Optimal variable selection for effective statistical process monitoring

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dc.contributor.author Ghosh, Kaushik
dc.contributor.author Ramteke, Manojkumar
dc.contributor.author Srinivasan, Rajagopalan
dc.date.accessioned 2014-03-18T18:42:52Z
dc.date.available 2014-03-18T18:42:52Z
dc.date.issued 2014-01
dc.identifier.citation Ghosha, Kaushik; Ramteke, Manojkumar and Srinivasan, Rajagopalan, "Optimal variable selection for effective statistical process monitoring", Computers & Chemical Engineering, DOI: 10.1016/j.compchemeng.2013.09.014, vol. 60, pp. 260-276, Jan. 2014. en_US
dc.identifier.issn 0098-1354
dc.identifier.uri http://dx.doi.org/10.1016/j.compchemeng.2013.09.014
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/924
dc.description.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. en_US
dc.description.statementofresponsibility by Kaushik Ghosh, Manojkumar Ramteke and Rajagopalan Srinivasan
dc.format.extent Vol. 60, pp. 260–276
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.subject Fault detection en_US
dc.subject Optimization en_US
dc.subject Process control en_US
dc.subject Safety en_US
dc.subject Systems engineeringq en_US
dc.subject Tennessee Eastman Process en_US
dc.title Optimal variable selection for effective statistical process monitoring en_US
dc.type Article en_US
dc.relation.journal Computers & Chemical Engineering


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