Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 49, Issue 1, Pages 198-210Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2017.2771229
Keywords
Data-driven; mixture of probabilistic principal component analysis (MPPCA); nonlinear systems; process monitoring
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Funding
- Chunmiao Project of Haixi Institute of Chinese Academy of Sciences [CMZX-2016-005]
- National Natural Science Foundation of China [61603369, 61703388]
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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 autosus-pension model are employed to demonstrate the effectiveness of the proposed scheme further.
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