Gaussian and non-Gaussian Double Subspace Statistical Process Monitoring Based on Principal Component Analysis and Independent Component Analysis

2015 ◽  
Vol 54 (3) ◽  
pp. 1015-1027 ◽  
Author(s):  
Jian Huang ◽  
Xuefeng Yan
Keyword(s):  
Principal Component Analysis ◽  
Independent Component Analysis ◽  
Process Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Independent Component ◽  
Statistical Process ◽  
Non Gaussian ◽  
Statistical Process Monitoring

Combination Method of Kernel Principal Component Analysis and Independent Component Analysis for Process Monitoring

2012 ◽  
Vol 249-250 ◽  
pp. 153-158
Author(s):  
Ying Wang Xiao ◽  
Ying Du
Keyword(s):  
Principal Component Analysis ◽  
Independent Component Analysis ◽  
Process Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Independent Component ◽  
Combination Method ◽  
Kernel Principal Component Analysis ◽  
Two Phase ◽  
Monitoring Method

A combination method of kernel principal component analysis (KPCA) and independent component analysis (ICA) for process monitoring is proposed. The new method is a two-phase algorithm: whitened KPCA plus ICA. KPCA spheres data and makes the data structure become as linearly separable as possible by virtue of an implicit nonlinear mapping determined by kernel. ICA seeks the projection directions in the KPCA whitened space, making the distribution of the projected data as non-gaussian as possible. The application to the Tennessee Eastman (TE) simulated process indicates that the proposed process monitoring method can effectively capture the nonlinear relationship in process variables. Its performance significantly outperforms monitoring method based on ICA or KPCA.

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