In many circumstances, the quality of a process or product is best characterized by a given mathematical function between a response variable and one or more explanatory variables that is typically referred to as profile. There are some investigations to monitor auto-correlated linear and nonlinear profiles in recent years. In the present paper, we use the linear mixed models to account autocor...

When monitoring a process which has multivariate normal variables, the Shewhart-type control chart (Hotelling (1947)) traditionally used for monitoring the process mean vector is effective for detecting large shifts, but for detecting small shifts it is more effective to use the multivariate exponentially weighted moving average (MEWMA) control chart proposed by Lowry et al. (1992). It has been...

The Multivariate EWMA control chart, MEWMA, Lowry, Woodall, Champ and Ridgon [1] and its univariate version EWMA, may be designed to efficiently detect small shifts in the mean vector of a set of p quality characteristics of a production process. However, this work presents a method for the optimal design of MEWMA and EWMA charts parameters to control processes where it is not convenient to det...

The EWMA quality control chart, and its multivariate version (MEWMA), may be designed to efficiently detect small shifts in the mean vector of a set of p quality characteristics of a production process. However, this work presents a method for the optimal design of the parameters of the MEWMA and EWMA charts to control processes where it is not convenient to detect small magnitude shifts and, a...

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...

At present, due to the rapid development of domestic economy, environmental pollution problems are becoming increasingly serious, especially such as PM2.5 and PM10 caused by industrial emissions waste gas most urgent. The general linear profile model multivariate exponential weighted moving average control chart (MEWMA) is designed perform monitoring evaluating air quality index (AQI) in Tianji...

An industrial company requires quality control to maintain consistency from the production results so that it is able compete with other companies in world market. In sector, most processes are influenced by more than one characteristic. One tool can be used characteristic Multivariate Exponentially Weighted Moving Average (MEWMA) chart. The graph determine whether process has been controlled o...

The drawbacks to multivariate charting schemes is their inability to identify which variable was the source of the signal. The multivariate exponentially weighted moving average (MEWMA) developed by Lowry, et al (1992) is an example of a multivariate charting scheme whose monitoring statistic is unable to determine which variable caused the signal. In this paper, the run length performance of m...

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. The main problem is how to interpret the out-ofcontrol signal of a multivariate chart. For example, in the case of control charts designed to monitor the mean vector, the chart signals showing that it must...

In many practical situations, multiple variables often need to be monitored simultaneously to ensure the process is in control. In this article, we develop a feasible multivariate monitoring procedure based on the general Multivariate Exponentially Weighted Moving Average (MEWMA) to monitor the multivariate count data. The multivariate count data is modeled using Poisson-Lognormal distribution ...