Non-obtrusive stiction detection methods for control systems
Industrial 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 t...
- Autores:
-
Escobar Davidson, Leonardo
Sucerquia Rincones, Stephany
Hadechni Bonett, Samir
Ramírez Parra, Jhon
Coll Velásquez, Jean
Beleño Saenz, Kelvin
Jiménez-Cabas, Javier
Díaz Saenz, Carlos
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/8000
- Acceso en línea:
- https://hdl.handle.net/11323/8000
https://repositorio.cuc.edu.co/
- Palabra clave:
- Control systems
Hysteresis
Cross correlation
Non-linearity
Curve fitting
- Rights
- openAccess
- License
- CC0 1.0 Universal
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Non-obtrusive stiction detection methods for control systems |
| title |
Non-obtrusive stiction detection methods for control systems |
| spellingShingle |
Non-obtrusive stiction detection methods for control systems Control systems Hysteresis Cross correlation Non-linearity Curve fitting |
| title_short |
Non-obtrusive stiction detection methods for control systems |
| title_full |
Non-obtrusive stiction detection methods for control systems |
| title_fullStr |
Non-obtrusive stiction detection methods for control systems |
| title_full_unstemmed |
Non-obtrusive stiction detection methods for control systems |
| title_sort |
Non-obtrusive stiction detection methods for control systems |
| dc.creator.fl_str_mv |
Escobar Davidson, Leonardo Sucerquia Rincones, Stephany Hadechni Bonett, Samir Ramírez Parra, Jhon Coll Velásquez, Jean Beleño Saenz, Kelvin Jiménez-Cabas, Javier Díaz Saenz, Carlos |
| dc.contributor.author.spa.fl_str_mv |
Escobar Davidson, Leonardo Sucerquia Rincones, Stephany Hadechni Bonett, Samir Ramírez Parra, Jhon Coll Velásquez, Jean Beleño Saenz, Kelvin Jiménez-Cabas, Javier Díaz Saenz, Carlos |
| dc.subject.spa.fl_str_mv |
Control systems Hysteresis Cross correlation Non-linearity Curve fitting |
| topic |
Control systems Hysteresis Cross correlation Non-linearity Curve fitting |
| description |
Industrial 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]. |
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2020 |
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2020-08 |
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2021-03-12T17:59:44Z |
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2021-03-12T17:59:44Z |
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Artículo de revista |
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https://hdl.handle.net/11323/8000 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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eng |
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eng |
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[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. [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. [3] Thornhill, nf and hugglund, t. (1997). Oscillation detection and diagnostics in control loops. Control engineering practice, 5(10), 1343-1354. [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. [5] M. Jelali, control performance management in industrial automation. London: springer, 2013. [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. [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. [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. [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. [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]. [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. [12] Takahashi, s., tachibana, k., & saito, t. (1991). U.s. patent no. 5,043,862. Washington, dc: u.s. patent and trademark office. [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. [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. |
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Escobar Davidson, LeonardoSucerquia Rincones, StephanyHadechni Bonett, SamirRamírez Parra, JhonColl Velásquez, JeanBeleño Saenz, KelvinJiménez-Cabas, JavierDíaz Saenz, Carlos2021-03-12T17:59:44Z2021-03-12T17:59:44Z2020-080453-2198https://hdl.handle.net/11323/8000Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Industrial 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].Escobar Davidson, LeonardoSucerquia Rincones, StephanyHadechni Bonett, SamirRamírez Parra, JhonColl Velásquez, JeanBeleño Saenz, KelvinJiménez-Cabas, Javier-will be generated-orcid-0000-0001-9707-8418-600Díaz Saenz, Carlosapplication/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Technology Reports of Kansai Universityhttps://www.researchgate.net/publication/345259859_Non-Obtrusive_Stiction_Detection_Methods_for_Control_Systems/link/5fa1dc81458515b7cfb9df90/downloadControl systemsHysteresisCross correlationNon-linearityCurve fittingNon-obtrusive stiction detection methods for control systemsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[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.[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.[3] Thornhill, nf and hugglund, t. (1997). Oscillation detection and diagnostics in control loops. Control engineering practice, 5(10), 1343-1354.[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.[5] M. Jelali, control performance management in industrial automation. London: springer, 2013.[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.[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.[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.[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.[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].[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.[12] Takahashi, s., tachibana, k., & saito, t. (1991). U.s. patent no. 5,043,862. Washington, dc: u.s. patent and trademark office.[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.[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. 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