IE interfaces (산업공학)

Korean Institute of Industrial Engineers (대한산업공학회)
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Abnormality Detection to Non-linear Multivariate Process Using Supervised Learning Methods

지도학습기법을 이용한 비선형 다변량 공정의 비정상 상태 탐지

  • Son, Young-Tae (Department of Industrial Engineering, Hanyang University) ;
  • Yun, Deok-Kyun (Department of Industrial Engineering, Hanyang University)
  • 손영태 (한양대학교 산업공학과) ;
  • 윤덕균 (한양대학교 산업공학과)
  • Received : 2010.11.11
  • Accepted : 2011.01.14
  • Published : 2011.03.01
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Abstract

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 $T^2$ chart for nonlinear processes.

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References

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