• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.26
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• pp.181-187
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• 1992

본 논문은 CIMS에서 적응되는 ARMA 공정제어의 새로운 3단계절차를 제안한다. 첫번째 단계는 다변량 ARMA모델을 식별하여 모수를 추정하고, white noise로 진단된 잔차 series에 대하여 다변량 제어통계량(즉, 다변량 Hotelling T$^2$통계량, 다변량 CUSUM, 다변량 EWHA 통계량, 다변량 MA 통계량)등을 계산한다. 마지막으로 본 논문에서 제안한 8가지 다변량 제어통계량을 상호비교하여 이상점을 발견한다.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1123-1135
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• 2011

This paper considers the multivariate integrated process control procedure for detecting special causes in a multivariate IMA(1, 1) process. When the multivariate control chart signals, the special cause will be detected and eliminated from the process. However, when the elimination of the special cause costs high or is not practically possible, an alternative action is to readjust the process with approximately modified adjustment scheme. In this paper, we propose the readjustment procedure after having a true signal, and show that the use of the readjustment can reduce the deviation of a process from the target.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1275-1283
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• 2013

This paper considers the multivariate integrated process control procedure for detecting special causes in a multivariate IMA(1, 1) process. When the multivariate control chart signals, the special cause will be detected and eliminated from the process. However, when the elimination of the special cause costs high or is not practically possible, an alternative action is to readjust the process with approximately modified adjustment scheme. In this paper, we estimate parameters in the readjustment procedure after having a true signal in the multivariate integrated process control.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.999-1008
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• 2010

The objective of this paper is to develop variable sampling interval multivariate control charts that can offer significant performance improvements compared to standard fixed sampling rate multivariate control charts. Most research on multivariate control charts has concentrated on the problem of monitoring the process mean, but here we consider the problem of simultaneously monitoring both the mean and variability of the process.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.26
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• pp.119-124
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• 1992

본 논문은 장치산업에서 적용되는 다변량 EWMA 공정제어와 통계적 공정감시 통합시스템을 제안한다. 본 논문에서 제안한 통합시스템은 자동공정제어(APC)의 예측, 조정기능과 통계적 정정감시(SPM)의 이상점 발견 및 제거등의 각각의 장점을 이용하였다. 기존의 다변량 EWMA연구는 데이타간의 독립성을 가정하였으나 본 논문은 데이타간의 종속적인 형태인 IMA(1,1)모델을 대상으로 하였다.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.639-652
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• 2015

In today's manufacturing environment, the process data can be easily measured and transferred to a computer for analysis in a real-time mode. As a result, it is possible to monitor several correlated quality variables simultaneously. Various multivariate statistical process control (MSPC) procedures have been presented to detect an out-of-control event. Although the classical MSPC procedures give the out-of-control signal, it is difficult to determine which variable has caused the signal. In order to solve this problem, data mining and machine learning techniques can be considered. In this paper, we applied the technique of decision tree learning to the MSPC, and we did simulation for MSPC procedures to monitor the bivariate normal process means. The results of simulation show that the overall performance of the MSPC procedure using decision tree learning technique is similar for several values of correlation coefficient, and the accurate classification rates for out-of-control are different depending on the values of correlation coefficient and the shift magnitude. The introduced procedure has the advantage that it provides the information about assignable causes, which can be required by practitioners.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.281-290
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• 2013

It is well known that the DD-plot is a useful graphical tool for non-parametric classification. In this paper, we propose another use of DD-plot for detecting the out-of-control state in multivariate process. We suggested a dynamic version of DD-plot and its accompanying a quality index plot in such case.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.192-197
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• 2020

Multivariate processes, such as large scale power plants or chemical processes are operated in very hazardous environment, which can lead to significant human and material losses if a fault occurs. On-line monitoring technology, therefore, is essential to detect system faults. In this paper, the ICA-based fault detection method is conducted using three different multivariate process data. Fault detection procedure based on ICA is divided into off-line and on-line processes. The off-line process determines a threshold for fault detection by using the obtained dataset when the system is normal. And the on-line process computes statistics of query vectors measured in real-time. The fault is detected by comparing computed statistics and previously defined threshold. For comparison, the PCA-based fault detection method is also implemented in this paper. Experimental results show that the ICA-based fault detection method detects the system faults earlier and better than the PCA-based method.

• Journal of the Korea Society of Computer and Information
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• v.12 no.4
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• pp.201-207
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• 2007

In some manufacturing industries, there are many situation in which the simultaneous monitoring or control of two or more related quality characteristics is necessary. However, monitoring these two or more related quality characteristics independently can be very misleading. When several characteristics of manufactured component are to be monitored simultaneously, multivariate $x^2$ or $T^2$ control chart can be used. In this paper, establishing a early warning system(EWS) using multivariate control charts to analyze early out-of-control signals in melting process with many quality characteristics was presented. This module which we developed to control several characteristics improved efficiency and effectiveness of process control in the melting process.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.53-69
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• 2003

Many process variables in modem manufacturing processes have influence on quality of products with complicated relationships. Therefore, it is necessary to control multiple quality variables in order to monitor abnormal signals in the processes. This study proposes an integrated procedure of self-organizing map (SOM) neural network and case-based reasoning (CBR) for multivariate process control. SOM generates patterns of quality variables. The patterns are compared with the reference patterns in order to decide whether their states are normal or abnormal using the goodness-of-fitness test. For validation, it generates artificial datasets consisting of six patterns, normal and abnormal patterns. Experimental results show that the abnormal patterns can be detected effectively. This study also shows that the CBR procedure enables to keep Type 2 error at very low level and reduce Type 1 error gradually, and then the proposed method can be a solution fur multivariate process control.