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• W2755549892 abstract "To reduce data-storage costs and enhance high accuracy of industrial process fault detection, a data driven fault diagnosis method is proposed based on diffusion maps and hidden Markov model. Firstly, the correlation dimension of sample data is calculated. Secondly, the high-dimensional eigenvectors are extracted into low-dimensional manifold space by diffusion maps. Finally, the low-dimensional eigenvectors are regarded as the observation sequence of hidden Markov model for fault pattern recognition. The effectiveness of this new method is proved by TE process simulator. In addition, the fault identification results are demonstrated to be superior to the traditional identification approach of hidden Markov model." @default.
• W2755549892 created "2017-09-25" @default.
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• W2755549892 date "2017-07-01" @default.
• W2755549892 modified "2023-10-01" @default.
• W2755549892 title "Fault diagnosis method based on diffusion maps and hidden Markov model for TE process" @default.
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• W2755549892 doi "https://doi.org/10.23919/chicc.2017.8028501" @default.
• W2755549892 hasPublicationYear "2017" @default.
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