• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.429-436
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• 2009

This paper considers the integrated process control procedure for detecting special causes in an IMA(1,1) process that is being adjusted automatically after each observation using a minimum mean squared error adjustment policy. When the control chart signals after the occurrence of a special cause, the special cause will be detected and eliminated from the process by the rectifying action. However, when the elimination of the special cause costs high or is not practically possible, an alternative action is to readjust the process with appropriately modified adjustment scheme. In this paper, we propose the readjustment procedure after having a true signal, and show that the use of the readjustment can reduce the deviation of a process from the target.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.11-24
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• 2009

The process controller in the adjustment procedure is often used effectively to control the process level close to target when noise is present and unremovable. Examples of the robust controller are single EWMA controller and double EWMA controller. Double EWMA controller is designed to reduce the offset of the process deviation, which single EWMA can not eliminate. In this paper, the two controllers are modified by taking EWMA of the original controller to reduce the adjustment variance, which may become excessively large when the two given controllers are implemented. It is shown that the EWMA modification of the given controllers is successful in reducing the adjustment variance, while the mean squared deviation increases slightly.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1123-1135
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• 2011

This paper considers the multivariate integrated process control procedure for detecting special causes in a multivariate IMA(1, 1) process. When the multivariate control chart signals, the special cause will be detected and eliminated from the process. However, when the elimination of the special cause costs high or is not practically possible, an alternative action is to readjust the process with approximately modified adjustment scheme. In this paper, we propose the readjustment procedure after having a true signal, and show that the use of the readjustment can reduce the deviation of a process from the target.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.357-365
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• 2010

This paper considers the integrated process control procedure for detecting special causes in an IMA(1,1) noise process that is being adjusted using a minimum mean squared error adjustment. As a SPC procedure, we use a GLR chart for detecting special causes whose effects are the sustained shift or the sustained drift in the process mean, and the sustained shift in the process variance. For the design of the GLR chart, we derive expressions for the control limit which accurately satisfies the given in-control ARL.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1275-1283
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• 2013

This paper considers the multivariate integrated process control procedure for detecting special causes in a multivariate IMA(1, 1) process. When the multivariate control chart signals, the special cause will be detected and eliminated from the process. However, when the elimination of the special cause costs high or is not practically possible, an alternative action is to readjust the process with approximately modified adjustment scheme. In this paper, we estimate parameters in the readjustment procedure after having a true signal in the multivariate integrated process control.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.247-264
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• 2008

This paper considers the integrated process control procedure for detecting special causes in an ARIMA(0,1,1) process that is being adjusted automatically after each observation using a minimum mean squared error adjustment policy. It is assumed that a special cause can change the process mean and the process variance. We derive expressions for the process deviation from target for a variety of different process parameter changes, and introduce a control chart, based on the generalized likelihood ratio, for detecting special causes. We also propose the integrated process control scheme bases on the future loss. The future loss denotes the cost that will be incurred in a process remaining interval from a true out-of-control signal.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.2
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• pp.191-200
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• 2011

A PHE (Process Heat Exchanger) is a key component of nuclear hydrogen system for massive production of hydrogen; the PHE transfers the very high temperature heat ($950^{\circ}C$) generated from the VHTR (Very High Temperature Reactor) to a chemical reaction. The Korea Atomic Energy Research Institute developed a small-scale gas loop for testing the performance of VHTR components and manufactured a modified PHE prototype for carrying out the testing in the gas loop. In this study, as a part of the evaluation of the high-temperature structural integrity of the modified PHE prototype which is scheduled to test in the gas loop, we carried out high-temperature structural analysis modeling, macroscopic thermal and structural analysis of the PHE prototype under the gas loop test conditions as a precedent study before carrying out the performance test in the gas loop. The results obtained in this study will be used to design the performance test setup for the modified PHE prototype.

• Journal of Korea Society of Industrial Information Systems
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• v.5 no.4
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• pp.55-59
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• 2000

In start up process control, it may be necessary to use appropriate scheme in monitoring processes with individual observations. In these situation individual observations are periodically drawn from the process. In this paper, using modifying statistics with individual measurement, we suggest a simple technique which operating control chart for monitoring the process. And compare individual observation control procedures that are X, an exponentially weighted moving(EWM), adjusted EWM and adjusted MCEWM charts. And estimate the ARL to detection of shifts in the process mean and standard deviation using simulation.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.841-852
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• 2010

An integrated process control (IPC) procedure is a scheme which simultaneously applies the engineering control procedure (EPC) and statistical control procedure (SPC) techniques to reduce the variation of a process. In the IPC procedure, the observed deviations are monitored during the process where adjustments are repeatedly done by its controller. Because the effects of the noise, the special cause, and the adjustment are mixed, the use and properties of the SPC procedure for the out-of-control process are complicated. This paper considers efficiency of EWMA charts for detecting special causes in an ARMA(1,1) noise model with a minimum mean squared error adjustment policy. And we propose the readjustment procedure after having a true signal. This procedure can be considered when the elimination of the special cause is not practically possible.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.4
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• pp.53-58
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• 2002

This paper presents a adjustment synthetic control chart that is an integration of the Shewhart X chart and the conforming run length(CRL) chart. The application of the adjustment synthetic control chart my therefore substantially enhance the effectiveness process control for manufacturing. In the synthetic control chart, denotes the average number of the X sample required to detect a process shift. The synthetic control chart outperforms the EWM chart and the X chart when σ is greater than 0.75σ. And the X-CRL charts suggested above evaluate using the conditional probability.