• 산업경영시스템학회지
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• 제25권6호
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• pp.1-6
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• 2002

Most of process data control have been designed under the assumption that there are independence between observed data. However, it has been difficult to apply the traditional method to realtime data because they are autocorrelated, and they are not normally distributed. And the more, they have fluctuating means. Already the control method for these data was proposed by Markov Modulated Gaussian Process. Therefore, this study take into account MMGP's traits especially for the MMGP's autocorrelation.

• 산업경영시스템학회지
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• 제35권4호
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• pp.118-125
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• 2012

When monitoring an instrumental process, one often collects a host of data such as characteristic signals sent by a sensor in short time intervals. Characteristic data of short time intervals tend to be autocorrelated. In the instrumental processes often the practice of adjusting the setting value simply based on the previous one, so-called 'adjacent point operation', becomes more critical, since in the short run the deviations are harder to detect and in the long run they have amplified consequences. Stochastic modelling using ARIMA or AR models are not readily usable here. Due to the difficulty of dealing with autocorrelated data conventional practice is resorting to choosing the time interval where autocorrelation is weak enough then to using I-MR control chart to judge the process stability. In the autocorrelated instrumental processes it appears that using the Shewhart chart and the time interval data where autocorrelation is relatively not existent turns out to be a rather convenient and very useful practice to determine the process stability. However in the autocorrelated instrumental processes we intend to show that one would presumably do better using the EWMA control chart rather than just using the Shewhart chart along with some arbitrarily intervalled data, since the former is more sensitive to shifts given appropriate weights.

• 한국시뮬레이션학회논문지
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• 제25권3호
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• pp.117-123
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• 2016

본 연구는 다중서버 대기행렬시스템의 관측이 제한되어 있는 경우에 시스템 내부 행태를 추론하는 데에 그 목적이 있다. 대기행렬시스템 분석에 있어 도착 및 서비스시간에 자기상관성이 존재하면 이론적으로 모형화하기가 매우 복잡하고 어렵다. 이에 따라 다양한 분석 기법 및 확률과정 모형들이 개발되었다. 본 논문에서는 외부 관측치에 존재하는 자기상관성과 내부 행태를 관측하기 어려운 경우에 대한 추론 방법을 소개한다. 선행연구의 가정을 완화하여 추론 방법을 제시하고 그에 대한 보조정리 및 정리를 제시한다. 제시된 비모수적 방법을 적용하면 서비스시간에 자기상관성이 존재하더라도 외부 관측치만을 사용하여 다중서버 대기행렬의 내부 행태를 추론할 수 있다. 주요 내부 추론 결과로는 대기시간과 서비스시간을 사용하였다. 또한 제시된 방법의 타당성 검증을 위해 실험 결과를 제시하였다.

• 응용통계연구
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• 제37권2호
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• pp.191-207
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• 2024

자기상관 공정에서 이상상태를 빠르게 탐지하는 절차에 대해 많은 연구가 진행되어 왔다. 가장 전통적인 절차는 관측된 데이터에 대해 적합한 시계열 모형에서 계산된 잔차를 이용하는 잔차 관리도이다. 그러나 최근에는 통계적 학습 방법을 이용하여 자기상관 공정을 모니터링하는 절차가 많이 제안되었다. 이 논문에서는 딥러닝에 기반한 비지도 학습 방법인 LSTM Autoencoder의 잠재 벡터를 이용한 모니터링 절차를 제안하고, 이를 모의실험을 통해 LSTM Autoencoder의 복원 오차를 이용한 절차, RNN 분류 모니터링 절차, 그리고 잔차 관리도 절차의 성능과 비교하였다. 모의실험 결과, 제안된 절차와 RNN 분류 모니터링 절차의 성능은 유사하지만, 제안된 절차는 학습에 이상상태의 데이터가 필요하지 않기 때문에 이상상태의 데이터를 충분하게 확보할 수 없는 공정에 유용하게 적용할 수 있다는 장점이 있다.

• Communications for Statistical Applications and Methods
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• 제14권1호
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• pp.155-167
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• 2007

Knowing the time of the process change could lead to quicker identification of the responsible special cause and less process down time, and it could help to reduce the probability of incorrectly identifying the special cause. In this paper, we propose the maximum likelihood estimator (MLE) for the process change point when a control chart is used in monitoring the mean of a process in which the observations can be modeled as an AR(1) process plus an additional random error. The performance of the proposed MLE is compared to the performance of the built-in estimator when they are used in EWMA charts based on the residuals. The results show that the proposed MLE provides good performance in terms of both accuracy and precision of the estimator.

• Journal of the Korean Data and Information Science Society
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• 제27권2호
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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.

• 품질경영학회지
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• 제36권4호
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• pp.87-92
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• 2008

It is common to use CUSUM charts for detecting small level shifts in processes control, in which reference value(k) and decision interval(h) are the design parameters to be determined. To control process with autocorrelation, CUSUM charts could be applied to residuals obtained from fitting ARIMA models. However, constant level shifts in processes lead to varying mean shifts in residual processes and thus standard CUSUM charts may need to be modified. In this paper, we study the performance of CUSUM charts with various design parameters applied to autocorrelated processes, especially focussing on ARMA(1,1) models, and propose how they can be determined to get better performance in terms of the average run length.

• Journal of the Korean Statistical Society
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• 제27권3호
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• pp.319-329
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• 1998

In the linear regression model with an autocorrelated disturbances, the Cochrane-Orcutt estimator (COE) is a well known alternative to the Generalized Least Squares estimator (GLSE). The efficiency of COE has been studied empirically in a Monte Carlo study when the unknown parameters are estimated by maximum likelihood method. In this paper, it is theoretically proved that the COE is shown to be inferior to the GLSE. The comparisons are based on the difference of corresponding information matrices or the ratio of their determinants.

• 산업경영시스템학회지
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• 제22권51호
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• pp.1-10
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• 1999

In recent industry society, it is revealed that, as an increase in the use of automated manufacturing and process inspection technology, the data from mass production system exhibits some degrees of autocorrelation. The operation characteristics of traditional control charts developed under the independence assumption are adversely affected by the presence of serial correlation. Therefore, when autocorrelated construction contacted with time-series models explain, the time-series models are the Box-Jenkins forecast models which have been proposed as the best forecasting tool which allows for partitioning of variation into result from the autocorrelation structure and variation due to unusual but assignable causes. In this paper, for the AR(1) process of Box-Jenkins forecast models, when the constant term ξ are zero and different from zero, I want to analyze the sensitivity of (equation omitted), CUSUM and EWMA control chart for forecast residuals.

• 산업경영시스템학회지
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• 제34권4호
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• pp.57-65
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• 2011

Monitoring auto correlated processes is prevalent in recent manufacturing environments. As a proactive control for manufacturing processes is emphasized especially in the semiconductor industry, it is natural to monitor real-time status of equipment through sensor rather than resultant output status of the processes. Equipment's sensor data show various forms of correlation features. Among them, considerable amount of sensor data, statistically autocorrelated, is well represented by Box-Jenkins autoregressive moving average (ARMA) model. In this paper, we present a design method of statistical process control (SPC) used for monitoring processes represented by the ARMA model. The proposed method shows benefits in the power of detecting process changes, and considers robustness to ARMA modeling errors simultaneously. We prove benefits through Monte carlo simulation-based investigations.