The control limits of an exponentially weighted moving average (EWMA) control chart should vary with time, approaching asymptotic limits as time increases. However, previous analytic analyses of EWMA charts consider only asymptotic control limits. In this article, the run length properties of EWMAs with time-varying control limits are approximated using nonhomogeneous Markov chains. Comparing t...

.Classifying streaming data requires the development of methods which are computationally efficient and able to cope with changes in the underlying distribution of the stream, a phenomenon known in the literature as concept drift. We propose a new method for detecting concept drift which uses an Exponentially Weighted Moving Average (EWMA) chart to monitor the misclassification rate of an strea...

This work presents an exponentially weighted moving average (EWMA)-based synthesised control scheme for a process with outliers. Based on experience, some processes occasionally have outliers. The use of traditional mean ( X ) and range (R) control charts (denoted as X=R) for monitoring process mean and variance leads to high-level false alarms. In this study, the fast-detection technique (EWMA...

Fault detection and root cause identification are both important tasks in Multivariate Statistical Process Control (MSPC) for improving process and product quality. Most traditional control charts, including Hotelling’s T 2 chart and the Multivariate Exponential Weighted Moving Average (MEWMA) chart, separate the two tasks into independent and successive procedures by signaling the existence of...

The Extended Exponentially Weighted Moving Average (extended EWMA) control chart is one of the charts and can be used to quickly detect a small shift. performance evaluated with average run length (ARL). Due deriving explicit formulas for ARL on two-sided extended EWMA trend autoregressive or AR(p) model has not been reported previously. aim this study derive as well AR(1) AR(2) models exponent...

A quality-control chart based on exponentially weighted moving averages (EWMA) has, in the past few years, become a popular tool for controlling inaccuracy in industrial quality control. In this paper, I explain the principles of this technique, present some numerical examples, and by computer simulation compare EWMA with other control charts currently used in clinical chemistry. The EWMA chart...

Quality control charts are very effective in detecting out of control signals but when a control chart signals an out of control condition of the process mean, searching for a special cause in the vicinity of the signal time would not always lead to prompt identification of the source(s) of the out of control condition as the change point in the process parameter(s) is usually different from th...

Unlike a Shewhart chart, the exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts are memory control charts (also known as time weighted control charts) that are used for a quick detection of small shifts in the process mean. Control charts that combine information from present and past samples, like the EWMA and CUSUM charts have the ability to detect process changes ...

In order to provide a balanced protection against range of shift sizes in high-quality processes, new one-sided adaptive truncated exponentially weighted moving average (ATEWMA) control chart with known and estimated parameters is developed for monitoring time-between-events (TBE) data. A dedicated Markov chain model established evaluating the run length properties operating conditions. Further...