Measurement of pressure losses using computer vision
Keywords:
Codebook, computer vision, background subtraction, Instrumentation and measurement, Piezometer
Abstract
In a hydraulic circuit, the fluid pressure loss when passing through different sections or accessories, and it can be inspected by observing the fluid level in piezometers. This paper presents a development in artificial vision, which allows on-line measurement of pressure levels recorded in sixteen piezometric columns. The technique of background subtraction based in algorithm on Codebook, combined with morphological filtering and junction blobs, are used to obtain piezometrics levels. The processing algorithm is designed in Open CV and supported on Linux. Coefficients of variation of less than 0.1% and maximum error of 1.4% were obtained.Downloads
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[5] G. Heredia and A. Ollero, “Virtual sensor for failure detection, identification and recovery in the transition phase of a morphing aircraft,” Sensor, vol. 10, pp. 2188-2201, 2010.
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[7] J. P. Sebastiá, J. Alberola, and R. Lajara, “Physical signal conditioning for GMR magnetic sensors : Applied to traffic speed monitoring GMR sensors,” Sensors Actuators A Phys., vol. 137, pp. 230-235, 2007.
[8] P. Ibarguengoytia, A. Reyes, M. Huerta, and J. Hermosillo, “Probabilistic virtual sensor for on-line viscosity estimation,” in Artificial Intelligence, 2008. MICAI ’08. Seventh Mexican International Conference on, 2008, pp. 3777-782.
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[10] N. Hardy and A. Ahmad, “ViSIAr – A virtual sensor integration architecture,” Robotica, vol. 17, pp. 637-647, 1999.
[11] E. Velásquez, “Lectura automática de instrumentos de medida mediante técnicas de visión artificial,” Universidad de Vigo, 2008.
[12] E. Vázquez-Fernández, A. Dacal-Nieto, H. González-Jorge, F. Martín, A. Formella, and V. Álvarez-Valado, “A machine vision system for the calibration of digital thermometers,” Meas. Sci. Technol., vol. 20, no. 6, p. 065-106, Jun. 2009.
[13] P. Jampana, “Computer vision based sensors for chemical processes outline introduction,” University of Alberta, 2009.
[14] E. Valverde and J. Valverde, “Diseño e implementación de un laboratorio remoto: caso de estudio planta de pérdidas de carga en conductos a presión del Laboratorio de Hidraúlica,” Universidad del Cauca, 2010.
[15] K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, “Real-time foreground–background segmentation using codebook model,” Real-Time Imaging, vol. 11, no. 3, pp. 172-185, Jun. 2005.
[16] R. Lagani, OpenCV 2 Computer Vision Application Programming Cookbook. Packt Publishing Ltd, 2011.
[17] I. Corporation, “Intel Corporation.pdf,” 2012. [Online]. Available: http://developer.intel.com.
[18] G. Bradski and A. Kaehler, Learnig OpenCV. O’Reilly Media, Inc., 2008.
[19] R. Gonzalez and R. Woods, Digital image processing. Prentice Hall Upper Saddle River, 2002.
[20] N. Raveendranathan, V. Loseu, E. Guenterberg, R. Giannantonio, R. Gravina, M. Sgroi, and R. Jafari, “Implementation of virtual sensors in body sensor networks with the SPINE framework,” 2009 IEEE Int. Symp. Ind. Embed. Syst., pp. 124-127, Jul. 2009.
[21] J. A. Cerquera and J. E. Córdoba, “Medición y pérdidas de presión usando visión por computador,” Universidad del Cauca, 2012.
[22] A. Senior, A. Hampapur, Y.-L. Tian, L. Brown, S. Pankanti, and R. Bolle, “Appearance models for occlusion handling,” Image Vis. Comput., vol. 24, no. 11-1, pp. 1233-1243, 2014.
[23] F. Chang, C. Chen, and C. Lu, “A linear-time component-labeling algorithm using contour tracing technique,” Comput. Vis. image Underst., vol. 93, pp. 206-220, 2003.
[24] I. Espejo, F. Fernández, M. López, M. Muñoz, A. Rodríguez, A. Sánchez, and C. Valero, “Estadística descriptiva y probabilidad: (Teoría y problemas),” Universidad de Cádiz, 2009.
Published
2016-04-04
How to Cite
Cerquera-Yacumal, J., Córdoba-Papamija, J., Realpe-Chamorro, J., & Flórez-Marulanda, J. (2016). Measurement of pressure losses using computer vision. ITECKNE, 13(1), 64-73. https://doi.org/https://doi.org/10.15332/iteckne.v13i1.1383
Issue
Section
Research and Innovation Articles
