Behavior computational tool for detection and diagnosis oscillations in a control systems
Control systems receive input signals to execute a process, resulting in an output. Based on this sequence, the computational tool has the function of detecting and diagnosing anomalies in the system. The oscillation diagnosis of the system is based on the analysis of the oscillations generated by a...
- Autores:
-
Hadechni Bonett, Samir
Ramírez Parra, Jhon
Escobar Davidson, Leonardo
Coll Velasquez, 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:11323/7998
- Acceso en línea:
- https://hdl.handle.net/11323/7998
https://repositorio.cuc.edu.co/
- Palabra clave:
- Control system
Oscillating disturbances
Integral absolute error
Fast fourier transform
Autocovariance function
- Rights
- openAccess
- License
- CC0 1.0 Universal
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| dc.title.spa.fl_str_mv |
Behavior computational tool for detection and diagnosis oscillations in a control systems |
| title |
Behavior computational tool for detection and diagnosis oscillations in a control systems |
| spellingShingle |
Behavior computational tool for detection and diagnosis oscillations in a control systems Control system Oscillating disturbances Integral absolute error Fast fourier transform Autocovariance function |
| title_short |
Behavior computational tool for detection and diagnosis oscillations in a control systems |
| title_full |
Behavior computational tool for detection and diagnosis oscillations in a control systems |
| title_fullStr |
Behavior computational tool for detection and diagnosis oscillations in a control systems |
| title_full_unstemmed |
Behavior computational tool for detection and diagnosis oscillations in a control systems |
| title_sort |
Behavior computational tool for detection and diagnosis oscillations in a control systems |
| dc.creator.fl_str_mv |
Hadechni Bonett, Samir Ramírez Parra, Jhon Escobar Davidson, Leonardo Coll Velasquez, Jean BELEÑO SAENZ, KELVIN Jiménez-Cabas, Javier Díaz Saenz, Carlos |
| dc.contributor.author.spa.fl_str_mv |
Hadechni Bonett, Samir Ramírez Parra, Jhon Escobar Davidson, Leonardo Coll Velasquez, Jean BELEÑO SAENZ, KELVIN Jiménez-Cabas, Javier Díaz Saenz, Carlos |
| dc.subject.spa.fl_str_mv |
Control system Oscillating disturbances Integral absolute error Fast fourier transform Autocovariance function |
| topic |
Control system Oscillating disturbances Integral absolute error Fast fourier transform Autocovariance function |
| description |
Control systems receive input signals to execute a process, resulting in an output. Based on this sequence, the computational tool has the function of detecting and diagnosing anomalies in the system. The oscillation diagnosis of the system is based on the analysis of the oscillations generated by any disturbance, whether internal or external. The most appropriate form of detection is through noninvasive methods, therefore, there are some specialized in system improvements such as; detection of peaks in the power spectrum (FFT), the method based on time domain criteria and the absolute error integral (IAE) and the method based on the autocovariance function (ACF). The computational tool aims to detect oscillations of closed-loop control systems, through the 'IAE', 'ACF' and 'FFT' method. |
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2020 |
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2020-07 |
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2021-03-12T17:32:58Z |
| dc.date.available.none.fl_str_mv |
2021-03-12T17:32:58Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/coar/resource_type/c_6501 |
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Text |
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info:eu-repo/semantics/article |
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0453-2198 |
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https://hdl.handle.net/11323/7998 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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0453-2198 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
