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 ...

The reliability data is getting used to monitor and improve the quality of products or services. Nowadays, most of products or services are the results of processes with dependent stages referred to as multi-stage process. In these processes, the quality characteristics are affected by the quality characteristics in the previous stages, called as cascade property. In some cases, it is not possi...

Nonparametric control charts can provide a robust alternative in practice to the data analyst when there is a lack of knowledge about the underlying distribution. A nonparametric exponentially weighted moving average (NPEWMA) control chart combines the advantages of a nonparametric control chart with the better shift detection properties of a traditional EWMA chart. A NPEWMA chart for the media...

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...

Multivariate control charts are very effective when monitoring several correlated characteristics. Proper process control consists of monitoring both mean and variability. In multivariate settings, the multivariate exponentially weighted moving average (MEWMA) is ideal to monitor the mean vector. Similarly, the recently-proposed multivariate exponentially weighted moving covariance matrix (MEWM...

Hotelling T 2 control chart not only reflects the correla-tions between different quality characteristics but also has good efficiency on monitoring multivariate in production process. A new alternative was constructed after original products data are processed by using exponentially weighted moving average for cumulating failure effects because is ineffective detecting minimal mean deviations....

Control charts are themost extensively used technique to detect the presence of special cause variations in processes. They can be classified intomemory andmemoryless control charts. Cumulative sum and exponentially weightedmoving average control charts are memory-type control charts as their control structures are developed in such a way that the past information is not ignored as it is done i...