Robust Multivariate Control Charts to Detect Small Shifts in Mean

Using CUSUM Charts To Detect Small Process Shifts

In order to monitor a process, quality practitioners frequently use Shewhart control charts (e.g. X –R, P-charts, etc.), so named after the pioneering work of Dr. Walter Shewhart. It can be shown that if there are sharp, intermittent changes to a process, these types of charts are highly effective. However, if one is interested in a small, sustained shift in a process, other types of control ch...

Shewhart Control Charts to Detect Mean and Standard Deviation Shifts Based on Grouped Data

We propose a Shewhart control chart based on gauging theoretically continuous observations into multiple groups. This chart is designed to monitor the process mean and standard deviation for deviations from stability. By assuming an underlying normal distribution, we derive the optimal grouping criteria that maximizes the expected statistical information available in a sample. Control charts ba...

On the non-parametric multivariate control charts in fuzzy environment

Multivariate control chats are generally used in situations where the simultaneous monitoring or control of two or more related quality characteristics is necessary. In most processes in the real world, distribution of the process characteristics are unknown or at least non-normal, so the non-parametric or distribution-free charts are desirable. Most non-parametric statistical process-control t...

On multivariate control charts

Industrial production requires multivariate control charts to enable monitoring of several components. Recently there has been an increased interest also in other areas such as detection of bioterrorism, spatial surveillance and transaction strategies in finance. In the literature, several types of multivariate counterparts to the univariate Shewhart, EWMA and CUSUM methods have been proposed. ...

A New Control Chart to Monitor Mean Shifts of Bi-Variate Quality Control Processes