Multi-block principal component analysis based on variable weight information and its application to multivariate process monitoring

2017 ◽  
Vol 96 (5) ◽  
pp. 1127-1141 ◽  
Author(s):  
Lei Wang ◽  
Xiaogang Deng
Keyword(s):  
Principal Component Analysis ◽  
Process Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Multivariate Process ◽  
Variable Weight ◽  
Multivariate Process Monitoring

Online reduced kernel principal component analysis for process monitoring

2018 ◽  
Vol 61 ◽  
pp. 1-11 ◽  
Author(s):  
Radhia Fezai ◽  
Majdi Mansouri ◽  
Okba Taouali ◽  
Mohamed Faouzi Harkat ◽  
Nasreddine Bouguila
Keyword(s):  
Principal Component Analysis ◽  
Process Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis

Uncertain Nonlinear Process Monitoring Using Interval Ensemble Kernel Principal Component Analysis

2021 ◽  
Vol 25 (1) ◽  
pp. 101-109
Author(s):  
Xianrui Wang ◽  
Guoxin Zhao ◽  
Yu Liu ◽  
Shujie Yang ◽  
◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Process Monitoring ◽  
Principal Component ◽  
Nonlinear Process ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Gaussian Kernel ◽  
Bayesian Decision ◽  
Symbolic Data ◽  
Nonlinear Process Monitoring

To solve uncertainties in industrial processes, interval kernel principal component analysis (IKPCA) has been proposed based on symbolic data analysis. However, it is experimentally discovered that the performance of IKPCA is worse than that of other algorithms. To improve the IKPCA algorithm, interval ensemble kernel principal component analysis (IEKPCA) is proposed. By optimizing the width parameters of the Gaussian kernel function, IEKPCA yields better performances. Ensemble learning is incorporated in the IEKPCA algorithm to build submodels with different width parameters. However, the multiple submodels will yield a large number of results, which will complicate the algorithm. To simplify the algorithm, a Bayesian decision is used to convert the result into fault probability. The final result is obtained via a weighting strategy. To verify the method, IEKPCA is applied to the Tennessee Eastman (TE) process. The false alarm rate, fault detection rate, accuracy, and other indicators used in the IEKPCA are compared with those of other algorithms. The results show that the IEKPCA improves the accuracy of uncertain nonlinear process monitoring.

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