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• W2905153436 abstract "To obtain the informative enough data from the modern chemical processes, various sensors and measuring instruments have been applied. Among them, both the online measurements and offline laboratory analyser results are collected and used for multivariate statistical process monitoring purpose. Hence, it indicates that the obtained measurements indeed contain different sampling rates due to the different demands of the control systems and the equipment constraints. To handle the multi-rate process monitoring problem, it is desirable to effectively integrate the measurements with different sampling rates. In this paper, a multiple probability principal component analysis (MPPCA) model is proposed for efficient collection of data and the improvement of the performance on model prediction and process monitoring. In the proposed model, it has been derived as a general form for any multi-rate systems and the model parameters are calibrated by the expectation–maximization algorithm. Based on it, several statistics are constructed for different sampling rates in the process monitoring scenario. Finally, two case studies are presented to show the effectiveness of the proposed method." @default.
• W2905153436 created "2018-12-22" @default.
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• W2905153436 date "2019-03-01" @default.
• W2905153436 modified "2023-10-18" @default.
• W2905153436 title "Multiple probability principal component analysis for process monitoring with multi-rate measurements" @default.
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• W2905153436 doi "https://doi.org/10.1016/j.jtice.2018.11.002" @default.
• W2905153436 hasPublicationYear "2019" @default.
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