Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.compchemeng.2013.09.014
Title: Optimal variable selection for effective statistical process monitoring
Authors: Ghosh, K.
Ramteke, M.
Srinivasan, R. 
Keywords: Fault detection
Optimization
Process control
Safety
Systems engineering
Tennessee Eastman Process
Issue Date: 10-Jan-2014
Source: Ghosh, K., Ramteke, M., Srinivasan, R. (2014-01-10). Optimal variable selection for effective statistical process monitoring. Computers and Chemical Engineering 60 : 260-276. ScholarBank@NUS Repository. https://doi.org/10.1016/j.compchemeng.2013.09.014
Abstract: In a typical large-scale chemical process, hundreds of variables are measured. Since statistical process monitoring techniques typically involve dimensionality reduction, all measured variables are often provided as input without weeding out variables. Here, we demonstrate that incorporating measured variables that do not provide any additional information about faults degrades monitoring performance. We propose a stochastic optimization-based method to identify an optimal subset of measured variables for process monitoring. The benefits of the reduced monitoring model in terms of improved false alarm rate, missed detection rate, and detection delay is demonstrated through PCA based monitoring of the benchmark Tennessee Eastman Challenge problem. © 2013 Elsevier Ltd.
Source Title: Computers and Chemical Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/64344
ISSN: 00981354
DOI: 10.1016/j.compchemeng.2013.09.014
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