Multivariate statistical process control deserves particular attention in the recent scenario. Though, Hotelling control chart is quite popular and widely used technique in this field but its performance is deteriorated when the underlying distribution of the quality characteristics is not following multivariate normal distribution. Hence the need of developing a non-parametric multivariate con...

this paper addresses the design of control charts for both variable ( x chart) andattribute (u and c charts) quality characteristics, when there is uncertainty about the processparameters or sample data. derived control charts are more flexible than the strict crisp case, dueto the ability of encompassing the effects of vagueness in form of the degree of expert’spresumption. we extend the use o...

This paper presents an improved version of the Shewhart-type generalized variance |S| control chart for multivariate Gaussian process dispersion monitoring, based on the Cornish-Fisher quantile formula for non-normality correction of the traditional normal based 3-sigma chart limits. Also, the exact sample distribution of |S| and its quantiles (chart exact limits) are obtained through the Meije...

in this paper, first the available single charting methods, which have been proposed to detect simultaneous shifts in a single process mean and variance, are reviewed. then, by designing proper simulation studies these methods are evaluated in terms of in-control and out-ofcontrol average run length criteria (arl). the results of these simulation experiments show that the ewma and ewms methods ...

A nonparametric control chart based on a bivariate signed-rank test is developed for monitoring the changes in the location of a bivariate process. The average run length performance of the proposed nonparametric chart is investigated using a simulation study and is compared with a parametric control chart under bivariate normal and bivariate double exponential distributions. Further the perfor...

Most commonly used control charts for monitoring quality characteristics of the processes were developed under the assumption that the observations are randomly sampled from a normal population. It is well known that these control charts have more false alarms than usual when processes are positively autocorrelated. One remedy is to adjust the control limits such that the modified control chart...

Centuries of chart-making have produced some outstanding charts tailored specifically to the data being visualized. They have also produced a myriad of less-than-outstanding charts in the same vein. I instead present a set of techniques that may be applied to arbitrary datasets with specific properties. In particular, I describe two techniques – Nested Category Maps and Correlation Maps – for v...