Author
Kevin L. Busby
Bio: Kevin L. Busby is an academic researcher from University of Alabama. The author has contributed to research in topics: Statistical process control & Multivariate statistics. The author has an hindex of 1, co-authored 1 publications receiving 86 citations.
Papers
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TL;DR: In this paper, a model demonstrates that the performance of multivariate control charting methods based on measured covariates is not directionally invariant to shifts in the mean vector of the underlying process variables.
Abstract: A model demonstrates that the performance of multivariate control charting methods based on measured covariates is not directionally invariant to shifts in the mean vector of the underlying process variables. This is the case even if it is directionally..
92 citations
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TL;DR: In this paper, the basic procedures for the implementation of multivariate statistical process control via control charting are discussed, and the most significant methods for the interpretation of an out-of-control signal are described.
Abstract: 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.
506 citations
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TL;DR: In this paper, the basic procedures for the implementation of multivariate statistical process control via control charting are discussed, and the most significant methods for the interpretation of an out-of-control signal are described.
Abstract: 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.
483 citations
TL;DR: Significant measurement error often exists in quality control applications as mentioned in this paper, which is known to result in reduced power to detect a given change in the mean or variance of a quality chara...
Abstract: Significant measurement error often exists in quality control applications. Measurement error is known to result in reduced power to detect a given change in the mean or variance of a quality chara...
141 citations
TL;DR: The aim of this paper is to present, to apply and to evaluate control charts that are designed to account for autocorrelation.
Abstract: The inference about the statistical properties of quality control methodologies is based on the assumptions of normality and independence. In real industrial environments though process data is often correlated or exhibits some serial dependence affecting the efficiency of Statistical Process Control (SPC) methodologies. New technology gives managers the option of using more sophisticated SPC models which more accurately reflect the process being monitored, by relaxing some of the assumptions. The aim of this paper is to present, to apply and to evaluate control charts that are designed to account for autocorrelation.
103 citations
TL;DR: This work builds-up the samples with non-neighbouring items, according to the time they were produced, to counteract the undesired effect of autocorrelation.
Abstract: Measurement error and autocorrelation often exist in quality control applications. Both have an adverse effect on the X¯ chart's performance. To counteract the undesired effect of autocorrelation, we build-up the samples with non-neighbouring items, according to the time they were produced. To counteract the undesired effect of measurement error, we measure the quality characteristic of each item of the sample several times. The chart's performance is assessed when multiple measurements are applied and the samples are built by taking one item from the production line and skipping one, two or more before selecting the next.
90 citations