Fault Detection Approach Based on Weighted Principal Component Analysis Applied to Continuous Stirred Tank Reactor
Shanmao Gu*, Yunlong Liu, Ni Zhang, De Du
Identifiers and Pagination:Year: 2015
First Page: 966
Last Page: 972
Publisher Id: TOMEJ-9-966
Article History:Received Date: 17/02/2014
Revision Received Date: 21/03/2015
Acceptance Date: 09/06/2015
Electronic publication date: 7/10/2015
Collection year: 2015
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.
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.