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 ...

i ABSTRACT There has been much research involving simultaneous monitoring of several correlated quality characteristics that rely on the assumptions of multivariate normality and independence. In real world applications, these assumptions are not always met, particularly when small counts are of interest. In general, the use of normal approximation to the Poisson distribution seems to be justif...

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 ...

Multivariate quality control charts show some advantages to monitor several variables in comparison with the simultaneous use of univariate charts. Nevertheless, there are some disadvantages when multivariate schemes are employed. The main problem is how to interpret the out-of-control signal of a multivariate chart. For example, in the case of control charts designed to monitor the mean vector...

Count data appears widely in finance, biomedical science, psychology, actuarial insurance, road safety and many other areas of life, it is often multivariate. In this paper, we consider using the multivariate exponentially weighted moving average (MEWMA) control chart method to monitor count under assumption Poisson distribution, which derived by He [1] from another form gamma distribution. We ...

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...

Statistical technique used to analyze quality problems and improve process performance is Process Control (SPC). In this study, multivariate control chart the of graduates Statistics Study Program, FMIPA USK, namely using Multivariate Exponentially Weighted Moving Average (MEWMA) average, Variance (MEWMV) variability, analysis Capability assess entire process. Data secondary data, GPA duration ...

As a common approach in the development of control charts Statistical Process Control (SPC), an industrial process is monitored with one or more quality characteristics using their corresponding distributions. Note though, modelling through relation between some independent and dependent variables alternative which designated as profiles monitoring. This study proposes integration adaptive to c...

Abstract The article delves into the development of a Non-Gaussian Process Monitoring Strategy for Copper Cathode Manufacturing Unit (CCMU). monitoring strategy being devised highlighted issue multi-stage process via usage Multi-block Independent Component Analysis (MBICA) techniques. MBICA is multi-block variant ICA technique which prevalently used laden with non-Gaussian or non-normal data. D...