The multivariate exponentially weighted moving average (MEWMA) control chart is one of the best statistical control chart that are usually used to detect simultaneous small deviations on the mean of more than one cross-correlated quality characteristics. The economic design of MEWMA control charts involves solving a combinatorial optimization model that is composed of a nonlinear cost function ...

In this paper a multivariate quality control procedure is offered in which several correlated stages are present in production systems and in each stage there are several correlated control variable. Using Hotelling's T2 statistic, first, each stage is tested for being out of control. Then out of control variables are selected using Murphy's method. The remainder of the research involves evalua...

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 ...

Statistical process control charts have been widely used for monitoring the quality characteristics of manufacturing processes. Analysis of unnatural patterns is an important aspect of control charting. The occurrence of unnatural patterns implies that a process is influenced by assignable causes, and corrective actions should be taken. In practice, there are many situations in which the simult...

In recent years, the monitoring of compositional data using control charts has been investigated in Statistical Process Control field. this study, we will design a Phase II Multivariate Exponentially Weighted Moving Average (MEWMA) chart with variable sampling intervals to monitor based on isometric log-ratio transformation. The Time Signal be computed Markov chain approach investigate performa...

The method of change (or anomaly) detection in high-dimensional discrete-time processes using a multivariate Hotelling chart is presented. We use normal random projections as a method of dimensionality reduction. We indicate diagnostic properties of the Hotelling control chart applied to data projected onto a random subspace of R. We examine the random projection method using artificial noisy i...

The method of change (or anomaly) detection in high-dimensional discrete-time processes using a multivariate Hotelling chart is presented. We use normal random projections as a method of dimensionality reduction. We indicate diagnostic properties of the Hotelling control chart applied to data projected onto a random subspace of R. We examine the random projection method using artificial noisy i...

Monitoring multivariate quality characteristics is very common in production and service environment. Therefore, many control charts have been suggested by authors for monitoring multivariate processes. In another side, profile monitoring is a new approach in the area of statistical process control. In this approach, the quality of a product or a process is characterized by a relation between o...