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

Qinqin ZHU; Qiang LIU; S. Joe QIN

doi:10.23919/ACC.2017.7963795

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 Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-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 language	English
Title of host publication	2017 American Control Conference, ACC 2017
Publisher	Institute of Electrical and Electronics Engineers
Pages	5404-5409
Number of pages	6
ISBN (Electronic)	9781509059928
ISBN (Print)	9781509045839
DOIs	https://doi.org/10.23919/ACC.2017.7963795
Publication status	Published - May 2017
Externally published	Yes
Event	2017 American Control Conference, ACC 2017 - Seattle, United States Duration: 24 May 2017 → 26 May 2017

Publication series

Name	Proceedings of the American Control Conference
Publisher	Institute of Electrical and Electronics Engineers
ISSN (Print)	0743-1619
ISSN (Electronic)	2378-5861

Conference

Conference	2017 American Control Conference, ACC 2017
Country/Territory	United States
City	Seattle
Period	24/05/17 → 26/05/17

Bibliographical note

Publisher Copyright:
© 2017 American Automatic Control Council (AACC).

Access to Document

10.23919/ACC.2017.7963795

Cite this

ZHU, Q., LIU, Q., & QIN, S. J. (2017). Quality-Relevant Fault Detection of Nonlinear Processes based on Kernel Concurrent Canonical Correlation Analysis. In 2017 American Control Conference, ACC 2017 (pp. 5404-5409). Article 7963795 (Proceedings of the American Control Conference). Institute of Electrical and Electronics Engineers. https://doi.org/10.23919/ACC.2017.7963795

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title = "Quality-Relevant Fault Detection of Nonlinear Processes based on Kernel Concurrent Canonical Correlation Analysis",

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.",

author = "Qinqin ZHU and Qiang LIU and QIN, \{S. Joe\}",

note = "Publisher Copyright: {\textcopyright} 2017 American Automatic Control Council (AACC).; 2017 American Control Conference, ACC 2017 ; Conference date: 24-05-2017 Through 26-05-2017",

year = "2017",

month = may,

doi = "10.23919/ACC.2017.7963795",

language = "English",

isbn = "9781509045839",

series = "Proceedings of the American Control Conference",

publisher = "Institute of Electrical and Electronics Engineers",

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ZHU, Q, LIU, Q & QIN, SJ 2017, Quality-Relevant Fault Detection of Nonlinear Processes based on Kernel Concurrent Canonical Correlation Analysis. in 2017 American Control Conference, ACC 2017., 7963795, Proceedings of the American Control Conference, Institute of Electrical and Electronics Engineers, pp. 5404-5409, 2017 American Control Conference, ACC 2017, Seattle, Washington, United States, 24/05/17. https://doi.org/10.23919/ACC.2017.7963795

Quality-Relevant Fault Detection of Nonlinear Processes based on Kernel Concurrent Canonical Correlation Analysis. / ZHU, Qinqin; LIU, Qiang; QIN, S. Joe.
2017 American Control Conference, ACC 2017. Institute of Electrical and Electronics Engineers, 2017. p. 5404-5409 7963795 (Proceedings of the American Control Conference).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

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T1 - Quality-Relevant Fault Detection of Nonlinear Processes based on Kernel Concurrent Canonical Correlation Analysis

AU - ZHU, Qinqin

AU - LIU, Qiang

AU - QIN, S. Joe

PY - 2017/5

Y1 - 2017/5

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85027051459

U2 - 10.23919/ACC.2017.7963795

DO - 10.23919/ACC.2017.7963795

M3 - Conference paper (refereed)

SN - 9781509045839

T3 - Proceedings of the American Control Conference

SP - 5404

EP - 5409

BT - 2017 American Control Conference, ACC 2017

PB - Institute of Electrical and Electronics Engineers

T2 - 2017 American Control Conference, ACC 2017

Y2 - 24 May 2017 through 26 May 2017

ER -

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

Abstract

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