• 산업경영시스템학회지
• /
• 제32권1호
• /
• pp.94-101
• /
• 2009

Complex Products may present more than one type of defects and these defects are not always of equal severity. These defects are classified according to their seriousness and effect on product quality and performance. Demerit systems are very effective systems to monitoring the different types of defects. So, classical demerit control chart used to monitor counts of several different types of defects simultaneously in complex products. S.M. Na et al.(2003) proposed the Demerit-CUSUM for the improvement of the demerit control chart performance and Nembhard, D. A. et al.(2001) and G.Y Cho et al.(2004) developed a Demerit control chart using the EWMA technique and evaluated the performance of the control chart. In this paper, we present an effective method for process control using the Demerit-CUSUM with fast initial response. Moreover, we evaluate exact performance of the Demerit-CUSUM control chart with fast initial response, Demerit-CUSUM and Demerit-EWMA according to changing sample size or parameters.

• 산업경영시스템학회지
• /
• 제33권2호
• /
• pp.9-17
• /
• 2010

Complex products may present more than one type of defects and these defects are not always of equal severity. These defects are classified according to their seriousness and effect on product quality and performance. So, demerit systems are very effective systems to monitor the different types of defects. Recently, Kang et al.(2009) proposed the revised Demerit-CUSUM for the evaluation of the Demerit-CUSUM control chart performance exactly. In this paper, we present an advanced Demerit control chart using the double EWMA technique. The double EWMA technique is very efficient and strong method for process control where defects and nonconformities occur with various defect types. Moreover, we compare exact performance of Demerit-CUSUM, Demerit-EWMA and Demerit-DEWMA control chart according to changing sample size or mean shifts magnitude. By the result, we confirm that the performance of Demerit-DEWMA control chart is more than the performance of the Demerit-CUSUM and Demerit-EWMA control chart.

• 대한산업공학회지
• /
• 제33권4호
• /
• pp.457-468
• /
• 2007

This paper proposes an adaptive moving average (A-MA) control chart with variable sampling intervals (VSI) for detecting shifts in the process mean. The basic idea of the VSI A-MA chart is to adjust sampling intervals as well as to accumulate previous samples selectively in order to increase the sensitivity. The VSI A-MA chart employs a threshold limit to determine whether or not to increase sampling rate as well as to accumulate previous samples. If a standardized control statistic falls outside the threshold limit, the next sample is taken with higher sampling rate and is accumulated to calculate the next control statistic. If the control statistic falls within the threshold limit, the next sample is taken with lower sampling rate and only the sample is used to get the control statistic. The VSI A-MA chart produces an 'out-of-control' signal either when any control statistic falls outside the control limit or when L-consecutive control statistics fall outside the threshold limit. The control length L is introduced to prevent small mean shifts from being undetected for a long period. A Markov chain model is employed to investigate the VSI A-MA sampling process. Formulae related to the steady state average time-to signal (ATS) for an in-control state and out-of-control state are derived in closed forms. A statistical design procedure for the VSI A-MA chart is proposed. Comparative studies show that the proposed VSI A-MA chart is uniformly superior to the adaptive Cumulative sum (CUSUM) chart and to the Exponentially Weighted Moving Average (EWMA) chart, and is comparable to the variable sampling size (VSS) VSI EWMA chart with respect to the ATS performance.

• Communications for Statistical Applications and Methods
• /
• 제17권3호
• /
• pp.357-365
• /
• 2010

통합공정관리란 잡음이 내재하는 공정에 대하여 수정조치를 취하고, 수정활동 중 공정에 이상원인이 발생하면 이를 관리도를 통해 탐지하여 제거하는 절차를 일컫는다. 이 논문에서는 공정의 잡음모형으로 IMA(1,1) 모형을 가정하고 최소평균제곱오차 수정절차를 수행할 때 일반화가능도비 관리도를 사용하여 이상원인을 탐지하는 절차를 고려하고 있으며, 이러한 상황에서 일반화가능도비 관리도의 관리한계를 설정하는 설계 방법을 제안하였다. 이상원인의 효과로는 공정 평균의 지속적 변화와 지속적 흐름 그리고 공정 분산의 지속적 변화를 고려하였다.

