Multi-block statistics local kernel principal component analysis algorithm and its application in nonlinear process fault detection

2020 ◽  
Vol 376 ◽  
pp. 222-231 ◽  
Author(s):  
Bingqian Zhou ◽  
Xingsheng Gu
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Principal Component ◽  
Nonlinear Process ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Analysis Algorithm ◽  
Principal Component Analysis Algorithm

scholarly journals Statistical Analysis of Nonlinear Processes Based on Penalty Factor

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yingwei Zhang ◽  
Chuanfang Zhang ◽  
Wei Zhang
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Feature Space ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Forgetting Factor ◽  
Analysis Algorithm ◽  
Penalty Factor ◽  
New Process ◽  
Principal Component Analysis Algorithm

A new process monitoring approach is proposed for handling the nonlinear monitoring problem in the electrofused magnesia furnace (EFMF). Compared to conventional method, the contributions are as follows: (1) a new kernel principal component analysis is proposed based on loss function in the feature space; (2) the model of kernel principal component analysis based on forgetting factor is updated; (3) a new iterative kernel principal component analysis algorithm is proposed based on penalty factor.

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