BACKGROUND In most clinical monitoring cases there is a need to track more than one quality characteristic. If separate univariate charts are used, the overall probability of a false alarm may be inflated since correlation between variables is ignored. In such cases, multivariate control charts should be considered. PURPOSE This paper considers the implementation and performance of the T(2), ...

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 autocorrelated linear and nonlinear profiles in recent years. In the present paper, we use the linear mixed models to account autocorr...

Control limits in traditional Multivariate Quality Control Charts (MQCC), such as Hotelling's T control chart, Multivariate Cumulative Sum (MCUSUM) and Multivariate Exponentially-Weighted Moving Average (MEWMA) control charts are based on multivariate normal assumption. This assumption is not usually satisfied, especially in real life applications. The purpose of a control chart based on suppor...

We propose a new approach to monitor an overall mean shift of a bi-variate quality control system. To do this, we first define beliefs on deciding whether the quality characteristics are in out-of-control state. Then, by taking new observations, in an iterative approach we update the belief of each quality characteristics being out-of-control. This task is performed using a recursive method and...

Simultaneously monitoring two or more quality characteristics depends on the development of more specific statistical tools to detect, identify and analyze the major causes of variability that affect the behavior of the production process. The multivariate control charts represent one of these emerging statistical techniques successfully used to monitor simultaneously several correlated charact...

Multivariate cumulative sum (MCUSUM) control charts are widely used in industry because they are powerful and easy to use. They cumulate recent process data to quickly detect out-of-control situations. MCUSUM procedures will usually give tighter process control than classical quality control charts. A MCUSUM signal does not mean that the process is producting bad product. Rather it means that a...

Since little attention has been devoted to multiattribute control charts in the literature, in this research a new methodology is developed to employ the multivariate exponentially weighted moving average (MEWMA) chart for multiattribute processes. Moreover, since the variable sample size and sampling interval (VSSI) MEWMA charts detect small process mean-shifts faster than the traditional MEWM...

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 ...