Combination method of support vector machine and fisher discriminant analysis for chemical process fault diagnosis

Liling Ma; Zhao Zhang; Junzheng Wang

Combination method of support vector machine and fisher discriminant analysis for chemical process fault diagnosis

Liling Ma^*, Zhao Zhang, Junzheng Wang

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

For chemical process, a new fault diagnosis method based on multi-phases is presented to overcome its difficulty in nonlinear and non-uniform sample data. Support vector machine is first used for phase identification, and for each phase, fisher discriminant analysis is developed to analyze and recognize fault patterns. Variable weighted discriminant matrix and similarity measurement based on manifold distance are proposed to enhance the incremental clustering capability of FDA. The proposed method is applied to citric acid fermentation process, and the comparison results indicate that the proposed algorithm has better capability to classify fault samples as well as high diagnosis precision.

Original language	English
Title of host publication	Proceedings of the 29th Chinese Control Conference, CCC'10
Pages	4000-4003
Number of pages	4
Publication status	Published - 2010
Event	29th Chinese Control Conference, CCC'10 - Beijing, China Duration: 29 Jul 2010 → 31 Jul 2010

Publication series

Name	Proceedings of the 29th Chinese Control Conference, CCC'10

Conference

Conference	29th Chinese Control Conference, CCC'10
Country/Territory	China
City	Beijing
Period	29/07/10 → 31/07/10

Keywords

Chemical process
Fault diagnosis
Fisher discriminant analysis
Support vector machine

Cite this

@inproceedings{ec4cfc55c96f42168227ff3f79a64a7b,

title = "Combination method of support vector machine and fisher discriminant analysis for chemical process fault diagnosis",

abstract = "For chemical process, a new fault diagnosis method based on multi-phases is presented to overcome its difficulty in nonlinear and non-uniform sample data. Support vector machine is first used for phase identification, and for each phase, fisher discriminant analysis is developed to analyze and recognize fault patterns. Variable weighted discriminant matrix and similarity measurement based on manifold distance are proposed to enhance the incremental clustering capability of FDA. The proposed method is applied to citric acid fermentation process, and the comparison results indicate that the proposed algorithm has better capability to classify fault samples as well as high diagnosis precision.",

keywords = "Chemical process, Fault diagnosis, Fisher discriminant analysis, Support vector machine",

author = "Liling Ma and Zhao Zhang and Junzheng Wang",

year = "2010",

language = "English",

isbn = "9787894631046",

series = "Proceedings of the 29th Chinese Control Conference, CCC'10",

pages = "4000--4003",

booktitle = "Proceedings of the 29th Chinese Control Conference, CCC'10",

note = "29th Chinese Control Conference, CCC'10 ; Conference date: 29-07-2010 Through 31-07-2010",

}

Combination method of support vector machine and fisher discriminant analysis for chemical process fault diagnosis. / Ma, Liling; Zhang, Zhao; Wang, Junzheng.
Proceedings of the 29th Chinese Control Conference, CCC'10. 2010. p. 4000-4003 5572965 (Proceedings of the 29th Chinese Control Conference, CCC'10).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Combination method of support vector machine and fisher discriminant analysis for chemical process fault diagnosis

AU - Ma, Liling

AU - Zhang, Zhao

AU - Wang, Junzheng

PY - 2010

Y1 - 2010

N2 - For chemical process, a new fault diagnosis method based on multi-phases is presented to overcome its difficulty in nonlinear and non-uniform sample data. Support vector machine is first used for phase identification, and for each phase, fisher discriminant analysis is developed to analyze and recognize fault patterns. Variable weighted discriminant matrix and similarity measurement based on manifold distance are proposed to enhance the incremental clustering capability of FDA. The proposed method is applied to citric acid fermentation process, and the comparison results indicate that the proposed algorithm has better capability to classify fault samples as well as high diagnosis precision.

AB - For chemical process, a new fault diagnosis method based on multi-phases is presented to overcome its difficulty in nonlinear and non-uniform sample data. Support vector machine is first used for phase identification, and for each phase, fisher discriminant analysis is developed to analyze and recognize fault patterns. Variable weighted discriminant matrix and similarity measurement based on manifold distance are proposed to enhance the incremental clustering capability of FDA. The proposed method is applied to citric acid fermentation process, and the comparison results indicate that the proposed algorithm has better capability to classify fault samples as well as high diagnosis precision.

KW - Chemical process

KW - Fault diagnosis

KW - Fisher discriminant analysis

KW - Support vector machine

UR - http://www.scopus.com/inward/record.url?scp=78650232993&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:78650232993

SN - 9787894631046

T3 - Proceedings of the 29th Chinese Control Conference, CCC'10

SP - 4000

EP - 4003

BT - Proceedings of the 29th Chinese Control Conference, CCC'10

T2 - 29th Chinese Control Conference, CCC'10

Y2 - 29 July 2010 through 31 July 2010

ER -

Combination method of support vector machine and fisher discriminant analysis for chemical process fault diagnosis

Abstract

Publication series

Conference

Keywords

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