[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/verificationvalidation.html. [5]. M. Jelali, Control Performance Management in Industrial Automation. London: Springer, 2013. [6]. Hadechni Bonett, S. J., Ramirez Parra, J. M., Diaz Saenz, C., & Jimenez Cabas, J. (2020, May). Computational Tool for The Detection and Diagnosis of Oscillations in A Control System. Universidad Autónoma del Caribe. [7] J. P. Shunta, Achieving World Class Manufacturing Through Process Control, 1st ed. Upper Saddle River, NJ, USA: Prentice Hall PTR, 1997. [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. Hägglund, “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/57ba6990-7ddc4b58-8555-b9ea5ec4b79d/view/b06afee3-316c-48e6-b8b6- f08c3d7a9ae4/Naghoosi_Elham_201607_PhD.pdf. [Accessed: 31- Aug- 2019]. [11] Lishner, M., Akselrod, S., Avi, V. M., Oz, O., Divon, M., & Ravid, M. (1987). Spectral analysis of heart rate fluctuations. A non-invasive, sensitive method for the early diagnosis of autonomic neuropathy in diabetes mellitus. Journal of the autonomic nervous system, 19(2), 119-125. [12]. Takahashi, S., Tachibana, K., & Saito, T. (1991). U.S. Patent No. 5,043,862. Washington, DC: U.S. Patent and Trademark Office. |
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CC0 1.0 Universal |
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http://creativecommons.org/publicdomain/zero/1.0/ |
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CC0 1.0 Universal http://creativecommons.org/publicdomain/zero/1.0/ http://purl.org/coar/access_right/c_abf2 |
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application/pdf |
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Technology Reports of Kansai University |
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Hadechni Bonett, SamirRamírez Parra, JhonEscobar Davidson, LeonardoColl Velasquez, JeanBELEÑO SAENZ, KELVINJiménez-Cabas, JavierDíaz Saenz, Carlos2021-03-12T17:32:58Z2021-03-12T17:32:58Z2020-070453-2198https://hdl.handle.net/11323/7998Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Control systems receive input signals to execute a process, resulting in an output. Based on this sequence, the computational tool has the function of detecting and diagnosing anomalies in the system. The oscillation diagnosis of the system is based on the analysis of the oscillations generated by any disturbance, whether internal or external. The most appropriate form of detection is through noninvasive methods, therefore, there are some specialized in system improvements such as; detection of peaks in the power spectrum (FFT), the method based on time domain criteria and the absolute error integral (IAE) and the method based on the autocovariance function (ACF). The computational tool aims to detect oscillations of closed-loop control systems, through the 'IAE', 'ACF' and 'FFT' method.Hadechni Bonett, SamirRamírez Parra, JhonEscobar Davidson, LeonardoColl Velasquez, JeanBELEÑO SAENZ, KELVIN-will be generated-orcid-0000-0002-9965-0681-600Jiménez-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/343615821_Behavior_Computational_Tool_for_Detection_and_Diagnosis_Oscillations_in_a_Control_SystemsControl systemOscillating disturbancesIntegral absolute errorFast fourier transformAutocovariance functionBehavior computational tool for detection and diagnosis oscillations in a 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/verificationvalidation.html.[5]. M. Jelali, Control Performance Management in Industrial Automation. London: Springer, 2013.[6]. Hadechni Bonett, S. J., Ramirez Parra, J. M., Diaz Saenz, C., & Jimenez Cabas, J. (2020, May). Computational Tool for The Detection and Diagnosis of Oscillations in A Control System. Universidad Autónoma del Caribe.[7] J. P. Shunta, Achieving World Class Manufacturing Through Process Control, 1st ed. Upper Saddle River, NJ, USA: Prentice Hall PTR, 1997.[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. Hägglund, “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/57ba6990-7ddc4b58-8555-b9ea5ec4b79d/view/b06afee3-316c-48e6-b8b6- f08c3d7a9ae4/Naghoosi_Elham_201607_PhD.pdf. [Accessed: 31- Aug- 2019].[11] Lishner, M., Akselrod, S., Avi, V. M., Oz, O., Divon, M., & Ravid, M. (1987). Spectral analysis of heart rate fluctuations. A non-invasive, sensitive method for the early diagnosis of autonomic neuropathy in diabetes mellitus. Journal of the autonomic nervous system, 19(2), 119-125.[12]. Takahashi, S., Tachibana, K., & Saito, T. (1991). U.S. Patent No. 5,043,862. 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