• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.67-79
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• 2011

The statistical process control (SPC) assumes that observations follow the particular statistical distribution and they are independent to each other. However, the time-series data do not always follow the particular distribution, and most of cases are autocorrelated, therefore, it has limit to adopt the general SPC in tim series process. In this study, we propose a MPBC (Model Parameter Based Control-chart) method for fault detection in time-series processes. The MPBC builds up the process as a time-series model, and it can determine the faults by detecting changes parameters in the model. The process we analyze in the study assumes that the data follow the ARMA (p,q) model. The MPBC estimates model parameters using RLS (Recursive Least Square), and $K^2$-control chart is used for detecting out-of control process. The results of simulations support the idea that our proposed method performs better in time-series process.

• Journal of the Korea Society for Simulation
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• v.16 no.2
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• pp.37-44
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• 2007

EWMA is one of the most popular controller method used in Run-to-Run control system for semiconductor manufacturing. The value of the control parameter in EWMA has major effect on the result. Therefore, it is important to use control parameter value fitting for the process state. When the process is unstable, it is more efficient to change EWMA control parameter dynamically to compensate for the changing process state than using fixed control parameter. In this paper, we review previous studies using dynamic EWMA control parameter and propose a new algorithm complementing the weaknesses of the previous studies. The performance of the proposed algorithm is validated using simulation.

• Communications for Statistical Applications and Methods
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• v.5 no.2
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• pp.361-371
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• 1998

공정수정을 목적으로 하는 자동공정관리는 품질특성 치를 목표치에 가능한한 일치하도록 공정을 조정하고 관리한다. 본 논문에서는 자동공정관리에서 정의되는 비용함수를 최소화하는 관리모수를 선정하는 방법을 제시한다. 이를 위하여 Lee(1997)가 제안한 비용함수의 추정식을 사용하며, Kramer(1989)와 Box와 framer(1992)에 의해 연구된 방법과 비교한 결과 본 논문에서 제안된 방법이 계산하기 간편하면서도 충분히 좋은 정확성을 갖음을 알 수 있었다.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1487-1498
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• 2016

In monitoring a process, in-control process parameters must be estimated from the Phase I data. When we design the control chart based on the estimated process parameters, the control limits are usually chosen to satisfy a specific in-control average run length (ARL). However, as the run length distribution is skewed when the process is either in-control or out-of-control, the median run length (MRL) can be used as alternative measure instead of the ARL. In this paper, we evaluate the performance of Shewhart $\bar{X}$ chart with estimated parameters in terms of the average of median run length (AMRL) and the standard deviation of MRL (SDMRL) metrics. In simualtion study, the grand sample mean is used as a process mean estimator, and several competing process standard deviation estimators are used to evaluate the in-control performance for various amounts of Phase I data.

• The Korean Journal of Applied Statistics
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• v.35 no.6
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• pp.725-737
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• 2022

In the standard assumption of statistical process monitoring (SPM) under consideration, the in-control region of the control parameter of quality characteristic consists of a single point. However, if small deviations from the ideal situation may not be of practical importance, the parametric space can consist of three regions: In-control, indifference, and out-of-control. In this paper, we propose two exponentially weighted moving average (EWMA) charting procedures applicable to the situation with three parameter regions, and compare the efficiency of the proposed procedures with the Shewhart chart and the cumulative sum (CUSUM) chart.

• Proceedings of the Safety Management and Science Conference
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• 2013.04a
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• pp.593-607
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• 2013

관리도를 사용하여 공정평균, 공정산포 등 여러 가지 공정모수를 관리할 수 있다. 그러나 공정모수의 미세 변동을 효과적으로 관리할 수 있는 기법체계는 아직 미완이다. 식스시그마 공정관리 등 정밀공정관리를 위해서는 미세 공정평균과 공정산포관리가 전제되어야한다. 특히 높은 수준의 공정능력을 유지하기위해서는 공정산포관리가 선결과제이다. 본 본문에서는 공정평균과 공정불량률, 공정산포의 미세변동을 효과적으로 관리할 수 있는 기술체계의 연구동향을 분석하고 미세공정산포관리를 위한 대안을 제시하고자 한다.

• The Korean Journal of Applied Statistics
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• v.12 no.1
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• pp.45-65
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• 1999

공정능력지구 $C_{pk}$는 제조공정이 제품을 제대로 생산하고 있는지를 평가하기 위하여 널리 사용되고 있는 측도이다. 최근까지 공정능력지수 $C_{pk}$에 관한 추정문제들이 만히 연구되었는 바, 대부분의 이러한 연구들은 공정분포가 정규분포임을 가정하였다. 하지만 실제 품질관리 현장의 공정으로부터 얻어지는 특성치들이 정규분포를 따르지 않는 경우가 많이 발생하며, 이를 감지하기가 어려울 수 있다. 따라서 본 논문에서는 공정능력지수 $C_{pk}$에 대한 바람직한 구간추정 방법을 제안하기 위하여 6가지 형태의 비모수적인 붓스트랩 신뢰구간을 설정하고 세 가지 공정분포에 대하여 다양하고 포괄적인 모의실험을 통하여 그 효율성에 관하여 비교연구를 하였다.

• 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.27 no.5
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• pp.787-796
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• 2014

Situations where sample size is not constant are common when monitoring a process with Poisson count data. In this paper, we propose a generalized likelihood ratio(GLR) control chart to detect shifts in the Poisson rate when the sample size varies. The performance of the proposed GLR chart is compared with the performance of several cumulative sum(CUSUM) type charts. It is shown that the overall performance of the GLR chart is comparable with CUSUM type charts and is significantly better in cases where the actual value of the shift is different from the pre-specified value in CUSUM type charts.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.339-348
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• 2017

One of the major issues of statistical process control for variables data is monitoring both the mean and the standard deviation. The traditional approach to monitor these parameters is to simultaneously use two seperate control charts. However there have been some works on developing a single chart using a single plotting statistic for joint monitoring, and it is claimed that they are simpler and may be more appealing than the traditonal one from a practical point of view. When using these control charts for variables data, estimating in-control parameters and checking the normality assumption are the very important step. Nonparametric Shewhart-Lepage chart, proposed by Mukherjee and Chakraborti (2012), is an attractive option, because this chart uses only a single control statistic, and does not require the in-control parameters and the underlying continuous distribution. In this paper, we introduce the Shewhart-Lepage chart, and propose the design procedure to find the optimal diagnosis limits when the location and the scale parameters change simultaneously. We also compare the efficiency of the proposed method with that of Mukherjee and Chakraborti (2012).