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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Feature extraction plays a key role in fault detection methods. Most existing methods focus on comprehensive and accurate feature extraction of normal operation data to achieve better detection performance. However, discriminative features based on historical fault data are usually ignored. Aiming at this point, a global-local marginal discriminant preserving projection (GLMDPP) method is proposed for feature extraction. Considering its comprehensive consideration of global and local features, global-local preserving projection (GLPP) is used to extract the inherent feature of the data. Then, multiple marginal fisher analysis (MMFA) is introduced to extract the discriminative feature, which can better separate normal data from fault data. On the basis of fisher framework, GLPP and MMFA are integrated to extract inherent and discriminative features of the data simultaneously. Furthermore, fault detection methods based on GLMDPP are constructed and applied to the Tennessee Eastman (TE) process. Compared with the PCA and GLPP method, the effectiveness of the proposed method in fault detection is validated with the result of TE process.

Details

Title
Fault Detection Method Based on Global-Local Marginal Discriminant Preserving Projection for Chemical Process
Author
Yang, Li 1   VIAFID ORCID Logo  ; Ma, Fangyuan 2   VIAFID ORCID Logo  ; Cheng, Ji 1   VIAFID ORCID Logo  ; Wang, Jingde 1 ; Sun, Wei 1   VIAFID ORCID Logo 

 College of Chemical Engineering, Beijing University of Chemical Technology, North Third Ring Road 15, Chaoyang District, Beijing 100029, China; [email protected] (Y.L.); [email protected] (F.M.); [email protected] (C.J.) 
 College of Chemical Engineering, Beijing University of Chemical Technology, North Third Ring Road 15, Chaoyang District, Beijing 100029, China; [email protected] (Y.L.); [email protected] (F.M.); [email protected] (C.J.); Center of Process Monitoring and Data Analysis, Wuxi Research Institute of Applied Technologies, Tsinghua University, Wuxi 214072, China 
First page
122
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
22279717
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2621357374
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.