In high-yields process monitoring, the Geometric distribution is particularly useful to control the cumulative counts of conforming (CCC) items. However, in some instances the number of defects on a nonconforming observation is also of important application and must be monitored. For the latter case, the use of the generalized Poisson distribution and hence simultaneously implementation of two ...

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...

a new method is developed for a fast identification of the stability situation of industrial processes. the proposed method includes two factor ratios of the control constants for the upper and lower control limits to process these constants. an indication ratio is then defined as the ratio of the maximum data range value to the difference between the maximum and average values for individual d...

A review of the literature on cause selecting charts (CSCs) in multistage processes is given, with a concentration on developments which have occurred since 1993. Model based control charts and multiple cause selecting charts (MCSCs) are reviewed. Several articles based on normally and non-normally distributed outgoing quality characteristics are analyzed and important issues such as economic d...

When a manufacturing process is subject to random shocks, detecting the changes in the process and adjusting an out-of-target process are two essential functions of process quality control. Traditional SPC techniques emphasize process change detection, but do not provide an explicit process adjustment method. This paper discusses a general sequential adjustment procedure based on Stochastic App...

The problem of detecting a shift in the percentile of a Birnbaum–Saunders population in a process monitoring situation is considered. For example, such problems may arise when the quality characteristic of interest is tensile strength or breaking stress. The parametric bootstrap method is used to develop a quality control chart for monitoring percentiles when process measurements have a Birnbau...

Control charts have been widely recognized as important and critical tools in system monitoring for detection of abnormal behavior and quality improvement. In particular, multivariate control charts have been effectively used when a process involves a number of correlated process variables. Most existing multivariate control charts were developed using the assumption of normally distributed pro...

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...