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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2011.3
- 수록면
- 8 - 14 (7page)
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초록· 키워드
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Principal Component Analysis (PCA) reduces the dimensionality of the process by creating a new set of variables, Principal components (PCs), which attempt to reflect the true underlying process dimension. However, for highly nonlinear processes, this form of monitoring may not be efficient since the process dimensionality can’t be represented by a small number of PCs. Examples include the process of semiconductors, pharmaceuticals and chemicals. Nonlinear correlated process variables can be reduced to a set of nonlinear principal components, through the application of Kernel Principal Component Analysis (KPCA). Support Vector Data Description (SVDD) which has roots in a supervised learning theory is a training algorithm based on structural risk minimization. Its control limit does not depend on the distribution, but adapts to the real data. So, in this paper proposes a non-linear process monitoring technique based on supervised learning methods and KPCA. Through simulated examples, it has been shown that the proposed monitoring chart is more effective than T² chart for nonlinear processes.
목차
1. 서론
2. 이론적 배경
3. 제안 기법
4. 수치 실험 및 결과
5. 결론 및 추후 연구
참고문헌
참고문헌 (15)
참고문헌 신청
Croiser, R. B / 1988 / Multivariate Generations of Cumulative Sum Quality Control Schemes / Technometrics 30(3):291~303
Gani, W / 2010 / An assessment of the kernel-distance-based multivariate control chart through an industrial application / Quality and Reliability Engineering International
Kumar, S / 2006 / Kernel distance-based robust support vector methods and its application in developing a robust K-chart / International Journal of Production Research 44(1):77~96
Lee, J. M / 2004 / Nonlinear process monitoring using kernel principal component analysis / CHEMICAL ENGINEERING SCIENCE 59(1):223~234
Lowry, C. A / 1992 / A Multivariate Exponentially Weighted Moving Average Control Chart / Technometrics 34(1):46~53
Gani, W / 2010 / An assessment of the kernel-distance-based multivariate control chart through an industrial application / Quality and Reliability Engineering International
Kumar, S / 2006 / Kernel distance-based robust support vector methods and its application in developing a robust K-chart / International Journal of Production Research 44(1):77~96
Lee, J. M / 2004 / Nonlinear process monitoring using kernel principal component analysis / CHEMICAL ENGINEERING SCIENCE 59(1):223~234
Lowry, C. A / 1992 / A Multivariate Exponentially Weighted Moving Average Control Chart / Technometrics 34(1):46~53
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