Show simple item record

dc.creatorHadechni Bonett, Samir
dc.creatorRamírez Parra, Jhon
dc.creatorEscobar Davidson, Leonardo
dc.creatorColl Velasquez, Jean
dc.creatorJiménez-Cabas, Javier
dc.creatorDíaz Saenz, Carlos
dc.description.abstractControl 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'
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.sourceTechnology Reports of Kansai Universityspa
dc.subjectControl systemspa
dc.subjectOscillating disturbancesspa
dc.subjectIntegral absolute errorspa
dc.subjectFast fourier transformspa
dc.subjectAutocovariance functionspa
dc.titleBehavior computational tool for detection and diagnosis oscillations in a control systemsspa
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),
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,
dcterms.references[3]. Thornhill, NF and Hugglund, T. (1997). Oscillation detection and diagnostics in control loops. Control Engineering Practice, 5(10),
dcterms.references[4]. Verification, validation, and testing: MATLAB and Simulink solutions. (s. f.). MATLAB & Simulink. Retrieved 10 March 2020, from
dcterms.references[5]. M. Jelali, Control Performance Management in Industrial Automation. London: Springer,
dcterms.references[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
dcterms.references[7] J. P. Shunta, Achieving World Class Manufacturing Through Process Control, 1st ed. Upper Saddle River, NJ, USA: Prentice Hall PTR,
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),
dcterms.references[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,
dcterms.references[10]. E. Naghoos, "Oscillation Detection and Causality Analysis of Control Systems",, 2016. [Online]. Available: f08c3d7a9ae4/Naghoosi_Elham_201607_PhD.pdf. [Accessed: 31- Aug- 2019].spa
dcterms.references[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),
dcterms.references[12]. Takahashi, S., Tachibana, K., & Saito, T. (1991). U.S. Patent No. 5,043,862. Washington, DC: U.S. Patent and Trademark

Files in this item


This item appears in the following Collection(s)

Show simple item record

CC0 1.0 Universal
Except where otherwise noted, this item's license is described as CC0 1.0 Universal