• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.937-945
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• 2017

The purpose of using a control chart is to detect any change that occurs in the process. When control charts are used to monitor processes, we want to identify this changes as quickly as possible. Many problems in quality control involve a vector of observations of several characteristics rather than a single characteristic. Multivariate CUSUM or EWMA charts have been developed to address the problem of monitoring covariance matrix or the joint monitoring of mean vector and covariance matrix. However, control charts tend to work poorly when we use the highly correlatted variables. In order to overcome it, Hawkins (1991) proposed the use of regression adjustment variables. In this paper, to monitor covariance matrix, we investigate the performance of MEWMA-type control charts with and without the use of regression adjusted variables.

• Journal of Korean Society for Quality Management
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• v.26 no.1
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• pp.126-142
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• 1998

Traditional statistical process control(SPC) assumes that consective observations from a process are independent. In industrial practice, however, observations are ofter serially correlated. A common a, pp.oach to building control charts for autocorrelatd data is to a, pp.y classical SPC to the residuals from a time series model fitted. Unfortunately, one cannot completely escape the effects of autocorrelation by using charts based on residuals of time series model. For the detection of variance change in the process we propose a CUSUM of squares control chart which does not require the model identification. The proposed CUSUM of squares chart and the conventional control charts are compared by a Monte Carlo simulation. It is shown that the CUSUM of squares chart is more effective in the presence of dependency in the processes.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.773-782
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• 2007

The statistical process control charts are very extensively used for monitoring of process mean, deviation, defect rate or reject rate. In this paper we consider a control chart to monitor the process reject rate in the high yield process, which is based on the observed cumulative probability of the number of items inspected until r defective items are observed. We first propose selection of the optimal value of r in the CPC-r charts, and also consider the usefulness of the chart in high yield process such as semiconductor or TFT-LCD manufacturing process.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.807-814
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• 2012

We know that the exponentially weighted moving average (EWMA) control charts are sensitive to detecting relatively small shifts. Multivariate EWMA control charts are considered for monitoring of variance-covariance matrix when the distribution of process variables is multivariate normal. The performances of the proposed EWMA control charts are evaluated in term of average run length (ARL). The performance is investigated in three types of shifts in the variance-covariance matrix, that is, the variances, covariances, and variances and covariances are changed respectively. Numerical results show that all multivariate EWMA control charts considered in this paper are effective in detecting several kinds of shifts in the variance-covariance matrix.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.999-1008
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• 2010

The objective of this paper is to develop variable sampling interval multivariate control charts that can offer significant performance improvements compared to standard fixed sampling rate multivariate control charts. Most research on multivariate control charts has concentrated on the problem of monitoring the process mean, but here we consider the problem of simultaneously monitoring both the mean and variability of the process.

• Journal of Korean Society for Quality Management
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• v.28 no.2
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• pp.39-56
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• 2000

${\overline{X}}$ control chart has proven to be an effective tool to improve the product quality. Shewhart charts assume that the observations are independent and normally distributed. Under the presence of positive autocorrelation and severe skewness, the control limits are not accurate because assumptions are violated- Autocorrelation in process measurements results in frequent false alarms when standard control chats are applied in process monitoring. In this paper, Threshold Bootstrap and Moving Block Bootstrap are used for constructing a confidence interval of correlated observations. Monte Carlo simulation studies are conducted to compare the performance of the bootstrap methods and that of standard method for constructing control charts under several conditions.

• Journal of Integrative Natural Science
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• v.5 no.4
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• pp.246-252
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• 2012

Multivariate cumulative sum (CUSUM) control charts for simultaneously monitoring both means and variances under multivariate normal process are investigated. Performances of multivariate CUSUM schemes are evaluated for matched fixed sampling interval (FSI) and variable sampling interval (VSI) features in terms of average time to signal (ATS), average number of samples to signal (ANSS). Multivariate Shewhart charts are also considered to compare the properties of multivariate CUSUM charts. Numerical results show that presented CUSUM charts are more efficient than the corresponding Shewhart chart for small or moderate shifts and VSI feature with two sampling intervals is more efficient than FSI feature. When small changes in the production process have occurred, CUSUM chart with small reference values will be recommended in terms of the time to signal.

• Journal of the Korea Society of Computer and Information
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• v.12 no.4
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• pp.201-207
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• 2007

In some manufacturing industries, there are many situation in which the simultaneous monitoring or control of two or more related quality characteristics is necessary. However, monitoring these two or more related quality characteristics independently can be very misleading. When several characteristics of manufactured component are to be monitored simultaneously, multivariate $x^2$ or $T^2$ control chart can be used. In this paper, establishing a early warning system(EWS) using multivariate control charts to analyze early out-of-control signals in melting process with many quality characteristics was presented. This module which we developed to control several characteristics improved efficiency and effectiveness of process control in the melting process.

• Proceedings of the Korean Society for Quality Management Conference
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• 2006.04a
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• pp.330-333
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• 2006

Many practical problems of quality control in service management are derived from the use of ordinal variables. Ordered linguistic variables differ from measurement variables. This paper presents a new control chart of a production process based on ordinal variables.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.10a
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• pp.31-39
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• 1993

This paper considers a procedure for the economic design of a cumulative score(CUSCORE) control chart and more sensitive than X-type control chart for small shift to control the mean of a process with a exponentially distributed quality characteristic. An expected loss - cost model as a function of design variables(sample size, sampling interval, scoring limit and decision limit) is derived. Direct search techniques are used to optimize the model subject to ARL in control. Numerical examples and sensitivity analysis of the model are presented. For selected values of situation parameters a comparison study with CUSUM charts is given. CUSCORE control charts compare favourably with CUSUM charts in cost for speedy production process. The proposed control chart can be directly applied for controlling the lifetime characteristics.