Nonlinear Process Monitoring: Part 2

2012 ◽  
pp. 61-80
Author(s):  
Zhiqiang Ge ◽  
Zhihuan Song
Keyword(s):  
Process Monitoring ◽  
Nonlinear Process ◽  
Nonlinear Process Monitoring

Nonlinear process monitoring based on load weighted denoising autoencoder

Measurement ◽  
2021 ◽  
Vol 171 ◽  
pp. 108782
Author(s):  
Jiazhen Zhu ◽  
Hongbo Shi ◽  
Bing Song ◽  
Yang Tao ◽  
Shuai Tan ◽  
...  
Keyword(s):  
Process Monitoring ◽  
Nonlinear Process ◽  
Denoising Autoencoder ◽  
Nonlinear Process Monitoring

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.

Nonlinear process monitoring using kernel nonnegative matrix factorization

2017 ◽  
Vol 96 (2) ◽  
pp. 554-563 ◽  
Author(s):  
Lirong Zhai ◽  
Yingwei Zhang ◽  
Shouping Guan ◽  
Yuanjian Fu ◽  
Lin Feng
Keyword(s):  
Process Monitoring ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Nonlinear Process ◽  
Nonlinear Process Monitoring
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