A kernel-based bayesian classifier for fault detection and classification

Chunmei Yu; Quan Pan; Yongmei Cheng; Hongcai Zhang

doi:10.1109/WCICA.2008.4592910

A kernel-based bayesian classifier for fault detection and classification

Chunmei Yu, Quan Pan, Yongmei Cheng, Hongcai Zhang

School of Automation

Northwestern Polytechnical University Xian

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

3 Scopus citations

Abstract

A kernel constructed by Shannon sampling function was utilized for kernel Fisher discriminant analysis (KFDA). And kernel-based Bayesian decision function was implemented for fault detection. Simultaneously, Bhattacharyya distance was introduced as a criterion function for separability comparison. The proposed Shannon KFDA was compared with Gaussian KFDA on Tennessee Eastman Process (TEP) data. The results show that Shannon KFDA has lager Bhattacharyya distance and detects more faults more quickly than Gaussian KFDA.

Original language	English
Title of host publication	Proceedings of the 7th World Congress on Intelligent Control and Automation, WCICA'08
Pages	124-128
Number of pages	5
DOIs	https://doi.org/10.1109/WCICA.2008.4592910
State	Published - 2008
Event	7th World Congress on Intelligent Control and Automation, WCICA'08 - Chongqing, China Duration: 25 Jun 2008 → 27 Jun 2008

Publication series

Name	Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

Conference

Conference	7th World Congress on Intelligent Control and Automation, WCICA'08
Country/Territory	China
City	Chongqing
Period	25/06/08 → 27/06/08

Keywords

Fault detection
Kernel fisher discriminant analysis
Kernel function construction
Kernel-based bayesian decision function

Access to Document

10.1109/WCICA.2008.4592910

Cite this

@inproceedings{5b0596e8092a40b5a49dbc52481161b2,

title = "A kernel-based bayesian classifier for fault detection and classification",

abstract = "A kernel constructed by Shannon sampling function was utilized for kernel Fisher discriminant analysis (KFDA). And kernel-based Bayesian decision function was implemented for fault detection. Simultaneously, Bhattacharyya distance was introduced as a criterion function for separability comparison. The proposed Shannon KFDA was compared with Gaussian KFDA on Tennessee Eastman Process (TEP) data. The results show that Shannon KFDA has lager Bhattacharyya distance and detects more faults more quickly than Gaussian KFDA.",

keywords = "Fault detection, Kernel fisher discriminant analysis, Kernel function construction, Kernel-based bayesian decision function",

author = "Chunmei Yu and Quan Pan and Yongmei Cheng and Hongcai Zhang",

year = "2008",

doi = "10.1109/WCICA.2008.4592910",

language = "英语",

isbn = "9781424421145",

series = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",

pages = "124--128",

booktitle = "Proceedings of the 7th World Congress on Intelligent Control and Automation, WCICA'08",

note = "7th World Congress on Intelligent Control and Automation, WCICA'08 ; Conference date: 25-06-2008 Through 27-06-2008",

}

Yu, C, Pan, Q , Cheng, Y & Zhang, H 2008, A kernel-based bayesian classifier for fault detection and classification. in Proceedings of the 7th World Congress on Intelligent Control and Automation, WCICA'08., 4592910, Proceedings of the World Congress on Intelligent Control and Automation (WCICA), pp. 124-128, 7th World Congress on Intelligent Control and Automation, WCICA'08, Chongqing, China, 25/06/08. https://doi.org/10.1109/WCICA.2008.4592910

A kernel-based bayesian classifier for fault detection and classification. / Yu, Chunmei; Pan, Quan ; Cheng, Yongmei et al.
Proceedings of the 7th World Congress on Intelligent Control and Automation, WCICA'08. 2008. p. 124-128 4592910 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA)).

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

TY - GEN

T1 - A kernel-based bayesian classifier for fault detection and classification

AU - Yu, Chunmei

AU - Pan, Quan

AU - Cheng, Yongmei

AU - Zhang, Hongcai

PY - 2008

Y1 - 2008

N2 - A kernel constructed by Shannon sampling function was utilized for kernel Fisher discriminant analysis (KFDA). And kernel-based Bayesian decision function was implemented for fault detection. Simultaneously, Bhattacharyya distance was introduced as a criterion function for separability comparison. The proposed Shannon KFDA was compared with Gaussian KFDA on Tennessee Eastman Process (TEP) data. The results show that Shannon KFDA has lager Bhattacharyya distance and detects more faults more quickly than Gaussian KFDA.

AB - A kernel constructed by Shannon sampling function was utilized for kernel Fisher discriminant analysis (KFDA). And kernel-based Bayesian decision function was implemented for fault detection. Simultaneously, Bhattacharyya distance was introduced as a criterion function for separability comparison. The proposed Shannon KFDA was compared with Gaussian KFDA on Tennessee Eastman Process (TEP) data. The results show that Shannon KFDA has lager Bhattacharyya distance and detects more faults more quickly than Gaussian KFDA.

KW - Fault detection

KW - Kernel fisher discriminant analysis

KW - Kernel function construction

KW - Kernel-based bayesian decision function

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U2 - 10.1109/WCICA.2008.4592910

DO - 10.1109/WCICA.2008.4592910

M3 - 会议稿件

AN - SCOPUS:52149083010

SN - 9781424421145

T3 - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

SP - 124

EP - 128

BT - Proceedings of the 7th World Congress on Intelligent Control and Automation, WCICA'08

T2 - 7th World Congress on Intelligent Control and Automation, WCICA'08

Y2 - 25 June 2008 through 27 June 2008

ER -

A kernel-based bayesian classifier for fault detection and classification

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Conference

Keywords

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