Stelios Psarakis

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.

Review of multinomial and multiattribute quality control charts

Attribute control charts are very useful nowadays for monitoring processes where the quality characteristics cannot be measured in a continuous scale, which may be manufacturing processes from industrial settings, health-care processes or processes from service industries and environments of non-manufacturing quality-improvement efforts. Many of the above cases, however, involve the monitoring of multiple attributes simultaneously, thus leading to the case of multinomial and multiattribute quality control methods, which are better than the simultaneous use of multiple uni-attribute methods. In this study, an attempt to review the research previously conducted on multiattribute quality control is made in order to help the interested researchers and practitioners get informed about the references on the relevant research in this field, regarding the design, performance and applications of multiattribute control charts. Copyright

Monitoring the Coefficient of Variation Using EWMA Charts

The coefficient of variation (CV) is a quality characteristic that has several applications in applied statistics and is receiving increasing attention in quality control. A few papers have proposed control charts that monitor this normalized measure of dispersion. This paper suggests a new method to monitor the CV by means of two one-sided EWMA charts of the coefficient of variation squared γ2. Tables are provided for the statistical properties of the EWMA-γ2 when the shift size is deterministic or unknown. An example illustrates the use of these charts on real data gathered from a metal sintering process.

Exploratory factor analysis for the Hirsch index, 17 h-type variants, and some traditional bibliometric indicators

The purpose of this article is to come up with a valid categorization and to examine the performance and properties of a wide range of h-type indices presented recently in the relevant literature. By exploratory factor analysis (EFA) we study the relationship between the h-index, its variants, and some standard bibliometric indicators of 26 physicists compiled from the Science Citation Index in the Web of Science.

Monitoring nonlinear profiles using support vector machines

In this work we focus on the use of SVMs for monitoring techniques applied to nonlinear profiles in the Statistical Process Control (SPC) framework. We develop a new methodology based on Functional Data Analysis for the construction of control limits for nonlinear profiles. In particular, we monitor the fitted curves themselves instead of monitoring the parameters of any model fitting the curves. The simplicity and effectiveness of the data analysis method has been tested against other statistical approaches using a standard data set in the process control literature.

Monitoring the Coefficient of Variation Using Control Charts with Run Rules

Abstract Monitoring the coefficient of variation (CV) is a successful approach to Statistical Process Control when the process mean and standard deviation are not constant. In recent years the CV has been investigated by many researchers as the monitored statistic for several control charts. Viewed under this perspective, this paper presents a new efficient method to monitor the CV by means of Run Rules (RR) type charts. Tables are provided to show the statistical run length properties of Shewhart- y , RR2,3 -y , RR3,4 -y and RR4,5 -y control charts for several combinations of in control CV values y0 , sample size n and shift size r. Indeed, comparative studies have been performed to find the best control chart for each combination. An example illustrates the use of these charts on real data gathered from a metal sintering process.

Monitoring the Coefficient of Variation Using a Variable Sampling Interval Control Chart

The coefficient of variation (CV) is a quality characteristic that has several applications in applied statistics and is receiving increasing attention in quality control. Few papers have proposed control charts that monitor this normalized measure of dispersion. In this paper, an adaptive Shewhart control chart implementing a variable sampling interval (VSI) strategy is proposed to monitor the CV. Tables are provided for the statistical properties of the VSI CV chart, and a comparison is performed with a Fixed Sampling Rate Shewhart chart for the CV. An example illustrates the use of these charts on real data gathered from a casting process. Copyright

The use of neural networks in statistical process control charts

Neural networks (NNs) are massively parallel computing mechanism emulating a human brain. It has been proved that they had a satisfactory performance when they were used for a wide variety of applications. In the recent years, the efficiencies that provided the NNs also began to be applied in statistical process control (SPC). SPC charts have become one of the most commonly used tools for monitoring process stability and variability in todays manufacturing environment. These tools are used to determine whether the process is statistically under or out of control but in some cases such as the presence of autocorrelation as well as the presence of a specific pattern in the data do not provide the possibility of correctly and quickly detecting and classifying the existing fault. These problems have led many researchers to propose alternative methods for monitoring processes such as the use of NNs. In this paper, we discuss issues concerning the combination of both tools. Specifically, we study the NNs for the detection and determination of mean and/or variance shifts as well as in pattern recognition in the SPC charts. Furthermore, the use of NNs when the data are correlated is discussed. Finally, the use of NNs in multivariate control charts is addressed. The networks architectures that were used for each case, the way of operation and the performance of the proposed NNs applications are pointed out. Copyright

Adaptive Control Charts: Recent Developments and Extensions

Statistical quality control is a very useful tool in the hands of business in order to improve their products quality. The control charts are the most widely used and the most effective tool of the statistical quality control. In this paper, the substantial recent developments in the design of the adaptive control charts, focusing on the univariate control charts, which allow some of their parameters to change during production, are presented. Copyright

Categorizing h-index variants

Utilizing the Hirsch index h and some of its variants for an exploratory factor analysis we discuss whether one of the most important Hirsch-type indices, namely the g-index, comprises information about not only the size of the productive core but also the impact of the papers in the core. We also study the effect of logarithmic and square-root transformation of the data utilized in the factor analysis. To demonstrate our approach we use a real data example analysing the citation records of 26 physicists compiled from the Web of Science. Copyright , Beech Tree Publishing.