Un estudio sobre la localización, detección y diagnóstico de fallas en máquinas eléctricas.
Resumen
En el presente trabajo se presenta un estudio que describe los diferentes tipos de fallas, las formas características de las señales que generan y los métodos de diagnóstico en máquinas eléctricas. Además, efectúa un comparativo de las ventajas de cada uno de los diferentes métodos de detección de fallas en función de la información que requieren para el diagnóstico, el número e importancia de las fallas que pueden detectar, la rapidez con la que son capaces de anticipar una falla y el grado de certeza en el diagnóstico final. En particular, dicho estudio ayudará a proporcionar una visión rápida y clara acerca de los últimos trabajos y las nuevas investigaciones en el área.Descargas
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esReferencias bibliográficas
Sin, M., Soong, W., Ertugrul, N. Induction Machine On-line Condition Monitoring and Fault Diagnosis-A Survey, http://www.academia.edu/416441/Induction_Mchine_on-Line_Condition_Monitoring_and_Fault_Diagnosis_A_Survey.
Calero, R; Carta, J., et al. Fundamentos de mecanismos y máquinas para ingenieros, Ed.McGraw-Hill, ISBN: 844812099X.
Puche, R. Nuevos Métodos de Diagnosis de Excentricidad y otras Asimetrías Rotóricas en Máquinas Eléctricas de Inducción a través del Análisis de la Corriente Estatórica, [Tesis Doctoral], Universidad Politécnica de Valencia, 2008.
Castelli, M., Andrade M., et al. Metodología de monitoreo, detección y diagnóstico de fallos en motores asíncronos de inducción, Memorias URUMAN, Uruguay, No. 6, pp. 1-20, 2008.
Acosta, G., Verucchi, C. Técnicas de Detección y Diagnóstico de Fallos en Máquinas Eléctricas de Inducción, IEEE Latin america Trans., Vol. 5, No.1, pp. 41-49. 2007.
Benbouzid, M.E.H. Bibliography on induction motors faults detection and diagnosis, IEEE Trans. Energy Convers., Vol. 14, No.4, pp. 1065-1074, 1999. http://dx.doi.org/10.1109/60.815029
Verucchi, C., Acosta, G., and Benger, F. A Review on Fault Diagnosis of Induction Machines, Latin American App. Research, pp. 113-121, 2008.
Gubric, S., Aller, J.M., et al. A Survey of Testing and Monitoring Methods for Stator Insulation System in Induction Machines, http://prof.usb.ve/jaller/PPI_papers/CMC2008.pdf.
Acosta, G., Gelso, E., Pe-alva, M., et al. Sistema de monitoreo y diagnóstico de fallas para motores de inducción, Latin-American Conf. Automatic Control, 2002.
Hidalgo, J. Análisis de las zonas de falla de motores eléctricos, http://fersddanka.files.wor-dpress.com/2011/02/fallas-electricas1.pdf
Stojne, G.C., Boulter, E.A., Culbert, I., and Dhirani, H. Electrical insulation for rotating machines-Design, Evaluation, Aging, Testing, and Repair, ISBN 0-471-44506-1, pp. 371, 2004.
Tallam, R., Lee, S., at al. A survey of methods for detection pf stator-related faults in induction machines, IEEE Trans. Ind. App., Vol 43, No 4, pp, 920-933, 2007.
Grubic, S., Aller, J., et al. A survey on testing and monitoring methods for stator insulation systems of low-voltage induction machines focusing on turn insulation problems. ISBN: 978-1-4144-1621-9 pp. 4127-4136, 2008.
Siddique, A. Singh, B., Yadava, G.S., A review of stator fault monitoring techniques of induction motors, IEEE Trans. Energy Convers., Vol. 20, No.1, pp. 106-114, 2005. http://dx.doi.org/10.1109/TEC.2004.837304
Grainger, J. Stevenson Jr., W. Análisis de Sistemas de Potencia, Ed. McGraw-Hill, ISBN: 0-07-091971-2.
