붓스트랩을 활용한 이상원인변수의 탐지 기법
Bootstrap-Based Fault Identification Method
2
조회
0
다운로드
5
인용
0
참고문헌
키워드
초록
Multivariate control charts are widely used to monitor the performance of a multivariate process over time to maintain control of the process. Although existing multivariate control charts provide control limits to monitor the process and detect any extraordinary events, it is a challenge to identify the causes of an out-of-control alarm when the number of process variables is large. Several fault identification methods have been developed to address this issue. However, these methods require a normality assumption of the process data. In the present study, we propose a bootstrapped-based T^2 decomposition technique that does not require any distributional assumption. A simulation study was conducted to examine the properties of the proposed fault identification method under various scenarios and compare it with the existing parametric T^2 decomposition method. The simulation results showed that the proposed method produced better results than the existing one, especially in nonnormal situations.
Abstract
Multivariate control charts are widely used to monitor the performance of a multivariate process over time to maintain control of the process. Although existing multivariate control charts provide control limits to monitor the process and detect any extraordinary events, it is a challenge to identify the causes of an out-of-control alarm when the number of process variables is large. Several fault identification methods have been developed to address this issue. However, these methods require a normality assumption of the process data. In the present study, we propose a bootstrapped-based T^2 decomposition technique that does not require any distributional assumption. A simulation study was conducted to examine the properties of the proposed fault identification method under various scenarios and compare it with the existing parametric T^2 decomposition method. The simulation results showed that the proposed method produced better results than the existing one, especially in nonnormal situations.
참고문헌 (16)
- 장경, "통계적 품질관리" (2006), 자유아카데미
- Chongfuangprinya, P., "Integration of support vector machines and control charts for multivariate process monitoring", Journal of Statistical Computation and Simulation (2010)
- Efron, B., "An Introduction to the Bootstrap", pp.1993, Chapman & Hall
- Hotelling, H., "Multivariate Quality Control, In Techniques of Statistical Analysis" (1947), McGraw-Hill
- 정현철, "신경망을 이용한 동적 수율 개선 모형", 산업경영시스템학회지, 32(2), pp.132~139 (2009), 한국산업경영시스템학회
- Li, J., "Causation-Based Decomposition for Multivariate Process Monitoring and Diagnosis", Journal of Quality Technology, 4, pp.6~58 (2008)
- Mason, R. L., "Decomposition of for multivariate control chart interpretation", Journal of Quality Technology, 27(2), pp.99~108 (1995)
- Mason, R. L., "Multivariate Statistical Process Control With Industrial Applications" (2002), American Statistical Association and the Society for Industrial and Applied Mathematics
- Montgomery, D. C., "Introduction to Statist ical Quality Control, 5th ed" (2005), Wiley
- Runger, G. C., "Projections and the multivariate control chart", Journal of Quality Technology, 28(3), pp.313~319 (1996)
- Runger, G. C., "Contributors to a multivariate statistical process control chart signal", Communications in Statistics: Theory and Methods, 25(10), pp.2203~2213 (1996)
- Shewhart, W. A., "Statistical Methods from the viewpoint of Quality Control" (1939), Dover Publications
- 김성범, "One-class classification-based control charts for multivariate process monitoring", IIE Trans., 42, pp.107~120
- 송서일, "붓스트랩 방법을 이용한 로버스트 관리도", 산업경영시스템학회지, 26(3), pp.39~49 (2003), 한국산업경영시스템학회
- Wierda, S. J., "Multivariate statistical process control: recent results and directions for future research", Statistica Neerlandica, 48(2), pp.147~168 (1994)
- Woodall, W. H., "Research issues and ideas in statistical process control", Journal of Quality Technology, 31(4), pp.376~386 (1999)