Assuming a first-order auto-regressive model for the auto-correlation structure between observations, in this paper, a transformation method is first employed to eliminate the effect of auto-correlation. Then, a maximum likelihood estimator (MLE) of a step change in the parameters of the transformed model is derived and three separate EWMA control charts are used to monitor the parameters of th...

Abstract:
 In many real-world applications, the data to be used in a control charting method are not crisp since they approximated due environmental uncertainties, these situations, fuzzy numbers and linguistic variables grab such uncertainties. That is why use of chart, which used, justified. As an exponentially weighted moving average (EWMA) scheme usually detect small shifts, this paper ...

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 ...

identification of a real time of a change in a process, when an out-of-control signal is present is significant. this may reduce costs of defective products as well as the time of exploring and fixing the cause of defects. another popular topic in the statistical process control (spc) is profile monitoring, where knowing the distribution of one or more quality characteristics may not be appropr...

Control Chart Patterns (CCPs) recognition is one the most important concepts in control chart application. Relating the patterns exhibited on the control chart to assignable causes is an ambiguous and vague task especially when multiple patterns co-exist. In this study, a fuzzy rule-based system is developed for X ̅ control charts to prioritize the control chart causes based on the accumulated e...