A New Multivariate Control Chart for Monitoring Both Location and Dispersion

Chen, Gemai; Cheng, Smiley W.; Xie, Hansheng

doi:10.1081/sac-200047087

Cited by 80 publications

(71 citation statements)

References 2 publications

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“…Choi and colleagues have also provided a computer program for the estimation of control limits (Hawkins et al 107 ). Yeh et al 108 introduced a MEWMA which is designed to detect small changes in the variability of correlated multivariate quality characteristics, while Chen et al 109 proposed a MEWMA control chart that is capable of monitoring simultaneously the process mean vector and process covariance matrix. Runger et al 110 showed how the shift detection capability of the MEWMA can be significantly improved by transforming the original process variables to a lower-dimensional subspace through the use of the U -transformation.…”

Section: Mewma Chartsmentioning

confidence: 99%

Multivariate statistical process control charts: an overview

Bersimis

Psarakis

Panaretos

2006

Quality & Reliability Eng

512

313

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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 Shewharttype 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 least squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.

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Section: Mewma Chartsmentioning

confidence: 99%

Multivariate statistical process control charts: an overview

Bersimis

Psarakis

Panaretos

2006

Quality & Reliability Eng

512

313

View full text Add to dashboard Cite

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“…when the process is in-control, the expected value and covariance matrix for^ k are given as follows [21]: In this method, we extend the proposed method by Chen et al [30] for simultaneous monitoring of mean vector and covariance matrix of the regression parameters estimators in the multivariate multiple linear regression pro les. De ne: z k = (^ k ) + (1 ) z k 1 ; k = 1; 2; :::;…”

Section: Proposed Control Chartsmentioning

confidence: 99%

“…Since M k is the maximum jC k j and jS k j, which are based on two Multivariate Exponentially Weighted Moving Average (MEWMA) statistics, it is natural to name the control chart, based on M k , Max{MEWMA control chart Chen et al [30]. A large value of M k means that the process mean vector and/or covariance matrix has shifted away from and , respectively.…”

Section: Proposed Control Chartsmentioning

confidence: 99%

“…The simultaneous monitoring of multivariate process mean vector and covariance matrix has also been addressed by several authors. Chen et al [30] proposed a control scheme called Max-EWMA for monitoring both mean and variance simultaneously. They performed a comparison analysis and found that their proposed control chart performed better than the combination of chi-square and jSj control charts when small shifts occurred in the process parameters.…”

Section: Introductionmentioning

confidence: 99%

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Maximum Multivariate Exponentially Weighted Moving Average and Maximum Multivariate Cumulative Sum Control Charts for Simultaneous Monitoring of Mean and Variability of Multivariate Multiple Linear Regression Profiles

Ghashghaei

Amiri

2017

Scientia Iranica

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Abstract. In some applications, quality of product or performance of a process is described by some functional relationships among some variables known as multivariate linear pro le in the literature. In this paper, we propose Max-MEWMA and Max-MCUSUM control charts for simultaneous monitoring of mean vector and covariance matrix in multivariate multiple linear regression pro les in Phase II. The proposed control charts also have the ability to diagnose whether the location or variation of the process is responsible for out-of-control signal. The performance of the proposed control charts is compared with that of the existing method through Monte-Carlo simulations. Finally, the applicability of the proposed control charts is illustrated using a real case of calibration application in the automotive industry.

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“…The results are not compelling, once the proposed chart is slow in signaling out-of-control conditions. Chen et al (2005) proposed a single EWMA chart to control both, the mean vector and the covariance matrix. Their chart is more efficient than the joint 2 T and S in signaling small changes in the process.…”

Section: Introductionmentioning

confidence: 99%

Monitoring bivariate process

2009

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The 2 T chart and the generalized variance |S| chart are the usual tools for monitoring the mean vector and the covariance matrix of multivariate processes. The main drawback of these charts is the difficulty to obtain and to interpret the values of their monitoring statistics. In this paper, we study control charts for monitoring bivariate processes that only requires the computation of sample means (the ZMAX chart) for monitoring the mean vector, sample variances (the VMAX chart) for monitoring the covariance matrix, or both sample means and sample variances (the MCMAX chart) in the case of the joint control of the mean vector and the covariance matrix.Keywords: control charts; bivariate processes; mean vector; covariance matrix. ResumoOs gráficos de 2 T e da variância amostral generalizada |S| são as ferramentas usualmente utilizadas no monitoramento do vetor de médias e da matriz de covariâncias de processos multivariados. A principal desvantagem desses gráficos é a dificuldade em obter e interpretar os valores de suas estatísticas de monitoramento. Neste artigo, estudam-se gráficos de controle para o monitoramento de processos bivariados que necessitam somente do cálculo de médias amostrais (gráfico ZMAX) para o monitoramento do vetor de médias, ou das variâncias amostrais (gráfico VMAX) para o monitoramento da matriz de covariâncias, ou então das médias e variâncias amostrais (gráfico MCMAX) para o caso do monitoramento conjunto do vetor de médias e da matriz de covariâncias.Palavras-chave: gráficos de controle; processos bivariados; vetor de médias; matriz de covariâncias.

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A New Multivariate Control Chart for Monitoring Both Location and Dispersion

Cited by 80 publications

References 2 publications

Multivariate statistical process control charts: an overview

Multivariate statistical process control charts: an overview

Maximum Multivariate Exponentially Weighted Moving Average and Maximum Multivariate Cumulative Sum Control Charts for Simultaneous Monitoring of Mean and Variability of Multivariate Multiple Linear Regression Profiles

Monitoring bivariate process

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