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JACIII Vol.25 No.1 pp. 101-109
doi: 10.20965/jaciii.2021.p0101
(2021)

Paper:

# Uncertain Nonlinear Process Monitoring Using Interval Ensemble Kernel Principal Component Analysis

## Xianrui Wang*, Guoxin Zhao*,†, Yu Liu*, and Shujie Yang**

*College of Information Engineering, Beijing Institute of Petrochemical Technology
No.19 Qingyuan North Road, Daxing District, Beijing 102617, China

**School of Marine Engineering Equipment, Zhejiang Ocean University
No.1 Haida South Road, Dinghai District, Zhoushan, Zhejiang 336022, China

Corresponding author

October 8, 2020
Accepted:
November 17, 2020
Published:
January 20, 2021
Keywords:
uncertain nonlinear process, interval-valued variables, ensemble learning, bayesian decision
Abstract

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.

X. Wang, G. Zhao, Y. Liu, and S. Yang, “Uncertain Nonlinear Process Monitoring Using Interval Ensemble Kernel Principal Component Analysis,” J. Adv. Comput. Intell. Intell. Inform., Vol.25 No.1, pp. 101-109, 2021.
Data files:
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Last updated on Jun. 03, 2024