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dc.creatorEscobar Davidson, Leonardo
dc.creatorSucerquia Rincones, Stephany
dc.creatorHadechni Bonett, Samir
dc.creatorRamírez Parra, Jhon
dc.creatorColl Velásquez, Jean
dc.creatorBeleño Saenz, Kelvin
dc.creatorJiménez-Cabas, Javier
dc.creatorDíaz Saenz, Carlos
dc.description.abstractIndustrial processes play a key role in the production sector. Production demands have forced the search for strategies such as automatic diagnosis to maintain continuous production with minimized machine failures. An industrial process provides many measured, controlled, and manipulated variables that associate nonlinearities and uncertainties, so it is necessary to monitor them, to acquire information about the behavior of the process. Historical and present information resulting from monitoring is used to implement intelligent monitoring systems. Within the monitoring scheme is the detection of failures, diagnosis, and restoration of operating conditions according to process performance criteria [1].spa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.sourceTechnology Reports of Kansai Universityspa
dc.subjectControl systemsspa
dc.subjectCross correlationspa
dc.subjectCurve fittingspa
dc.titleNon-obtrusive stiction detection methods for control systemsspa
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dcterms.references[13] Borrero-Salazar, A. A., Cardenas-Cabrera, J. M., Barros-Gutierrez, D. A., & Jiménez-Cabas, J. A. (2019). A Comparison Study of Mpc Strategies Based on Minimum Variance Control Index
dcterms.references[14] Cardenas-Cabrera, J., Diaz-Charris, L., Torres-Carvajal, A., Castro-Charris, N., Romero-Fandiño, E., Ruiz Ariza, J. D., & Jiménez-Cabas, J. (2019). Model Predictive Control Strategies Performance Evaluation Over a Pipeline Transportation System. Journal of Control Science and Engineering,

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