Chart for process control is designed to detect relatively small shifts in the mean of the process variable.
Resources for teachers. Suggest improvements; provide feedback; point out spelling, grammar, or other errors. We showed earlier that the Shewhart chart is not too sensitive to detecting shifts in the mean. This shift is almost imperceptible in the raw data see the 3rd row in the figure. In fact, as the second row in the figure shows, a surprising amount of movement up and down occurs even when the process is in control.