On the application of recursive principal component analysis method to fault detection and isolation

2015 ◽  
Author(s):  
I. Jaffel ◽  
O. Taouali ◽  
I. Elaissi ◽  
H. Massaoud
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Detection And Isolation ◽  
Analysis Method ◽  
Principal Component Analysis Method

Fault detection and isolation in nonlinear systems with partial Reduced Kernel Principal Component Analysis method

2016 ◽  
Vol 40 (4) ◽  
pp. 1289-1296 ◽  
Author(s):  
Ines Jaffel ◽  
Okba Taouali ◽  
Mohamed Faouzi Harkat ◽  
Hassani Messaoud
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Kernel Principal Component Analysis ◽  
Continuous Stirred Tank Reactor ◽  
Analysis Method ◽  
Continuous Stirred Tank

In this article, we suggest an extension of our proposed method in fault detection called Reduced Kernel Principal Component Analysis (RKPCA) (Taouali et al., 2015) to fault isolation. To this end, a set of structured residues is generated by using a partial RKPCA model. Furthermore, each partial RKPCA model was performed on a subset of variables to generate structured residues according to a properly designed incidence matrix. The relevance of the proposed algorithm is revealed on Continuous Stirred Tank Reactor.

scholarly journals Application of Principal Component Analysis Method to Determine Prediction Indexes Sensibility Sequence

2011 ◽  
Vol 26 ◽  
pp. 1346-1351
Author(s):  
Yang Guo-liang ◽  
Wang Can-zhao ◽  
Wu Shi-yue ◽  
Jia Li-qing ◽  
Zhang Sheng-zhu
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Method ◽  
Principal Component Analysis Method
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