With process computers routinely collecting measurements on large numbers of process variables, multivariate statistical methods for the analysis, monitoring and diagnosis of process operating performance have received increasing attention. Extensions of traditional univariate Shewhart, CUSUM and EWMA control charts to multivariate quality control situations are based on Hotelling's T 2 statist...

In the development of industrial processes there are situations where it is necessary to control or simultaneously monitor two or more quality variables of the production process. The problems of process monitoring where several related variables are studied can be controlled by means of multivariate control charts. The objective of this work is to describe the implementation of the control cha...

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Under estimated in-control parameters, the Phase II control chart performance is expected to vary among practitioners due to the use of different Phase I data sets. Accordingly, the typical measure of Phase II control chart performance, the average run length (ARL), becomes a random variable. In the literature, control charts with estimated parameters were assessed and the appropriate amounts o...

In a multi-sensor measurement or monitoring environment, p variables are measured simultaneously. The measured data are correlated and can be monitored to identify special causes of variation in order to establish control and to obtain reference samples to use as a basis in determining the control limits for future observations. One common method of constructing multivariate control charts is b...

Advanced automatic data acquisition is now widely adopted in manufacturing industries and it is common to monitor several correlated quality variables simultaneously. Most of multivariate quality control charts are effective in detecting out-of-control signals based upon an overall statistics in multivariate manufacturing processes. The main problem of such charts is that they can detect an out...

Excessive variation in a manufacturing process is one of the major causes of a high defect rate and poor product quality. Therefore, quick detection of changes, especially increases in process variability, is essential for quality control. In modern manufacturing environments, most of the quality characteristics that have to be closely monitored are multivariate by the nature of the application...

In some statistical process control applications, the quality of the product is characterized by the combination of both correlated variable and attributes quality characteristics. In this paper, we propose a novel control scheme based on the combination of two multi-layer perceptron neural networks for simultaneous monitoring of mean vector as well as the covariance matrix in multivariate-attr...

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...