Chemical Process Monitoring Based on DNPE-SVDD

Authors

Xiaochun Han, College of Electrical Engineering and Control Science, Nanjing TECH University, Nanjing 211816, China;
Cuimei Bo, College of Electrical Engineering and Control Science, Nanjing TECH University, Nanjing 211816, China;
Yi Hui, College of Electrical Engineering and Control Science, Nanjing TECH University, Nanjing 211816, China;

Abstract

Abstract: Chemical processes test dataset are high dimensional, and have the combined feature of nonlinear and dynamic characteristics. However, traditional linear dimension reduction algorithm cannot extract the local structure information and dynamic characteristics. The monitoring model of chemical process based on dynamic neighborhood preserving embedding-support vector data description (DNPE-SVDD) algorithm is proposed. With the superiority of NPE in nonlinear dimensionality reduction and SVDD in the detection of outliers, dimension is reduced by DNPE algorithm and the monitoring model of the manifold space with reduced dimension is established by SVDD algorithm. The Tennessee Eastman (TE) process is simulated using the proposed model. Compared with DPCA and DNPE algorithms, the simulation results show that the DNPE-SVDD has a higher accuracy of fault detection.

Recommended Citation

Han, Xiaochun; Bo, Cuimei; and Hui, Yi (2019) "Chemical Process Monitoring Based on DNPE-SVDD," Journal of System Simulation: Vol. 30: Iss. 1, Article 23.
DOI: 10.16182/j.issn1004731x.joss.201801023
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss1/23

First Page

184

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201801023

Last Page

190

CLC

TP277

Recommended Citation

Han Xiaochun, Bo Cuimei, Yi Hui. Chemical Process Monitoring Based on DNPE-SVDD[J]. Journal of System Simulation, 2018, 30(1): 184-190.

DOI

10.16182/j.issn1004731x.joss.201801023