Quality-Relevant Fault Detection of Nonlinear Processes based on Kernel Concurrent Canonical Correlation Analysis

Qinqin ZHU, Qiang LIU, S. Joe QIN

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

16 Citations (Scopus)

Abstract

Canonical correlation analysis (CCA) has been used for concurrent quality and process monitoring to extract multidimensional correlation structure between process and quality variables. In this paper, a new kernel concurrent CCA (KCCCA) algorithm is proposed for quality-relevant nonlinear process monitoring, which decomposes the original space into five subspaces, including correlation subspace, quality-principal subspace, quality-residual subspace, process-principal subspace and process-residual subspace. The proposed KCCCA considers the nonlinearity in both process and quality variables, and incorporates a regularization term as well for numerical robustness. In the case studies, the Tennessee Eastman process is employed to demonstrate the effectiveness of the proposed KCCCA.
Original languageEnglish
Title of host publication2017 American Control Conference (ACC)
PublisherInstitute of Electrical and Electronics Engineers
Pages5404-5409
Number of pages6
ISBN (Electronic)9781509059928
ISBN (Print)9781509045839
DOIs
Publication statusPublished - May 2017
Externally publishedYes
Event2017 American Control Conference, ACC 2017 - Seattle, United States
Duration: 24 May 201726 May 2017

Publication series

NameProceedings of the American Control Conference
PublisherInstitute of Electrical and Electronics Engineers
ISSN (Print)0743-1619
ISSN (Electronic)2378-5861

Conference

Conference2017 American Control Conference, ACC 2017
Country/TerritoryUnited States
CitySeattle
Period24/05/1726/05/17

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