• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.523-530
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• 2016

Most classical control charts assume that processes are serially independent, and autocorrelation among variables makes them unreliable. To address this issue, a variety of statistical approaches has been employed to estimate the serial structure of the process. In this paper, we propose a multioutput least squares support vector regression and apply it to construct a residual multivariate cumulative sum control chart for detecting changes in the process mean vector. Numerical studies demonstrate that the proposed multioutput least squares support vector regression based control chart provides more satisfying results in detecting small shifts in the process mean vector.

• Journal of the Korean Data and Information Science Society
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• v.13 no.1
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• pp.105-112
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• 2002

In this paper we establish bivariate exponentially weighted moving average (EWMA) control charts for autocorrelated processes using residual vectors. We first derive the residual vectors, their expectation, variance-covariance matrix, then evaluate the control chart based on the average run length (ARL).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.2
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• pp.50-56
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• 2002

In this study, we establish bootstrap control limits for EWMA chart by applying the bootstrap method, called resampling, which could not demand assumptions about pre-distribution when the process is skewed and/or the normality assumption is doubt. The results obtained in this study are summarized as follows : bootstrap EWMA control chart is developed for applying bootstrap method to EWMA chart, which is more sensitive to small shifts of process. With the purpose of eliminating a skewness of the resampling distribution, the bootstrap control limits are established by using a modified residual, and its performance is analyzed by ARL. It is shown that the bootstrap EWMA control chart developed in this study includes the properties of standard EWMA control chart that is sensitive to a small shift, and detects process in out of control more quickly than standard EWMA chart.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.3
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• pp.50-60
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• 1998

This paper is concerned with the design of two residual control charts for real-time monitoring of the continuous flow processes. Two different control charts are designed under the situation that observations are correlated each other. Kalman-Filter based model estimation is employed when the process model is known. A black-box approach, based on Back-Propagation Neural Network, is also applied for the design of control chart when there is no prior information of process model. Performance of the designed control charts and traditional control charts is evaluated. Average run length(ARL) is adopted as a criterion for comparison. Experimental results show that the designed control chart using the Neural Network's modeling has shorter ARL than that of the other control charts when process mean is shifted. This means that the designed control chart detects the out-of-control state of the process faster than the others. The designed control chart using the Kalman-Filter based model estimation also has better performance than traditional control chart when process is out-of-control state.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.365-370
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• 2002

In this study, according to MARMA(1,0) model which was suggested by Seppala, in case of existing autocorrelation in X control chart and EWMA control chart, the standard method and the non-parametric bootstrap method were compared and analysed using the bootstrap method which use the resampling prediction residual.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.3
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• pp.289-296
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• 2001

This paper proposes multivariate control chart for autocorrelated data which are common in chemical and process industries and lead to increase in the number of false alarms when conventional control charts are applied. The effect of autocorrelated data is modeled as a vector autoregressive process, and canonical analysis is used to reduce the dimensionality of the data set and find the canonical variables that explain as much of the data variation as possible. Charting statistics are constructed based on the residual vectors from the canonical variables which are uncorrelated over time, and therefore the control charts for these statistics can attenuate the autocorrelation in the process data. The charting procedures are illustrated with a numerical example and Monte Carlo simulation is conducted to investigate the performances of the proposed control charts.

• Journal of Korean Society for Quality Management
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• v.20 no.1
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• pp.1-10
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• 1992

When we compare different control charting schemes, the average run length of each control chart is usually used. The use of the average run length implies that there is unbounded number of samples or observations. The regression recursive residuals, however, have been applied to the cumulative sum chart to detect whether the mean or variance changes. To implement choice of decision interval, we calculate the probability that certain fixed number of control statistics stay in the in-control state. This probability can be used as the significance level of a test for detecting the change in the residual mean or variance of the data with a finite number of observations.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.289-301
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• 2003

In this paper, we propose Shewhart control chart and EWMA control chart using the autocorrelated data which are common in chemical and process industries and lead to increase the number of false alarms when conventional control charts are applied. The effect of autocorrelated data is modeled as a autoregressive process, and canonical analysis is used to reduce the dimensionality of the data set and find the canonical variables that explain as much of the data variation as possible. Charting statistics are constructed based on the residual vectors from the canonical variables which are uncorrelated over time, and the control charts for these statistics can attenuate the autocorrelation in the process data. The charting procedures are illustrated with a numerical example and simulation is conducted to investigate the performances of the proposed control charts.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.11-14
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• 1996

Performance of the group cumulative sum(CUSUM) control scheme using multiple univariate CUSUM charts is more sensitive to the change of quality control(QC) characteristics than the control chart scheme based on the Hotelling statistics. We examine three group charts for multivariate normal data sets simulated with various correlation structures and shift directions in the mean vector. These group schemes apply the orginal measurement vectors, the scaled residual vectors from the regression of each variable on all others and the principal component vectors respectively to calculating the CUSUM statistics. They are also compared to the multivariate QC charts based on the Hotelling statistic by estimating average run lengths, coefficients of variation of run length and ranks in signaling order. On the basis of simulation results, we suggest a control chart scheme appropriate for specific quality control environment.

• Management Science and Financial Engineering
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• v.5 no.1
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• pp.51-71
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• 1999

Performance of the group cumulative sum (CUSUM) control scheme using multiple univariate CUSUM charts is more sensitive to the change of quality control (QC) characteristics than the control chart schemes based on the Hotelling statistic We vexamine three group charts for multivariate normal data sets simulated with various correlation structures and shift directions in the mean vector. These group schemes apply the original measurement vectors, the scaled residual vectors from the re-gression of each variable on all others and the principal component vectors respectively to calculat-ing the CUSUM statistics. They are also compared to the multivariate QC charts based on the Ho-telling statistic by estimating average run lengths, coefficients of variation of run length and ranks in signaling order. On the basis of simulation results, we suggest a control chart scheme appropriate for specific quality control environment.