The Adaptive Exponentially Weighted Moving Average (AEWMA) control chart has the advantage of detecting in balance mixed range of mean shifts. Its performance has been studied under the assumption that the process parameters are known. Under this assumption, previous studies have shown that AEWMA provides superior statistical performance when compared to other different types of control charts....

The main purpose of this article is the development and the study of runs rules applied to a Shewhart type control chart for the mean in order to detect non-random patterns in Phase I, when we are interested to conclude if the process is in a stable condition for the first twenty five or thirty samples in order to estimate the process’ parameters. A characterization scheme such as which rule is...

To detect sustainable changes in the manufacturing processes, memory-type charting schemes are frequently functioning. The recently designed, homogenously weighted moving average (HWMA) technique is effective for identifying substantial processes. make HWMA chart more persistent shifts industrial a double (DHWMA) has been proposed recently. This study intends to develop triple (THWMA) efficient...

Identifying convergence in numerical optimization is an ever-present, difficult, and often subjective task. The statistical framework of Gaussian process surrogate model optimization provides useful measures for tracking optimization progress; however, the identification of convergence via these criteria has often provided only limited success and often requires a more subjective analysis. Here...

Control charts are used to detect assignable causes in different manufacturing and nonmanufacturing processes. This study presents a new maximum exponentially weighted moving average (Max-EWMA) chart for joint unit interval time magnitude monitoring. To this end, beta distribution is considered whereas simplex magnitude. Average run length (ARL), standard deviation of (SDRL), quantiles evaluate...

Control charts have been broadly used for monitoring the process mean and dispersion. Cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts are memory control charts as they utilize the past information in setting up the control structure. This makes CUSUM and EWMA-type charts good at detecting small disturbances in the process. This article proposes two new mem...

In this paper, a remedial measure is first proposed to eliminate the effect of autocorrelation in phase-ІІ monitoring of autocorrelated polynomial profiles, where there is a first order autoregressive (AR(1)) relation between the error terms in each profile. Then, a control chart based on the generalized linear test (GLT) is proposed to monitor the coefficients of polynomial profiles and an R-c...

The performance of an X-bar chart is usually studied under the assumption that the process standard deviation is well estimated and does not change. This is, of course, not always the case in practice and X-bar charts are not robust against errors in estimating the process standard deviation or changing standard deviation. In this paper, the use of an exponentially weighted moving average (EWMA...

Most SPC approaches for detecting the process variance assume that it remains constant over time. However, these approaches may not be well handled with the case in which patterned changes exist. In this paper, we propose a new control chart which integrates EWMA procedure with generalized likelihood ratio (GLR) test statistics for monitoring patterned variance shifts. It can be easily designed...

Control charts are graphical tools widely used to monitor manufacturing processes to quickly detect any change in a process that may result in a change in product quality. The statistic plotted on a control chart is based on samples of n ≥ 1 observations (rational subgroups) that may be taken at regular sampling intervals. However, there are numerous practical applications using individual obse...