[1] Zhang Xi, Yan Weiwu, Zhao Xu, Shao Huihe, et al. Nonlinear online process monitoring and fault diagnosisof condenser based on kernel PCA plus FDA [J]. Journal of Southeast University (English Edition), 2007, 23 (1): 51-56. [doi:10.3969/j.issn.1003-7985.2007.01.012]

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Nonlinear online process monitoring and fault diagnosisof condenser based on kernel PCA plus FDA()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

23

Issue:

2007 1

Page:

51-56

Research Field:

Energy and Power Engineering

Publishing date:

2007-03-30

Info

Title:

Nonlinear online process monitoring and fault diagnosisof condenser based on kernel PCA plus FDA

Author(s):

Zhang Xi;  Yan Weiwu;  Zhao Xu;  Shao Huihe

Department of Automation, Shanghai Jiaotong University, Shanghai 200040, China

Keywords:

nonlinear;  kernel PCA;  FDA;  process monitoring;  fault diagnosis;  condenser

PACS:

TK267;TP277

DOI:

10.3969/j.issn.1003-7985.2007.01.012

Abstract:

A novel online process monitoring and fault diagnosis method of condenser based on kernel principle component analysis(KPCA)and Fisher discriminant analysis(FDA)is presented. The basic idea of this method is: First map data from the original space into high-dimensional feature space via nonlinear kernel function and then extract optimal feature vector and discriminant vector in feature space and calculate the Euclidean distance between feature vectors to perform process monitoring. Similar degree between the present discriminant vector and optimal discriminant vector of fault in historical dataset is used for diagnosis. The proposed method can effectively capture the nonlinear relationship among process variables. Simulating results of the turbo generator’s fault data set prove that the proposed method is effective.

References:

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Memo

Memo:

Biographies: Zhang Xi(1974—), male, graduate; Shao Huihe(corresponding author), male, professor, HHshao@sjtu.edu.cn.

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