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|Title:||Comparative study of PCA approaches in process monitoring and fault detection||Authors:||Tien, D.X.
|Issue Date:||2004||Citation:||Tien, D.X.,Lim, K.-W.,Jun, L. (2004). Comparative study of PCA approaches in process monitoring and fault detection. IECON Proceedings (Industrial Electronics Conference) 3 : 2594-2599. ScholarBank@NUS Repository. https://doi.org/10.1109/IECON.2004.1432212||Abstract:||This paper suggests an alternative scaling approach to PCA analysis for monitoring industrial processes. It also compares performance of the proposed moving PCA (MPCA) and three other PCA-based approaches including conventional PCA, adaptive PCA and exponentially weighted PCA, on a well known simulation model of an industrial plant and on data obtained from a petrochemical plant over a period of X months. The result showed that MPCA, which uses the mean and standard deviation of a moving window for scaling purpose, appeared to outperform the other three methods in monitoring processes with/without changes in operating conditions/set-points. While a conventional PCA seemed to work satisfactorily with the Tennessee Eastman Process (TEP) simulation, its performance was much poorer on the industrial data set. This comparison demonstrates that a degree of adaptation in scaling parameters is necessary for PCA-based approaches, especially for processes with multi operating modes. © 2004 IEEE.||Source Title:||IECON Proceedings (Industrial Electronics Conference)||URI:||http://scholarbank.nus.edu.sg/handle/10635/69650||DOI:||10.1109/IECON.2004.1432212|
|Appears in Collections:||Staff Publications|
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