• Communications for Statistical Applications and Methods
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• 제23권6호
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• pp.497-515
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• 2016

Modern processes often monitor more than one quality characteristic that are referred to as multivariate statistical process control (MSPC) procedures. The MSPC is the most rapidly developing sector of statistical process control and increases interest in the simultaneous inspection of several related quality characteristics. Most multivariate detection procedures based on a multi-normality assumptions are independent, but there are many processes that assume non-normality and correlation. Many multivariate control charts have a lack of related joint distribution. Copulas are tool to construct multivariate modelling and formalizing the dependence structure between random variables and applied in several fields. From copula literature review, there are a few copula to apply in MSPC that have multivariate control charts, and represent a successful tool to identify an out-of-control process. This paper presents various types of copulas modelling for the multivariate control chart. The performance measures of the control chart are the average run length (ARL) and the average number of observations to signal (ANOS). Furthermore, a Monte Carlo simulation is shown when the observations were from an exponential distribution.

• Journal of the Korean Data and Information Science Society
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• 제26권3호
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• pp.639-652
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• 2015

현대의 제조공정은 컴퓨터의 발전과 통신 및 네트워크의 발달로 컴퓨터통합제조가 가능해졌다. 이로 인해 고품질 제품의 고속 생산공정이 확대되고, 공정에서 실시간으로 전송되는 다양한 품질변수들의 데이터 축적 또한 가능하게 되었다. 이를 관리하기 위해서는 다변량 통계적 공정관리 절차가 필요하다. 전통적으로 사용하는 다변량 관리도는 이상상태 발생시 이상신호를 주지만, 이상원인이 어떠한 변수에 어떠한 영향을 주는지에 대한 정보를 제공하지 않는다는 단점이 있다. 이를 보완하기 위해 데이터마이닝과 기계학습 기법을 이용할 수 있다. 이 논문에서는 의사결정나무 학습 기법을 이용한 다변량 공정관리 절차를 소개하고, 이변량인 경우 모의실험을 통하여 그 효율을 살펴보았다. 모의실험 결과를 살펴볼 때, 상관계수에 따라 이상상태 탐지 능력은 비슷한 것으로 나타났고, 이상상태에 대한 분류 정확도는 상관계수와 이상원인의 형태에 따라 차이가 있지만 기존의 다변량 관리도에서는 제공하지 않는 이상원인의 정보를 제공하는 장점이 있음을 알 수 있다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.391-391
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• 2000

Manufacturing process generally exhibits major or minor variations due to deviation of raw materials, equipment degradation, controller malfunction, and so on. Extensive research based on multivariate statistical process control has been done to monitor the unstable states and indicate a corrective action. A prototype of intelligent operation support system ("ISYS-MSPC") has been developed as a tool that supports the enhanced operation to guarantee the high productivity and a uniform high quality product. This system has been applied to the industrial batch and continuous processes and its performance has been validated .

• 한국경영과학회지
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• 제36권1호
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• pp.39-55
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• 2011

Process monitoring has been emphasized for the monitoring of complex system such as chemical processing industries to achieve the efficiency enhancement, quality management, safety improvement. Recently, ICA (Independent Component Analysis) based MSPC (Multivariate Statistical Process Control) was widely used in process monitoring approaches. Moreover, DICA (Dynamic ICA) has been introduced to consider the system dynamics. However, the existing approaches show the limitation that their performances are strongly dependent on the statistical distributions of control variables. To improve the limitation, we propose a novel approach for process monitoring by integrating DICA and LOF (Local Outlier Factor). In this paper, we aim to improve the fault detection rate with the proposed method. LOF detects local outliers by using density of surrounding space so that its performance is regardless of data distribution. Therefore, the proposed method not only can consider the system dynamics but can also assure robust performance regardless of the statistical distributions of control variables. Comparison experiments were conducted on the widely used benchmark dataset, Tennessee Eastman process (TE process), and showed the improved performance than existing approaches.

• Environmental Engineering Research
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• 제11권2호
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• pp.63-76
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• 2006

Multivariate analysis and batch monitoring on a pilot-scale sequencing batch reactor (SBR) are described for integrated wastewater treatment management system, where a batchwise multiway independent component analysis method (MICA) are used to extract meaningful hidden information from non-Gaussian wastewater treatment data. Three-way batch data of SBR are unfolded batch-wisely, and then a non-Gaussian multivariate monitoring method is used to capture the non-Gaussian characteristics of normal batches in biological wastewater treatment plant. It is successfully applied to an 80L SBR for biological wastewater treatment, which is characterized by a variety of error sources with non-Gaussian characteristics. The batchwise multivariate monitoring results of a pilot-scale SBR for integrated wastewater treatment management system showed more powerful monitoring performance on a WWTP application than the conventional method since it can extract non-Gaussian source signals which are independent and cross-correlation of variables.