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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.date.accessioned2021-03-12T17:59:44Z
dc.date.available2021-03-12T17:59:44Z
dc.date.issued2020-08
dc.identifier.issn0453-2198
dc.identifier.urihttps://hdl.handle.net/11323/8000
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.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.sourceTechnology Reports of Kansai Universityspa
dc.subjectControl systemsspa
dc.subjectHysteresisspa
dc.subjectCross correlationspa
dc.subjectNon-linearityspa
dc.subjectCurve fittingspa
dc.titleNon-obtrusive stiction detection methods for control systemsspa
dc.typearticlespa
dcterms.references[1] Thornhill, nf and horch, a. (2007). Advances and new directions in the detection and diagnosis of disturbances throughout the plant. Control engineering practice, 15 (10), 1196-1206.spa
dcterms.references[2] C. Pryor, "autocovariance and power spectrum analysis derive new information from process data.," control eng, vol. V 29, no. N 11, pp. 103–106, 1982.spa
dcterms.references[3] Thornhill, nf and hugglund, t. (1997). Oscillation detection and diagnostics in control loops. Control engineering practice, 5(10), 1343-1354.spa
dcterms.references[4] Verification, validation, and testing: MATLAB and Simulink solutions. (s. F.). MATLAB & Simulink. Retrieved 10 March 2020, from https://la.mathworks.com/solutions/verification-validation.html.spa
dcterms.references[5] M. Jelali, control performance management in industrial automation. London: springer, 2013.spa
dcterms.references[6] Escobar Davidson, L, Rincones Sucerquia, S.S, Diaz Sáenz, c., & Jiménez Cabas, j. (2020, May). Computational tool for the detection and diagnosis of oscillations in a control system. Universidad Autonoma Del Caribe.spa
dcterms.references[7] Tarantino R., Szigeti F., Colina E., (2000) “Generalized Luenberger Observer-Based Fault-Detection Filter Desing: An Industrial Application”. Control Engineering Practice. Julio, pp. 665-671.spa
dcterms.references[8] Thornhill, nf and horch, a. (2007). Advances and new directions in the detection and diagnosis of disturbances throughout the plant. Control engineering practice, 15 (10), 1196-1206.spa
dcterms.references[9] N. F. Thornhill and t. H'gglund, "detection and diagnosis of oscillation in control loops," control eng. Pract., vol. 5, no. 10, pp. 1343–1354, 1997.spa
dcterms.references[10] E. Naghoos, "oscillation detection and causality analysis of control systems", era.library.ualberta.ca, 2016. [online]. Available: https://era.library.ualberta.ca/items/57ba6990-7ddc-4b58-8555- b9ea5ec4b79d/view/b06afee3-316c-48e6-b8b6-f08c3d7a9ae4/naghoosi_elham_201607_phd.pdf. [accessed: 31- aug- 2019].spa
dcterms.references[11] Jelali, M. (2012). Control Performance Management in Industrial Automation Assessment, Diagnosis, and Improvement of Control Loop Performance. 1st ed. [eBook] Available at: https://www.springer.com/gp/book/9781447145455.spa
dcterms.references[12] Takahashi, s., tachibana, k., & saito, t. (1991). U.s. patent no. 5,043,862. Washington, dc: u.s. patent and trademark office.spa
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 Performance.spa
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, 2019.spa
dc.source.urlhttps://www.researchgate.net/publication/345259859_Non-Obtrusive_Stiction_Detection_Methods_for_Control_Systems/link/5fa1dc81458515b7cfb9df90/downloadspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.type.hasversioninfo:eu-repo/semantics/publishedVersionspa


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