• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2006년도 추계 학술대회
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• pp.195-198
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• 2006

In this paper, we propose a 2dimension Control Chart. Suggested which Control chart augments Schwart Control chart which is 1-dimensional and Independent variable and dependent variable interrelationship by using control chart. Schwart control chart cannot use input variable and output variable together. In this paper, we try to analysis input variable and output variable dependent and effect. So called 2-dimensional control char.

• 품질경영학회지
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• 제45권2호
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• pp.209-225
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• 2017

Purpose: Since traditional p chart is unable to deal with the variation of attribute data, this paper proposes a new attribute control chart for nonconforming proportions incorporating overdispersion with a beta-binomial model. Methods: Statistical theories for control chart developed under the beta-binomial model and a new approach using this control chart are presented Results: False alarm probabilities of p chart with the beta-binomial model are evaluated and demerits of p chart under overdispersion are discussed from three examples. Hence a concrete procedure for the proposed control chart is provided and illustrated with examples Conclusion: The proposed chart is more useful than traditional p chart, individual chart to treat observed proportions nonconforming as variable data and Laney p' chart.

• 산업경영시스템학회지
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• 제34권2호
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• pp.1-8
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• 2011

This paper proposes a variable sampling interval multivariate $T^2$ control chart with sampling at fixed times, where samples are taken at specified equally spaced fixed time points and additional samples are allowed between these fixed times when indicated by the preceding $T^2$ statistics. At fixed sampling points, the $T^2$ statistics are composed of all quality characteristics and a part of quality characteristics are selected to obtain $T^2$ statistics at additional sampling points. A Markov chain approach is used to evaluate the performance of the proposed chart. Numerical studies for the performance of the proposed chart show that the proposed chart reduces the observations obtained from a process and detects the assignable cause of a process with low correlated quality characteristics quickly.

• 산업경영시스템학회지
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• 제29권2호
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• pp.97-103
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• 2006

Recently, the control chart is developed for monitoring processes with normal short production runs by the coefficient of variation(CV) characteristic for a normal distribution. This control chart does not work well in non-normal short production runs. And most of industrial processes are known to follow the non-normal distribution. Therefore, the control chart is required to be developed for monitoring the processes with non-normal short production runs by the CV characteristics for a non-normal distribution. In this paper, we suggest the control chart for monitoring the processes with a gamma short runs by the CV characteristics for a gamma distribution. This control chart is denoted by the gamma CV control chart. Futhermore evaluated the performance of the gamma CV control chart by average run length(ARL).

• 응용통계연구
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• 제33권2호
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• pp.161-170
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• 2020

${\bar{X}}-S^2$ 관리도는 공정 평균과 산포의 변화를 동시에 탐지하는 전통적인 관리도들 중 하나이다. 일반적으로 사용하는 ${\bar{X}}-S^2$ 관리도의 설계 방법은 병행하는 관리도의 오경보율은 주어진 값을 만족하면서 각 관리도는 동일한 개별적인 오경보율을 갖도록 설정하는 것이다. 이 논문에서는 각 관리도의 개별 오경보율을 다르게 설정하고 이것이 ${\bar{X}}-S^2$ 관리도의 성능에 어떠한 영향을 주는지 살펴보았다. 이를 위해 ${\bar{X}}$ 관리도의 오경보율을 S² 관리도의 오경보율에 γ배한 경우를 고려하였고, γ값에 따른 ${\bar{X}}-S^2$ 관리도 성능을 비교하였다. 관리도의 성능을 평가하는 측도로는 특정한 변화에 대한 성능을 판단하는 경우 이상상태에서의 평균런길이를 사용하였고, 전반적인 성능을 판단하는 경우 RMI(relative mean index)를 사용하였다.

