quality control plays an important role in increasing the product quality. fuzzy control charts are more sensitive than shewhart control chart. hence, the correct use of fuzzy control chart leads to producing better-quality products. this area is complex because it involves a large scope of industries, and information is not well organized. in this research, we provide a literature review of th...

Control charts are used to identify the presence of assignable cause of variation in the process. Non-parametric control chart is an emerging area of recent development in the theory of SPC. Its main advantage is that it does not require any knowledge about the underlying distribution of the variable. In this paper a non-parametric control chart for controlling variability has been developed. I...

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 ...

although control charts are very common to monitoring process changes, they usually do not indicate the real time of the changes. identifying the real time of the process changes is known as change-point estimation problem. there are a number of change point models in the literature however most of the existing approaches are dedicated to normal processes. in this paper we propose a novel appro...

abstract. in recent years several studies have shown that  control charts with adaptive schemes or double sampling plans detect both small and moderate shifts in the process mean more quickly than the traditional shewhart  chart. in the classical double sampling  chart, the difference between two points were placed in the central region of first stage was not considered. in this study, a new co...

Special Issue A Comparison of Shewhart Individuals Control Charts Based on Normal, Non-parametric, and Extreme-value Theory‡ M. B. (Thijs) Vermaat1, Roxana A. Ion2, Ronald J. M. M. Does1,3,∗,† and Chris A. J. Klaassen1,3 1Institute for Business and Industrial Statistics of the University of Amsterdam, IBIS UvA BV, Plantage Muidergracht 24, 1018 TV Amsterdam, The Netherlands 2Eurandom, PO Box 51...

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...

Control charts are one of the most important tools in statistical process control that lead to improve quality processes and ensure required quality levels. In traditional control charts, all data should be exactly known, whereas there are many quality characteristics that cannot be expressed in numerical scale, such as characteristics for appearance, softness, and color. Fuzzy sets theory is a...

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 ...