• Journal of the Korean Data and Information Science Society
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• 제20권1호
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• pp.11-24
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• 2009

수정절차에서 공정수정기는 잡음이 존재하지만 제거할 수 없을 때 공정수준을 목표치에 가깝게 수정하는데 종종 유용하게 사용된다. 강건 수정기의 예로는 단일 및 이중 지수가중이동평균 수정기가 있다. 이중 지수가중이동평균 수정기는 단일 지수가중이동평균 수정기가 제거할 수 없는 공정편차의 치우침을 줄일 수 있도록 고안되었다. 이 논문에서는 이 두 가지 수정기가 적용될 때 과도하게 커질 수 있는 수정량분산을 줄일 수 있도록 원래의 수정기에 지수가중이동평균을 적용함으로써 수정되었다. 주어지 수정기에 대한 지수가중이동평균 수정은 편차제곱평균은 조금 증가시키지만, 수정량분산을 줄이는데 성공적임을 보이고 있다.

• Journal of the Korean Data and Information Science Society
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• 제16권2호
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• pp.467-476
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• 2005

Multivariate control charts for monitoring mean vector of several related quality variables with combine-accumulate approach and accumulate-combine apprach were investigated. Shewhart chart is also proposed to compare the performances of CUSUM and EWMA charts. Numerical comparisons show that CUSUM and EWMA charts are more efficient than Shewhart chart for small or moderate shifts, and multivariate charts based on accumulate- combine approach is more efficient than corresponding multivariate charts based on combine-accumulate approach.

• Communications for Statistical Applications and Methods
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• 제12권2호
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• pp.265-273
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• 2005

In this paper, we proposed multivariate EWMA control charts for both combine-accumulate and accumulate-combine approaches to monitor dispersion matrix of multiple quality variables. Numerical performance of the proposed charts are evaluated in terms of average run length(ARL). The performances show that small smoothing constants with accumulate-combine approach is preferred for detecting small shifts of the production process.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2013년도 춘계학술대회 논문집
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• pp.643-644
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• 2013

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2011년도 춘계학술대회
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• pp.675-693
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• 2011

We compare three Techniques control systems with The Investigate Study on the relation between Multivariate SPC and Autoregressed Algorithm. We also investigate Autoregressed Algorithm with relevant EWMA, CUSUM, Shewhart chart, Precontrol chart and Process Capacity.

• Journal of the Korean Data and Information Science Society
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• 제21권4호
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• pp.681-688
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• 2010

통계적 공정관리에서 결점수를 모니터링 하는데는 c-관리도가 사용된다. 전통적인 c-관리도는 표본에서 결점의 발생은 포아송분포를 따른다는 가정 하에서 만들어진다. 포아송 분포에 대한 가정이 맞지 않을 때에는 X-관리도가 사용될 수 있다. 지수가중이동평균관리도는 공정의 작은 변화를 찾는 데 유용한 것으로 알려져 있다. 본 논문에서는 다양한 카즈분포족으로부터 생성된 계수자료에 대하여 3시그마 X-관리도와 지수가중이동평균관리도의 효율을 평균 런의 길이에 근거하여 비교한다. 즉, 자료가 어떤 분포로부터 생성되었는지 알 수 없을 때, X-관리도와 지수가중이동평균관리도를 비교하는 것이다.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2004년도 학술발표논문집
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• pp.179-184
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• 2004

Statistical process control (SPC) and engineering process control (EPC) are based on different strategies for process quality improvement. SPC reduces process variability by detecting and eliminating special causes of process variation, while EPC reduces process variability by adjusting compensatory variables to keep the quality variable close to target. Recently there has been need for an integrated process control (IPC) procedure which combines the two strategies. This article considers a scheme that simultaneously applies SPC and EPC techniques to reduce the variation of a process. The process disturbance model under consideration is an IMA(1,1) model with a location shift. The EPC part of the scheme adjusts the process, while the SPC part of the scheme detects the occurrence of a special cause. For adjusting the process repeated adjustment is applied by compensating the predicted deviation from target. For detecting special causes the two kinds of exponentially weighted moving average (EWMA) control chart are applied to the observed deviations: One for detecting location shift and the other for detecting increment of variability. It was assumed that the adjustment of the process under the presence of a special cause may change any of the process parameters as well as the system gain. The effectiveness of the IPC scheme is evaluated in the context of the average cost per unit time (ACU) during the operation of the scheme. One major objective of this article is to investigate the effects of the process parameters to the ACU. Another major objective is to give a practical guide for the efficient selection of the parameters of the two EWMA control charts.

• 산업경영시스템학회지
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• 제22권52호
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• pp.337-345
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• 1999

The long-tailed, positively skewed exponential distribution can be made into an almost symmetric distribution by taking the exponent of the data. In these situations, to use the traditional shewhart control limits on an individuals chart would be impractical and inconvenient. The transformed data, approximately bell-shaped, can be plotted conveniently on the individuals chart and exponentially weighted moving average chart. In this paper, using modifying statistics with transformed exponential of the data, we give a method for constructing control charts. Selecting method of exponent for individual chart is evaluated. And consider that smaller weight being assigned to the older data as time process and properties and taking method of exponent($\theta$), weighting factor($\alpha$) are suggested. Our recommendation, on the basis result of simulation, is practical method for EWMA chart.

• Communications for Statistical Applications and Methods
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• 제6권2호
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• pp.413-422
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• 1999

Multivariate EWMA control charts to simultaneously monitor correlation coefficients of correlated quality characteristics under multivariate normal process are proposed. Performances of the proposed charts are measured in terms of average run length(ARL). Numerical results show that smalle values for smoothing constant with accumulate-combine approach are preferred for detecting smalle shifts.

• Communications for Statistical Applications and Methods
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• 제7권3호
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• pp.773-788
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• 2000

Statistical process control (SPC) and engineering process control (EPC) are based on different strategies for processes quality improvement. SPC reduces process variability by detecting and eliminating special causes of process variation. while EPC reduces process variability by adjusting compensatory variables to keep the quality variable close to target. Recently there has been needs for a process control proceduce which combines the tow strategies. This paper considers a combined scheme which simultaneously applies SPC and EPC techniques to reduce the variation of a process. The process model under consideration is an integrated moving average(IMA) process with a step shift. The EPC part of the scheme adjusts the process back to target at every fixed monitoring intervals, which is referred to a repeated adjustment scheme. The SPC part of the scheme uses an exponentially weighted moving average(EWMA) of observed deviation from target to detect special causes. A Markov chain model is developed to relate the scheme's expected cost per unit time to the design parameters of he combined control scheme. The expected cost per unit time is composed of off-target cost, adjustment cost, monitoring cost, and false alarm cost.