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Monitoring Framework Based on Generalized Tensor PCA for Three-Dimensional Batch Process Data
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DOI：10.1021/acs.iecr.9b06244.png)
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摘要
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Unfolding is a pretreating operation in most existing batch process modeling methods, but it would destroy the essential structure of the raw data. Also, the number of estimated parameters in the unfolding model is prohibitively large. In this work, a generalized tensor PCA (GTPCA) method is proposed for batch process monitoring. It can be performed on the 3-D batch process data directly to avoid the curse of dimensionality problem and potential information loss caused by data unfolding. Also, the uneven-length problem can be solved naturally without batch trajectory synchronization to prevent distorting data, which guarantees better quality models and monitoring performances. Moreover, within-batch detection can be easily performed using the tensor-based modeling scheme. The advantages and effectiveness of the proposed method are illustrated through a numerical case and an injection molding process in comparison with the traditional MPCA, PARAFAC, and Tucker3 methods.
Keyword:
PRINCIPAL COMPONENT ANALYSIS
DATA-DRIVEN
QUALITY PREDICTION
FAULT-DETECTION
MODEL
CHARTS
COMMON
期刊
I
IF:
3.9
论文数:
4.0W
被引数:
9.6W
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