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Fault discriminant enhanced kernel principal component analysis incorporating prior fault information for monitoring nonlinear processes

Fault discriminant enhanced kernel principal component analysis incorporating prior fault information for monitoring nonlinear processes
Fault discriminant enhanced kernel principal component analysis incorporating prior fault information for monitoring nonlinear processes
Kernel principal component analysis (KPCA) based fault detection method, whose statistical model only utilizes normal operating data and ignores available prior fault information, may not provide the best fault detection performance for nonlinear process monitoring. In order to exploit available prior fault data to enhance fault detection performance, a fault discriminant enhanced KPCA (FDKPCA) method is proposed, which simultaneously monitors two types of data features, nonlinear kernel principal components (KPCs) and fault discriminant components (FDCs). More specifically, based on the normal operating data, KPCs are extracted by usual KPCA modeling, while with the normal operating data and prior fault data, FDCs are obtained by the kernel local-nonlocal preserving discriminant analysis (KLNPDA). Monitoring statistics are constructed for both the KPCA and KLNPDA sub-models. Moreover, Bayesian inference is employed to transform the corresponding monitoring statistics into fault probabilities, and the overall probability-based monitoring statistics are constructed by weighting the results of the two sub-models, which provides more effective on-line fault detection capability. Two case studies involving a simulated nonlinear system and a continuous stirred tank reactor demonstrate the superior fault detection performance of the proposed KLNPDA scheme over the traditional KPCA method.
21-34
Deng, Xiaogang
c95b981b-d71c-4058-9e29-03cecca6003f
Tian, Xuemin
5b7f2306-69c1-41c7-8cab-49932ac1ae01
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Christopher
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Deng, Xiaogang
c95b981b-d71c-4058-9e29-03cecca6003f
Tian, Xuemin
5b7f2306-69c1-41c7-8cab-49932ac1ae01
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Christopher
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Deng, Xiaogang, Tian, Xuemin, Chen, Sheng and Harris, Christopher (2017) Fault discriminant enhanced kernel principal component analysis incorporating prior fault information for monitoring nonlinear processes. Chemometrics and Intelligent Laboratory Systems, 162, 21-34. (doi:10.1016/j.chemolab.2017.01.001).

Record type: Article

Abstract

Kernel principal component analysis (KPCA) based fault detection method, whose statistical model only utilizes normal operating data and ignores available prior fault information, may not provide the best fault detection performance for nonlinear process monitoring. In order to exploit available prior fault data to enhance fault detection performance, a fault discriminant enhanced KPCA (FDKPCA) method is proposed, which simultaneously monitors two types of data features, nonlinear kernel principal components (KPCs) and fault discriminant components (FDCs). More specifically, based on the normal operating data, KPCs are extracted by usual KPCA modeling, while with the normal operating data and prior fault data, FDCs are obtained by the kernel local-nonlocal preserving discriminant analysis (KLNPDA). Monitoring statistics are constructed for both the KPCA and KLNPDA sub-models. Moreover, Bayesian inference is employed to transform the corresponding monitoring statistics into fault probabilities, and the overall probability-based monitoring statistics are constructed by weighting the results of the two sub-models, which provides more effective on-line fault detection capability. Two case studies involving a simulated nonlinear system and a continuous stirred tank reactor demonstrate the superior fault detection performance of the proposed KLNPDA scheme over the traditional KPCA method.

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Accepted/In Press date: 5 January 2017
e-pub ahead of print date: 6 January 2017
Published date: 15 March 2017
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 404721
URI: http://eprints.soton.ac.uk/id/eprint/404721
PURE UUID: 264eb403-2b67-4f0b-b3ab-bb992d27d4e9

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Date deposited: 20 Jan 2017 14:51
Last modified: 07 Oct 2020 04:39

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