Normality is a common assumption for many quality control charts. One should expect misleading results once this assumption is violated. In order to avoid this pitfall, we need to evaluate this assumption prior to the use of control charts which require normality assumption. However, in certain cases either this assumption is overlooked or it is hard to check. Robust control charts and bootstra...

Acceptance control charts are effective tools tomonitor capable processes in which the fraction of the produced nonconforming items is very low. In these charts, some controlled changes in the process mean are allowed, and the production of a specified number of defectives is tolerated. Designing these acceptance control charts by considering the cost of sampling, detecting, and investigating o...

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 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...

Quality control is one of the most used statistical applications in industry. The control of attribute quality characteristics is a subject of study due to the need of improving the efficiency of the charts. The present paper deals with the improvement of the classical u chart, using the Daudin strategy, based in a double sampling scheme and a variable sample size. The DS-u chart is proposed. T...

I N STATISTICAL process control (SPC), one major concern is whether there has been a change of the distribution from the target in the process. To answer this question, we have to distinguish the variation due to real change of distribution (assignable causes) from that due to random error (chance causes). Many kinds of control charts serve this purpose. When there is no change and the process ...

This paper studies design schemes for the X control chart under non-normality. Different estimators of the standard deviation are considered and the effect of the estimator on the performance of the control chart under non-normality is investigated. Two situations are distinguished. In the first situation, the effect of non-normality on the X control chart is investigated by using the control l...

In many applications the shift directions of observation vectors are limited, which allows focusing detection power on a limited subspace with improved sensitivity. This paper develops a new multivariate nonparametric statistical process control chart for monitoring location parameters, which is based on integrating a directional multivariate spatial-sign test and exponentially weighted moving ...

The Shewhart and the Bonferroni-adjustment S control charts are usually applied to monitor the standard deviation of a quality characteristic. The control limits of these charts are constructed using approximately the normal distribution in case that the standard deviation parameter is known or unknown. In this paper, we establish a new S chart that is based approximately on the normal distribu...