REVIEW ARTICLE


Fault Detection Approach Based on Weighted Principal Component Analysis Applied to Continuous Stirred Tank Reactor



Shanmao Gu*, Yunlong Liu, Ni Zhang, De Du
College of Information and Control Engineering, Weifang University, Weifang 261061, P.R. China


© 2015 Gu et al

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the College of Information and Control Engineering, Weifang University, Weifang 261061, PR China; Tel: 086+186-5471-0898; E-mail: gsm197851@126.com


Abstract

Fault detection approach based on principal component analysis (PCA) may perform not well when the process is time-varying, because it can cause unfavorable influence on feature extraction. To solve this problem, a modified PCA which considering variance maximization is proposed, referred to as weighted PCA (WPCA). WPCA can obtain the slow features information of observed data in time-varying system. The monitoring statistical indices are based on WPCA model and their confidence limits are computed by kernel density estimation (KDE). A simulation example on continuous stirred tank reactor (CSTR) show that the proposed method achieves better performance from the perspective of both fault detection rate and fault detection time than conventional PCA model.

Keywords: Chemical process, continuous stirred tank reactor, fault detection, time-varying, weighted principal component analysis.