• 한국시뮬레이션학회논문지
• /
• 제20권4호
• /
• pp.115-125
• /
• 2011

제품의 품질 수준 제고를 위해 통계적 공정 관리(SPC : Statistical Process Control)의 다양한 관리도가 기업의 생산 공정을 관리하는데 사용된다. 관리도에 기록되는 공정 데이터는 특정 요인(Assignable Cause)에 의한 이상이 발생했을 때 그 요인에 따라 서로 다른 패턴(Pattern)으로 변화한다. 이러한 패턴을 구별하는 관리도 패턴(CCP : Control Chart Pattern) 인식(Recognition)은 공정에 대한 관리자의 빠른 의사 결정을 위해 매우 중요하다. 앞 선 연구들은 수집되는 원 데이터를 가공 하지않고 그대로 사용하였기 때문에 인식기(Recognizer)의 성능과 학습 속도가 저하되는 문제점이 있었다. 따라서 최근 데이터의 차원 축소와 인식기의 성능 향상을 위해 특질 추출법(Feature Extraction)을 적용한 특질 기반 인식기(Feature based Recognizer)에 대한 연구가 활발히 진행 중이다. 본 논문은 BDK(Bi-Directional Kohonen Network)를 사용하여 CCP의 참조 벡터(Reference Vector)를 생성하고 참조 벡터와 CCP 데이터의 거리를 기반으로 하는 특질을 추출하였다. 추출된 특질을 인공 신경망 기반 인식기의 입력 벡터로 사용하여 학습하였으며 원 데이터를 사용하여 학습하는 인공신경망 인식기와 예측 정확도 비교를 통해 제안 알고리즘의 성능을 평가하였다.

• 품질경영학회지
• /
• 제38권3호
• /
• pp.313-321
• /
• 2010

The coefficient of variation represents the ratio of the standard deviation to the mean, and it is a useful statistic for comparing the degree of variation from one data series to another, even if the means are drastically different from each other. Recently, the CV control chart is developed for monitoring processes in such situations. However, the CV control chart has low performance in detecting small shift. Due to the development of equipment and technique, currently, small shift of process occurs more frequently than large shift. In this paper, we proposes the CV-CUSUM control chart using CUSUM scheme which is cumulative sum of the deviations between each data point and a target value to detect a small shift in the process. We also found that the FIR(fast initial response) CUSUM control chart is especially valuable at start-up or after a CV-CUSUM control chart has signaled out-of-control.

• 한국국방경영분석학회지
• /
• 제22권2호
• /
• pp.126-138
• /
• 1996

The statistical control chart has been proven to be the effective tool most widely used in the manufacturing industry for monitoring and controlling the manufacturing processes. However, the Shewhart chart sometimes gives us false information when the distribution of quality characteristics is skewed. Therefore, it cannot serve as the universal quality control chart if there exist odd events in the manufacturing process. The objective of this study is thus to develop the new technique for constructing the limits of quality control chart based on a sample median and range when the distribution of the underlying population is skewed. This new control chart can effectively solve and manage the processes which have the non-normally distributed quality characteristics frequently occurring in the practical situation.

• 응용통계연구
• /
• 제21권3호
• /
• pp.509-521
• /
• 2008

평균과 분산이 동시에 변화할 수 있는 공정을 관리할 경우, 평균의 변화를 탐지하는 관리도와 분산의 변화를 탐지하는 관리도를 병행하여 사용하는 것이 일반적이다. 여러 연구자들이 하나의 관리도를 사용하여 공정 평균과 분산의 변화를 동시에 탐지할 수 있는 절차를 제안했는데, 이 논문에서는 이와 같은 관리도들을 소개하고 그 효율을 비교하였다. 그 결과 GLR 관리도 Omnibus EWMA 관리도 그리고 Interval 관리도는 충분히 좋은 효율을 가짐을 알 수 있었다.

• 한국경영과학회:학술대회논문집
• /
• 한국경영과학회 2006년도 추계학술대회
• /
• pp.560-570
• /
• 2006

This paper proposes a selectively cumulative sum (S-CUSUM) control chart with variable sampling intervals (VSI) for detecting shifts in the process mean. The basic idea of the VSI S-CUSUM chart is to adjust sampling intervals and to accumulate previous samples selectively in order to increase the sensitivity. The VSI S-CUSUM chart employs a threshold limit to determine whether to increase sampling rate as well as to accumulate previous samples or not. If a standardized control statistic falls outside the threshold limit, the next sample is taken with higher sampling rate and is accumulated to calculate the next control statistic. If the control statistic falls within the threshold limit, the next sample is taken with lower sampling rate and only the sample is used to get the control statistic. The VSI S-CUSUM chart produces an 'out-of-control' signal either when any control statistic falls outside the control limit or when L-consecutive control statistics fall outside the threshold limit. The number L is a decision variable and is called a 'control length'. A Markov chain model is employed to describe the VSI S-CUSUM sampling process. Some useful formulae related to the steady state average time-to signal (ATS) for an in-control state and out-of-control state are derived in closed forms. A statistical design procedure for the VSI S-CUSUM chart is proposed. Comparative studies show that the proposed VSI S-CUSUM chart is uniformly superior to the VSI CUSUM chart or to the Exponentially Weighted Moving Average (EWMA) chart with respect to the ATS performance.

• 품질경영학회지
• /
• 제37권1호
• /
• pp.29-39
• /
• 2009

This article investigates economic-statistical design of VSSI(variable sampling size and interval) cause-selecting charts considering two assignable causes. We consider a process which is composed of two dependent sub-processes. In each sub-process, two kinds of assignable cause may exist. We propose a procedure for designing VSSI cause-selecting charts, based on Lorenzen and Vance model. Computational experiments show that the VSSI cause-selecting chart is superior to the FSSI cause-selecting chart in the economic-statistical characteristics, even under two assignable causes.