• Journal of the Korea Society of Computer and Information
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• v.9 no.4 s.32
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• pp.215-220
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• 2004

Production management confronted the environment of infinite competition should be prepared to abrupt variations of management environment and have the ability to be changed in short term. It has to be studied, the control method of products that correspond to multi-functionalization and reduced product life which is caused by high-quality and varied customers demands. As a process control method, we most be able not only to control varies characteristic in a control at once but also to detected special values quickly for high-quality. In this paper a control method referred above is presented.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.16 no.27
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• pp.51-71
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• 1993

본 논문은 새로운 3차원 분류체계를 이용해서 관리도의 문헌을 고찰하고 검토하는데 연구 목적이 있다. 1차원 분류체계는 시간에 따른 연속된 관측치의 관계가 독립인가 자동상판인가로 나누어지며 2차원 분류체계는 독립관측치인 경우 가중치 방법에 따라 Shewart, MA EWMA, CUSUM Charts로 분류되며 자동상관된 관측치인 경우 모델링 방법에 따라 ARIMA, Spectral Charts로 분류된다. 3차원 분류체계는 품질특성인 변수의 수와 종속관계에 따라 일변량과 다변량으로 나누어 진다. 재래식 생산, 자동화 생산, 혹은 장치산업에 적용될 수 있는 관리도가 이 분류체계에 따라 장으로 구분되어 고찰된다. 이는 실무진들의 이해를 돕기 위한 지침으로 활용될 수 있다.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.981-999
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• 2017

In the standard control chart assuming a simple random model, we estimate the process variance without considering the between-sample variance. If the between-sample exists in the process, the process variance is under-estimated. When the process variance is under-estimated, the narrower control limits result in the excessive false alarm rate although the sensitivity of the control chart is improved. In this paper, using the variance component model to incorporate the between-sample variance, we set the control limits using both the within- and between-sample variances, and evaluate the efficiency of the control chart in terms of the average run length (ARL). Considering the most widely used control chart types such as ${\bar{X}}$, EWMA and CUSUM control charts, we compared the differences between two cases, Case I and Case II, where the between-sample variance is ignored and considered, respectively. We also considered the two cases when the process parameters are given and estimated. The results showed that the false alarm rate of Case I increased sharply as the between-sample variance increases, while that of Case II remains the same regardless of the size of the between-sample variance, as expected.

• Journal of Korea Society of Industrial Information Systems
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• v.1 no.1
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• pp.111-125
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• 1996

This paper presents a statistical process control(SPC) system in the electronic parts manufacturing process. In this system, an SPC method is integrated into the automated inspection technology on a real time base. It shows how the collected data can be analyzed with the SPC to provide process information. also presented are stuided of subpixel image processing technology to improve the accuracy of parts mearements , and the cumulative-sum(CUSUM) control chart for fraction defectives.An application of the developed system to connector manufacturing process as a part of computer integrated manufacturing (CIM) is presented.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.16 no.28
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• pp.93-101
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• 1993

In this paper, two adaptive exponentially weighted moving avenge control chart schemes which available for real-time are proposed. The weighting coefficient is estimated using a recursive kalman filter algorithm. Simulated average run lengths indicate the proposed schemes are sensitive to process shifts And their performance is comparable to CUSUM control chart and customary EWMA control chart.

• Journal of Korean Society for Quality Management
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• v.24 no.2
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• pp.172-178
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• 1996

In this study, we developed a software for statistical process control. This software presents $\bar{x}$, R, CUSUM, EWMA control chart and process capability index. In this system, statistical process control methods are integrated into the automated method on a real time base. It is available in process control of specified type and can be performed on personal computer with network system.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.103-112
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• 2009

In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be calibrated. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In this paper, principal component-based Auto-Associative support vector regression (PCSVR) was proposed for the sensor signal validation of the NPP. It utilizes the attractive merits of principal component analysis (PCA) for extracting predominant feature vectors and AASVR because it easily represents complicated processes that are difficult to model with analytical and mechanistic models. With the use of real plant startup data from the Kori Nuclear Power Plant Unit 3, SVR hyperparameters were optimized by the response surface methodology (RSM). Moreover the statistical techniques are integrated with PCSVR for the failure detection. The residuals between the estimated signals and the measured signals are tested by the Shewhart Control Chart, Exponentially Weighted Moving Average (EWMA), Cumulative Sum (CUSUM) and generalized likelihood ratio test (GLRT) to detect whether the sensors are failed or not. This study shows the GLRT can be a candidate for the detection of sensor drift.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.1141-1149
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• 2009

In Bone Mineral Density(BMD) measurements, accuracy and precision must be superior in order to know the small changes in bone mineral density and actual biological changes. Therefore the purpose of this study is to increase the reliability of bone mineral density inspection through appropriate management of image quality from machines and inspectors. For the machine management method, the recommended phantom from each bone mineral density machine manufacturer was used to take 10~25 measurements to determine the standard amount and permitted limit. On each inspection day, measurements were taken everyday or at least three times per week to verify the whether or not change existed in the amount of actual bone mineral density. Also evaluations following Shewhart control chart and CUSUM control chart rules were made for the bone mineral density figures from the phantoms used for measurements. Various forms of management became necessary for machine installation and movement. For the management methods of inspectors, evaluation of the measurement precision was conducted by testing the reproducibility of the exact same figures without any real biological changes occurring during reinspection. There were two measurement methods followed: patients were either measured twice with 30 measurements or three times with 15 measurements. An important point to make regarding measurements is that after the first inspection and any other inspection following, the patient was required to come off the inspection table completely and then get back on for any further measurements. With a 95% confidence level, the precision error produced from the measurement bone mineral figures produced a precision error of 2.77 times the minimum of the biological bone mineral density change (Least significant change: LSC). In order to assure reliability in inspection, there needs to be good oversight of machine management and measurer for machine operation and inspection error. Accuracy error in machines needs to be reduced to under 1% for scientific development in bone mineral density machines.

• Journal of Korean Society for Quality Management
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• v.32 no.4
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• pp.169-183
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• 2004

The synthetic control chart (SCC) proposed by Wu and Spedding (2000) is to detect shifts in the process mean. The performance was re-evaluated by Davis and Woodall (2002), and the steady-state average run length (ARL) performance was shown to be inferior to cumulative sum (CUSUM) or exponentially weighted moving average (EWMA) chart This paper proposes a simple adaptive scheme to improve the performance of the synthetic control chart. That is, once a non-conforming (NC) sample occurs, we investigate the next L-consecutive samples with larger sample sizes and shorter sampling intervals. We employ a Markov chain model to derive the ARL and the average time to s19na1 (ATS). We also propose a statistical design procedure for determining decision variables. Comprehensive comparative study shows that the proposed control chart is uniformly superior to the original SCC or double sampling (DS) Χ chart and comparable to the EWMA chart in ATS performance.

• Journal of Korean Society for Quality Management
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• v.38 no.2
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• pp.190-201
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• 2010

This research proposes a method for designing VSI (Variable Sampling Interval) control charts with supplementary run rules. The basic idea is to apply various run rules and the VSI scheme to a control chart in order to increase the sensitivity. The sampling process of the VSI run rules chart is constructed by Markov chain approach. A procedure for designing the VSI run rules chart is proposed based on Lorenzen and Vance's model. Sensitivity study shows that the VSI run rules charts outperform the FSI (Fixed Sampling Interval) run rules charts for wide range of process mean shifts. A major advantage of the VSI run rules chart over other charts such as CUSUM, EWMA, and adaptive charts is it's simplicity in implementation. Some useful guidelines are proposed based on the sensitivity study.