Design of Multivariate Hotelling’s T2 Control Chart Based on Medical Images Processing

A New Bootstrap Based Algorithm for Hotelling’s T2 Multivariate Control Chart

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...

On the multivariate variation control chart

Multivariate control charts such as Hotelling`s T^ 2 and X^ 2 are commonly used for monitoring several related quality characteristics. These control charts use correlation structure that exists between quality characteristics in an attempt to improve monitoring. The purpose of this article is to discuss some issues related to the G chart proposed by Levinson et al. [9] for detecting shifts in ...

Multivariate Multinomial T2 Control Chart using Fuzzy Approach

Economic- Statistical design of T2 control chart with the VSSC scheme

T2 control charts are used to monitor a process when more than one quality variable associated with process is being observed. Recent studies have shown that using variable sample size (VSS) schemes result in charts with more statistical power when detecting small to moderate shifts in the process mean vector. This paper presents an economic- statistical design of T2 control charts with variabl...

Economic Design of T2 −V SSC Chart Using Genetic Algorithms

The principal function of a control chart is to help management distinguish different sources of variation in a process. Control charts are widely used as a graphical tool to monitor a process in order to improve the quality of the product. Chen and Hsieh (2007) have designed a T2 control chart using a Variable Sampling Size and Control limits (V SSC) scheme. They have shown that using the V SSC...