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An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring

Zhang, J., Chen, H., Chen, S. and Hong, X. (2019) An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring. IEEE Transactions on Cybernetics, 49 (1). pp. 198-210. ISSN 2168-2267

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To link to this item DOI: 10.1109/TCYB.2017.2771229

Abstract/Summary

An improved mixture of probabilistic principal component analysis (PPCA) has been introduced for nonlinear data-driven process monitoring in this paper. To realize this purpose, the technique of a mixture of probabilistic principal component analyzers is utilized to establish the model of the underlying nonlinear process with local PPCA models, where a novel composite monitoring statistic is proposed based on the integration of two monitoring statistics in modified PPCA-based fault detection approach. Besides, the weighted mean of the monitoring statistics aforementioned is utilized as a metrics to detect potential abnormalities. The virtues of the proposed algorithm are discussed in comparison with several unsupervised algorithms. Finally, Tennessee Eastman process and an autosuspension model are employed to demonstrate the effectiveness of the proposed scheme further.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:74380
Publisher:IEEE Systems, Man, and Cybernetics Society

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