Multivariate Statistical Process Control Charts: An Overview

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS

Control Charts to Enhance Quality

Control charts are important tools of statistical quality control to enhance quality. Quality improvement methods have been applied in the last few 10 years to fulfill the needs of consumers. The product has to retain the desired properties with the least possible defects, while maximizing profit. There are natural variations in production, but there are also assignable causes which do not form part of chance. Control charts are used to monitor production; in particular, their application may serve as an “early warning” index regarding potential “out-of-control” processes. In order to keep production under control, different control charts which are prepared for dissimilar cases are established incorporating upper and lower control limits. There are a number of control charts in use and are grouped mainly as control charts for variables and control charts for attributes. Points plotted on the charts may reveal certain patterns, which in turn allows the user to obtain specific information. Patterns showing deviations from normal behavior are raw material, machine setting or measuring method, human, and environmental factors, inadvertently affecting the quality of product. The information obtained from control charts assists the user to take corrective actions, hence opting for specified nominal values enhancing as such quality

Nonparametric Control Chart for the Range

A method is provided for detecting or predicting an undesired deviation in variability of at least one parameter being monitored, wherein the variation in the parameter is incrementally recorded. The method comprises establishing the number of subsets of a dataset that have a range of the difference between any two datapoints within the dataset, and computing a control chart for the range based thereon. The method accurately detects changes in variability in real time. The true distribution of the data is reflected, and the desired result is achieved without requiring an inordinate number of computations

Statistical process control techniques for the telecommunications systems manager

The purpose of this thesis is to provide personnel, who are undergoing Total Quality Leadership (TQL) implementation at their telecommunications-related command, an understanding of Statistical Process Controls (SPCs) and their potential application to telecommunications issues. Basic SPC tools common to most Total Quality programs are discussed. Advanced SPC methods including Analysis of Means (ANOM), Analysis of Variance (ANOVA), Weibull analysis and Taguchi Methods are also presented. Selected SPC training plans for both naval telecommunication commands and commercial telecommunication industry are examined. Finally, a case study of a telecommunications-related issue is provided to demonstrate an integrated approach to the use of SPCs.http://archive.org/details/statisticalproce1094538538Lieutenant, United States NavyApproved for public release; distribution is unlimited

Framework for development of data analysis protocols for ground water quality monitoring, A

Also issued as thesis (Ph. D.)--Colorado State University, 1992.June 1993.Includes bibliographical references (pages 75-85)

MEASURING & MONITORING Plant Populations

The root of the word monitoring means to warn, and an essential purpose of monitoring is to raise a warning flag that the current course of action is not working. Monitoring is a powerful tool for identifying problems in the early stages, before they become dramatically obvious or crises. If identified early, problems can be addressed while cost-effective solutions are still available. For example, an invasive species that threatens a rare plant population is much easier to control at the initial stages of invasion, compared to eradicating it once it is well established at a site. Monitoring is also critical for measuring management success. Good monitoring can demonstrate that the current management approach is working and provide evidence supporting the continuation of current management

A Quality Systems Economic-Risk Design Theoretical Framework

Quality systems, including control charts theory and sampling plans, have become essential tools to develop business processes. Since 1928, research has been conducted in developing the economic-risk designs for specific types of control charts or sampling plans. However, there has been no theoretical or applied research attempts to combine these related theories into a synthesized theoretical framework of quality systems economic-risk design. This research proposes to develop a theoretical framework of quality systems economic-risk design from qualitative research synthesis of the economic-risk design of sampling plan models and control charts models. This theoretical framework will be useful in guiding future research into economic risk quality systems design theory and application

Multivariate Statistical Process Control Charts: An Overview

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal

The viability of Weibull analysis of small samples in process manufacturing

This research deals with some Statistical Quality Control (SQC) methods, which are used in quality testing. It investigates the problem encountered with statistical process control (SPC) tools when small sample sizes are used. Small sample size testing is a new area of concern especially when using expensive (or large) products, which are produced in small batches (low volume production). Critical literature review and analysis of current technologies and methods in SPC with small samples testing failed to show a conformance with conventional SPC techniques, as the confidence limits for averages and standard deviation are too wide. Therefore, using such sizes will provide unsecured results with a lack in accuracy. The current research demonstrates such problems in manufacturing by using examples, in order to show the lack and the difficulties faced with conventional SPC tools (control charts). Weibull distribution has always shown a clear and acceptable prediction of failure and life behaviour with small sample size batches. Using such distribution enables the accuracy needed with small sample size to be obtained. With small sample control charts generate inaccurate confidence limits, which are low. On the contrary, Weibull theory suggests that using small samples enable achievement of accurate confidence limits. This research highlights these two aspects and explains their features in more depth. An outline of the overall problem and solution point out success of Weibull analysis when Weibull distribution is modified to overcome the problems encountered when small sample sizes are used. This work shows the viability of Weibull distribution to be used as a quality tool and construct new control charts, which will provide accurate result and detect nonconformance and variability with the use of small sample sizes. Therefore, the new proposed Weibull deduction control charts shows a successful replacement of the conventional control chart, and these new charts will compensate the errors in quality testing when using small size samples