• Industrial Engineering and Management Systems
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• 제4권2호
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• pp.158-164
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• 2005

Kusukawa and Ohta presented the $CS_{CQ-r}$ chart to monitor the process defect $rate{\lambda}$ in high-yield processes that is derived from the count of defects. The $CS_{CQ-r}$ chart is more sensitive to $monitor{\lambda}$ than the CQ (Cumulative Quantity) chart proposed by Chan et al.. As a more superior chart in high-yield processes, we propose a Synthetic chart that is the integration of the CQ_-r chart and the $CS_{CQ-r}$chart. The quality characteristic of both charts is the number of units y required to observe r $({\geq}2)$ defects. It is assumed that this quantity is an Erlang random variable from the property that the quality characteristic of the CQ chart follows the exponential distribution. In use of the proposed Synthetic chart, the process is initially judged as either in-control or out-of-control by using the $CS_{CQ-r}$chart. If the process was not judged as in-control by the $CS_{CQ-r}$chart, the process is successively judged by using the $CQ_{-r}$chart to confirm the judgment of the $CS_{CQ-r}$chart. Through comparisons of ARL (Average Run Length), the proposed Synthetic chart is more superior to monitor the process defect rate in high-yield processes to the stand-alone $CS_{CQ-r}$ chart.

• 품질경영학회지
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• 제40권3호
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• pp.337-346
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• 2012

Purpose: A Control chart is one of the important statistical process control tools that can improve processes by reducing variability and defects. Methods: In the present study, we propose the local $T^2$ multivariate control chart that can efficiently detect abnormal observations by considering the local pattern of the in-control observations. Results: A simulation study has been conducted to examine the property of the proposed control chart and compare it with existing multivariate control charts. Conclusion: The results demonstrate the usefulness and effectiveness of the proposed control chart.

• 산업경영시스템학회지
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• 제36권2호
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• pp.56-62
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• 2013

Control chart is representative tool of Statistical Process Control (SPC). But, it is not given information about the economic loss that occurs when a product is produced characteristic value does not match the target value of the process. In order to manage the process, we should consider not only stability of the variation also produce products with a high degree of matching the target value that is most ideal quality characteristics. There is a need for process control in consideration of economic loss. In this paper, we design a new control chart using the quadratic loss function of Taguchi. And we demonstrate effectiveness of new control chart by compare its ARL with ${\overline{x}}-R$ control chart.

• Industrial Engineering and Management Systems
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• 제12권2호
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• pp.112-117
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• 2013

The $\overline{x}$ control chart for the process mean and either the R or s control chart for the process dispersion have been used together to monitor the manufacturing processes. However, it has been pointed out that this procedure is flawed by a fault that makes it difficult to capture the behavior of process condition visually by considering the relationship between the shift in the process mean and the change in the process dispersion because the respective characteristics are monitored by an individual control chart in parallel. Then, the ($\overline{x}$, s) control chart has been proposed to enable the process managers to monitor the changes in the process mean, process dispersion, or both. On the one hand, identifying which process parameters are responsible for out-of-control condition of process is one of the important issues in the process management. It is especially important in the ($\overline{x}$, s) control chart where some parameters are monitored at a single plane. The previous literature has proposed the multiple decision method based on the statistical hypothesis tests to identify the parameters responsible for out-of-control condition. In this paper, we propose how to identify parameters responsible for out-of-control condition using the information criterion. Then, the effectiveness of proposed method is shown through some numerical experiments.

• 지질공학
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• 제25권1호
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• pp.9-20
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• 2015

A strainmeter goes through a period of instability immediately after installation. To determine the stability of strainmeters installed around the Andong fault zone, South Korea, an x-MR control chart analysis and a T² control chart analysis were conducted. The x-MR control chart analysis used an empirically determined 3σ control limit line to identify abnormal data in recently installed strain gauges. In the T² control chart analysis, the control limit line was set at a confidence of 95%. A comparison of the early stage of measurement with the terminal stage of measurement for three months after installation indicates that stabilization depends on the location and direction of each strain gauge in x-MR control chart analysis. In the T² control chart analysis, the number of values exceeding the control limit line decreased as the terminal stage was approached. Based on these results, it is suggested that the 3σ control limit line of an x-MR control chart can be used as a standard for single gauge stability, and that the 95% confidence limit of a T² control chart analysis could be used as the standard for the stability of multi-gauge strainmeters.