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Modified kernel principal component analysis based on local structure analysis and its application to nonlinear process fault diagnosis

Modified kernel principal component analysis based on local structure analysis and its application to nonlinear process fault diagnosis
Modified kernel principal component analysis based on local structure analysis and its application to nonlinear process fault diagnosis
Traditional kernel principal component analysis (KPCA) concentrates on the global structure analysis of data sets but omits the local information which is also important for process monitoring and fault diagnosis. In this paper, a modified KPCA, referred to as the local KPCA (LKPCA), is proposed based on local structure analysis for nonlinear process fault diagnosis. In order to extract data feature better, local structure analysis is integrated within the KPCA, and this results in a new optimisation objective which naturally involves both global and local structure information. With the application of usual kernel trick, the optimisation problem is transformed into a generalised eigenvalue decomposition on the kernel matrix. For the purpose of fault detection, two monitoring statistics, known as the T2 and Q statistics, are built based on the LKPCA model and confidence limit is computed by kernel density estimation. In order to identify fault variables, contribution plots for monitoring statistics are constructed based on the idea of sensitivity analysis to locate the fault variables. Simulation using the Tennessee Eastman benchmark process shows that the proposed method outperforms the traditional KPCA, in terms of fault detection performance. The results obtained also demonstrate the potential of the proposed fault identification approach.
kernel principal component analysis, local structure analysis, fault diagnosis, fault detection, fault identification, nonlinear process
195-209
Deng, Xiaogang
c95b981b-d71c-4058-9e29-03cecca6003f
Tian, Xuemin
5b7f2306-69c1-41c7-8cab-49932ac1ae01
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Deng, Xiaogang
c95b981b-d71c-4058-9e29-03cecca6003f
Tian, Xuemin
5b7f2306-69c1-41c7-8cab-49932ac1ae01
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Deng, Xiaogang, Tian, Xuemin and Chen, Sheng (2013) Modified kernel principal component analysis based on local structure analysis and its application to nonlinear process fault diagnosis. Chemometrics and Intelligent Laboratory Systems, 127, 195-209. (doi:10.1016/j.chemolab.2013.07.001).

Record type: Article

Abstract

Traditional kernel principal component analysis (KPCA) concentrates on the global structure analysis of data sets but omits the local information which is also important for process monitoring and fault diagnosis. In this paper, a modified KPCA, referred to as the local KPCA (LKPCA), is proposed based on local structure analysis for nonlinear process fault diagnosis. In order to extract data feature better, local structure analysis is integrated within the KPCA, and this results in a new optimisation objective which naturally involves both global and local structure information. With the application of usual kernel trick, the optimisation problem is transformed into a generalised eigenvalue decomposition on the kernel matrix. For the purpose of fault detection, two monitoring statistics, known as the T2 and Q statistics, are built based on the LKPCA model and confidence limit is computed by kernel density estimation. In order to identify fault variables, contribution plots for monitoring statistics are constructed based on the idea of sensitivity analysis to locate the fault variables. Simulation using the Tennessee Eastman benchmark process shows that the proposed method outperforms the traditional KPCA, in terms of fault detection performance. The results obtained also demonstrate the potential of the proposed fault identification approach.

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Published date: 15 August 2013
Keywords: kernel principal component analysis, local structure analysis, fault diagnosis, fault detection, fault identification, nonlinear process
Organisations: Southampton Wireless Group

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Local EPrints ID: 355324
URI: http://eprints.soton.ac.uk/id/eprint/355324
PURE UUID: d16efe09-e4aa-492a-845a-62ded8a124f2

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Date deposited: 20 Aug 2013 09:24
Last modified: 25 Nov 2019 20:47

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