Nandi, S., Toliyat, H. Condition monitoring and fault diagnosis of electrical motors-a review, IEEE Trans. Energy Convers., Vol.20, No. 4, pp. 719-729, 2005. http://dx.doi.org/10.1109/TEC.2005.847955
Sin, M., Soong, W., Ertugrul, N. Induction Machine On-line Condition Monitoring and Fault Diagnosis-A Survey, http://adelaide.academia.edu
Villada, F., Parra, D., Ocampo, G. Estudio del comportamiento de motores de inducción ante fallas estatóricas, ISSN: 0120-6230, pp. 76-94, 2007.
Lee, S., Tallam, R., e. A robust, on-line turn-fault detection technique for induction machines based on monitoring the sequence component impedance matrix, IEEE, Trans. Power Electron., Vol. 18, No. 3, pp. 865-872, 2003. http://dx.doi.org/10.1109/TPEL.2003.810848
Cash, M., Habetler, T., Kliman, G. Insulation failure prediction in AC machines using line-neutral voltage, IEEE Trans. Ind. App., Vol. 34, No. 6, pp. 1234-1239, 1998. http://dx.doi.org/10.1109/28.738983
Thomson, W., Ferger, M. Current signature analysis to detect induction motor faults, IEEE Ind. Applications Magazine, pp. 26-34,2001. http://dx.doi.org/10.1109/2943.930988
Puche, R., Pons, J., et al. Review diagnosis methods of induction electrical machines based on steady state current, http://www.aedie.org/11chile-papers/263-Puche.pdf
Aguado, J. Análisis de Fallas en Motores de Inducción utilizando la corriente estatórica , Dise-o y construcción de prototipo basado en un Microcontrolador, http://es.scrib.com/doc/8203474/Analisis-de-Fallas-en-Motores-de-Induccion
Mehla, N., Dahiya, R. An approach of condition monitoring of induction motor using MCSA, Int. journal of Syst. App., Vol. 1, Issue 1, pp. 13-17, 2007.
Awadallah, M., Morcos, M., A. Application of Al tools in fault diagnosis of electrical machines and drives-an overview, IEEE Trans. Energy Convers., Vol. 18, No. 2, pp. 245-251, 2003. http://dx.doi.org/10.1109/TEC.2003.811739
El Hachemi, M. A Review of Induction Motors Signature Analysis as a Medium for Faults Detection, IEEE Trans. Ind. Electron., Vol. 47, No. 5, pp. 984-993, 2000. http://dx.doi.org/10.1109/41.873206
Thomson, W. A review of on-line condition monitoring techniques for three-phase squirrel-cage induction motors-past present and future, IEEE Int. Symposium on Diagnostics for Electr. Machines, pp. 3-18, 2002.
Han, Y., Song, T. Condition monitoring techniques for electrical equipment-a literature survey, IEEE Trans. Power Delivery, Vol. 18, No. 1, pp. 4-13, 2003. http://dx.doi.org/10.1109/TPWRD.2002.801425
Li, W., Mechefske, C. Detection of induction motor faults: a comparison of stator current, vibration and acoustic methods, http://jvc.sagepub.com/content/12/2/165.full.pdf, 2005.
Villada, F., Cadavid, D. Diagnóstico de fallas en motores de inducción mediante la aplicación de redes neuronales artificiales, http://www.scielo.cl/pdf/infotec/v18n2/art16.pdf, 2007.
Günal, S., Gokhan, D., Nezih, Ö. Induction machine condition monitoring using notch-filtered motor current, http://www.mm.anadolu.edu.tr/-gerek/Research
Rangel, J., Romero, R., et al. Novel methodology for online half-broken-bar detection on induction motors, IEEE Trans. instrumentation and measurement, Vol. 58, No. 5, pp. 1690-1698, 2009. http://dx.doi.org/10.1109/TIM.2009.2012932
Jung, J., Lee, J., Kwon, B. Online diagnosis of induction motors using MCSA, IEEE Trans. Ind. Electron., Vol. 53, No. 6, pp. 1842-1852, 2006. http://dx.doi.org/10.1109/TIE.2006.885131
Culbert, I., Rhodes, W. Using current signature analysis technology to reliably detect cage winding defect in squirrel-cage induction motors, IEEE Trans. Ind. App., Vol. 43, No. 2, pp. 422-428, 2007. http://dx.doi.org/10.1109/TIA.2006.889915
Benbouzid, M., Kliman, G. What stator current processing-based technique to use for induction motor rotor fault diagnosis? IEEE Trans. Energy Convers., Vol. 18, No. 2, pp. 238-244, 2003. http://dx.doi.org/10.1109/TEC.2003.811741
Armas, M. Analysis of induction machines trough generalized air gap torque method, Datos no publicados.
