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Deep principal component analysis based on layerwise feature extraction and its application to nonlinear process monitoring

Deep principal component analysis based on layerwise feature extraction and its application to nonlinear process monitoring
Deep principal component analysis based on layerwise feature extraction and its application to nonlinear process monitoring

In order to deeply exploit intrinsic data feature information hidden among the process data, an improved kernel principal component analysis (KPCA) method is proposed, which is referred to as deep principal component analysis (DePCA). Specifically, motivated by the deep learning strategy, we design a hierarchical statistical model structure to extract multilayer data features, including both the linear and nonlinear principal components. To reduce the computation complexity in nonlinear feature extraction, the feature-samples' selection technique is applied to build the sparse kernel model for DePCA. To integrate the monitoring statistics at each feature layer, Bayesian inference is used to transform the monitoring statistics into fault probabilities, and then, two probability-based DePCA monitoring statistics are constructed by weighting the fault probabilities at all the feature layers. Two case studies involving a simulated nonlinear system and the benchmark Tennessee Eastman process demonstrate the superior fault detection performance of the proposed DePCA method over the traditional KPCA-based methods.

Bayesian inference, Computational modeling, deep learning, Eigenvalues and eigenfunctions, Feature extraction, Kernel, kernel principal component analysis (KPCA), Machine learning, Monitoring, nonlinear process monitoring., Principal component analysis
1063-6536
2526-2540
Deng, Xiaogang
c95b981b-d71c-4058-9e29-03cecca6003f
Tian, Xuemin
5b7f2306-69c1-41c7-8cab-49932ac1ae01
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Chris J.
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, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Deng, Xiaogang, Tian, Xuemin, Chen, Sheng and Harris, Chris J. (2019) Deep principal component analysis based on layerwise feature extraction and its application to nonlinear process monitoring. IEEE Transactions on Control Systems Technology, 27 (6), 2526-2540. (doi:10.1109/TCST.2018.2865413).

Record type: Article

Abstract

In order to deeply exploit intrinsic data feature information hidden among the process data, an improved kernel principal component analysis (KPCA) method is proposed, which is referred to as deep principal component analysis (DePCA). Specifically, motivated by the deep learning strategy, we design a hierarchical statistical model structure to extract multilayer data features, including both the linear and nonlinear principal components. To reduce the computation complexity in nonlinear feature extraction, the feature-samples' selection technique is applied to build the sparse kernel model for DePCA. To integrate the monitoring statistics at each feature layer, Bayesian inference is used to transform the monitoring statistics into fault probabilities, and then, two probability-based DePCA monitoring statistics are constructed by weighting the fault probabilities at all the feature layers. Two case studies involving a simulated nonlinear system and the benchmark Tennessee Eastman process demonstrate the superior fault detection performance of the proposed DePCA method over the traditional KPCA-based methods.

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Accepted/In Press date: 8 August 2018
e-pub ahead of print date: 6 September 2018
Published date: 9 October 2019
Keywords: Bayesian inference, Computational modeling, deep learning, Eigenvalues and eigenfunctions, Feature extraction, Kernel, kernel principal component analysis (KPCA), Machine learning, Monitoring, nonlinear process monitoring., Principal component analysis

Identifiers

Local EPrints ID: 426912
URI: http://eprints.soton.ac.uk/id/eprint/426912
ISSN: 1063-6536
PURE UUID: a489d56c-d458-4be7-8038-0ce15dd785bf

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Date deposited: 14 Dec 2018 17:30
Last modified: 17 Feb 2020 17:32

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