Please use this identifier to cite or link to this item:
|Title:||Optimal variable selection for effective statistical process monitoring|
Tennessee Eastman Process
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 8, 2018
WEB OF SCIENCETM
checked on Feb 12, 2018
checked on Mar 12, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.