Stochastic performance indices to infer deterministic indices through machine learning in the performance analysis of control loops

dc.creatorGómez Múnera, John Anderson
dc.creatorDíaz-Charris, Luis
dc.creatorRuiz Ariza, José David
dc.creatorCárdenas-Cabrera, Jorge
dc.creatorRo-mero, Elena
dc.creatorJiménez-Cabas, Javier
dc.date2021-09-23T13:55:23Z
dc.date2021-09-23T13:55:23Z
dc.date2021
dc.date.accessioned2023-10-03T18:55:14Z
dc.date.available2023-10-03T18:55:14Z
dc.identifier1000-0992
dc.identifierhttps://hdl.handle.net/11323/8746
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9166124
dc.descriptionControl loops are the most critical components in many production processes. In this process, the economic yield is strongly linked to the performance of the control loops since aspects such as safety conditions, process quality, and energy and raw material consumption depend on this. However, experience has shown that most of the control loops can be improved by identifying and correcting the causes of the poor perfor-mance. The indices to evaluate the performance of the control loops can be divided into two groups, stochastic and deterministic. The most known of the former is the minimum variance index. Stochastic indices only require data collected under normal operating conditions and minimum knowledge of the process, making it possible to evaluate performance online. However, some disadvantages, such as scale and span problems, make performance analysis difficult. The deterministic indices (rise time, settling time, overshoot, phase and gain margins, etc.) are easy to interpret, facilitating the analysis; however, invasive plant tests are necessary to estimate them, making them impractical. Is it possible to link these two approaches? With that question in mind, in this work, it is proposed to build a model to estimate deterministic indices (to evaluate robustness and performance of control loops), considering stochastic indices and some process information as model inputs. This paper shows the procedure to build the inferential model by using machine learning techniques.
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherCorporación Universidad de la Costa
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dc.rightsCC0 1.0 Universal
dc.rightshttp://creativecommons.org/publicdomain/zero/1.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourceAdvances in Mechanics
dc.sourcehttp://advancesinmech.com/index.php/am/article/view/164
dc.subjectControl loop performance
dc.subjectPerformance indices
dc.subjectMachine learning
dc.subjectNeural networks
dc.subjectInferential models
dc.titleStochastic performance indices to infer deterministic indices through machine learning in the performance analysis of control loops
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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