Hedayati, S., Henao, H., Capolino, G. Torsional vibration assessment using induction machine electromagnetic torque estimation, IEEE Trans. Ind. Electron., Vol. 57, No. 1, pp. 209-219, 2010. http://dx.doi.org/10.1109/TIE.2009.2034181
Pernia-Márquez, D. Introducción a la medición de vibración, http://mediola.comli.com/UTCV_IMIGZ/NOVENO/PREDICTIVO
Vijayraghavan, P., Krishnan, R. Noise in electrical machines: a review, IEEE Trans. Ind. App., Vol. 35, No. 5, pp. 1007-1013, 1999. http://dx.doi.org/10.1109/28.793360
Kreitzer, S., Obermeyer, J., Mistry, R. The effects of structural and localized resonances on induction motor performance, IEEE Trans. Ind. App., Vol. 44, No. 5, pp. 11367-1375, 2008. http://dx.doi.org/10.1109/TIA.2008.2002179
Radkowski, S. Vibro-acoustic diagnostics of low-energy stage of failures evolution, http://pig.sagepub.com/content/223/5/589.short
Lisner, R., Timar, P. A new approach to electric motor acoustic noise standards and test procedures, IEEE Trans. Energy Convers., Vol. 14, No. 3, pp. 692-697, 1999. http://dx.doi.org/10.1109/60.790937
Filippetti, F., Franceschini, G., et al. AI techniques in induction machines diagnosis including the speed ripple effect, IEEE Trans. Ind. App., Vol. 34, No. 1, pp. 98-108, 1998. http://dx.doi.org/10.1109/28.658729
Megahed, A. An artificial neural network based digital differential protection scheme for synchronous generator stator winding protection, IEEE Trans. Power Delivery, Vol. 14, No. 1, pp. 86-93, 1999. http://dx.doi.org/10.1109/61.736692
Lebaround, A., Clerc, G. Classification of induction machine faults by optimal time-frequency representations, IEEE Trans. Ind. Electron., Vol. 55, No. 12, pp. 4290-4298, 2008. http://dx.doi.org/10.1109/TIE.2008.2004666
Arnanz, R., Pacheco, M., González, L. Detección de fallos en motores de corriente alterna mediante ecuaciones de paridad y redes neuronales, http://webcache.googleusercontent.com/search?q=cache:quw21b1PTH4J:www.ceautomatica.es/old/actividades/jornadas/XXI/documentos/ja00_060/ja00_060.ps+http://www.ceautomatica.es/old/actividades/jornadas/XXI/documentos/ja00_060/ja00_060.ps.&cd=1&hl=es-419&ct=clnk&gl=mx
Darwish., H, Taalab, A. Development and implementation of an ANN-based fault diagnosis scheme for generator winding protection, IEEE Trans. Power Delivery, Vol. 16, No. 2, pp. 208-214, 2001. http://dx.doi.org/10.1109/61.915484
Hongyu, Y., Joseph, M., Lin, M. Intelligent diagnosis of rotating machinery faults- a review, http://eprints.qut.edu.au/17942/1/17942.pdf
Pillutla, S. Neural Network based saturation model for round rotor synchronous generator, IEEE Trans. Energy Convers., Vol. 14, No. 4, pp. 1019-1025, 1999. http://dx.doi.org/10.1109/60.815022
Bahbah, A., Girgis A. New method for generators' angles and angular velocities prediction for transient stability assessment of multimachine power systems using recurrent artificial neural network, IEEE Trans. Power Syst., Vol. 19, No. 2, pp. 1015-1022, 2004. http://dx.doi.org/10.1109/TPWRS.2004.826765
Filippetti, F., Franceschini, G., et al. Recent developments of induction motor drives fault diagnosis using AI techniques, IEEE Trans. Ind. Electron., Vol. 47, No. 5, pp. 994-1004, 2000. http://dx.doi.org/10.1109/41.873207
