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Data-Driven Batch-End Quality Modeling and Monitoring Based on Optimized Sparse Partial Least Squares.
- Source :
-
IEEE Transactions on Industrial Electronics . May2020, Vol. 67 Issue 5, p4098-4107. 10p. - Publication Year :
- 2020
-
Abstract
- Batch-end quality modeling is used to predict the quality by using batch measurements and generally involves a large number of predictor variables. However, not all of the variables are beneficial for the prediction. Conventional multiway partial least squares (PLS) may not function properly for batch-end quality modeling because of many irrelevant predictor variables. This paper proposes an optimized sparse PLS (OSPLS) modeling approach for simultaneous batch-end quality prediction and relevant-variable selection. The effect of irrelevant variables on the quality-prediction performance is analyzed, and the importance of the relevant-variable selection is emphasized. Then, an OSPLS batch-end quality modeling approach is developed by incorporating the variable resolution optimization and sparse PLS modeling. The quality-prediction accuracy and modeling interpretability are improved because only quality-relevant variables are selected, and quality-irrelevant variables are eliminated. Based on the selected quality-relevant variables, a statistic is established for monitoring the quality status. The proposed OSPLS-based modeling and monitoring approach is applied on a fed-batch penicillin fermentation process and an industrial injection molding process. The results are compared with the state-of-the-art methods to verify the effectiveness of the OSPLS approach. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02780046
- Volume :
- 67
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Industrial Electronics
- Publication Type :
- Academic Journal
- Accession number :
- 141599640
- Full Text :
- https://doi.org/10.1109/TIE.2019.2922941