Detección de pérdidas de aislamiento en un motor de inducción basado en el análisis de la transformada Wavelet aplicada al flujo de dispersión magnético
Keywords:
Diagnosis, Faults, Flow, Transient, Wavelet
Abstract
Present work evidences the aftermath in the detection instant faults (losses of isolation) occurred in a motor of three-phase induction and his intervening detection the present information in the flow of magnetic dispersion through the transformed wavelet. Normally the losses of isolation among bobbins of same phase or of different phases in his premature statuses of development generate transient short-lived that they are not easily detectable, transient that normally they end up shutting out the machine on duty. The transformed Wavelet permits to do a sweeping on a sign in frequency and time, that permits identifying faults or disturbances of obvious form and needs to, diagnosing with it to detect faults in premature statuses of development.
Downloads
Download data is not yet available.
References
[1] P. J. Tavner and J. Penman, Condition Monitoring Of Electrical Machines. Letchworth, U. K: Research Studies Press, 1987.
[2] J. L. Kohler, J. Sottile, and F. C. Trutt, “Conditionbased maintenance of electric machines,” in Proc. IEEE Ind. Applicat. Conf, 1999, pp. 205– 211.
[3] Bonnett. A. H, Soukup, G.C. Analysis of rotor failures in squirrel cage induction motors. IEEE Transactions on Industry Applications. Vol. 24-6, p.1124-1130. Nov-Dic 1998
[4] Hachemi B. M, A review of Induction Motor Signature Analysis as a Medium for Faults Detection. IEEE Transactions on Industry Electronics, Vol 47-5 p.984-993. Oct 2000
[5] Motor Reliability Working Group, “Report of large motor reliability. survey of industrial and comercial installations Part I, and II,” IEEE. Trans. Ind. Appl., vol. 21-4, p. 853-872, July-Aug. 1985
[6] Veruchhi. C. J, Acosta. G.G, “Técnicas de detección y diagnóstico de fallos en Máquinas eléctricas de inducción”. IEEE Latin America Transaction Volumen 5 No 1. 2007, p.41-49.
[7] S. Nandi, H. A. Toliyat, X. Li, “Condition Monitoring and Fault Diagnosis of Electrical Motors-A Review,” IEEE Trans. on Energy Conversion, vol. 20, no. 4, pp 719 – 729, December 2005.
[8] J. H. Jung, J. J. Lee, and B. H. Kwon, “Online diagnosis of induction motors using MCSA,” IEEE Trans. Ind. Electron., vol. 53, no. 6, pp. 1842– 1852, Dec. 2006.
[9] Jaideva C.Goswami and Andrew K.Chan, “Fundamentals of Wavelets: Theory, Algorithms, and Applications, John Wiley & Sons”, 1999
[10] J. A. Antonino-Daviu, M. Riera-Guasp, J. R. Folch, and M. P. M. Palomares, “Validation of a new method for the diagnosis of rotor bar failures via wavelet transform in industrial induction machines,” IEEE Trans. Ind. Appl., vol. 42, no. 4, pp. 990–996, Jul./Aug. 2006.
[11] J. Cusido, L. Romeral, J. A. Ortega, J. A. Rosero, and A. García, “Fault detection in induction machines using power spectral density in wavelet decomposition,” IEEE Trans. Ind. Electron., vol. 55, no. 2, p. 633–643, Feb. 2008
[12] P.Addison, “The illustrated wavelet transform handbook, Institute of Physics Publishing”, 2002.
[13] Jaideva C.Goswami and Andrew K.Chan, “Fundamentals of Wavelets: Theory, Algorithms, and Applications, John Wiley & Sons”, 1999.
[14] Cortés J.A, Cano H.B, Chaves J.A. “Del Análisis de Fourier a las Wavelets- Transformada Continua Wavelet (CWT). Scientia Et Technica. Vol XIII. No 037.p.p 133-138. Diciembre 2007.
[2] J. L. Kohler, J. Sottile, and F. C. Trutt, “Conditionbased maintenance of electric machines,” in Proc. IEEE Ind. Applicat. Conf, 1999, pp. 205– 211.
[3] Bonnett. A. H, Soukup, G.C. Analysis of rotor failures in squirrel cage induction motors. IEEE Transactions on Industry Applications. Vol. 24-6, p.1124-1130. Nov-Dic 1998
[4] Hachemi B. M, A review of Induction Motor Signature Analysis as a Medium for Faults Detection. IEEE Transactions on Industry Electronics, Vol 47-5 p.984-993. Oct 2000
[5] Motor Reliability Working Group, “Report of large motor reliability. survey of industrial and comercial installations Part I, and II,” IEEE. Trans. Ind. Appl., vol. 21-4, p. 853-872, July-Aug. 1985
[6] Veruchhi. C. J, Acosta. G.G, “Técnicas de detección y diagnóstico de fallos en Máquinas eléctricas de inducción”. IEEE Latin America Transaction Volumen 5 No 1. 2007, p.41-49.
[7] S. Nandi, H. A. Toliyat, X. Li, “Condition Monitoring and Fault Diagnosis of Electrical Motors-A Review,” IEEE Trans. on Energy Conversion, vol. 20, no. 4, pp 719 – 729, December 2005.
[8] J. H. Jung, J. J. Lee, and B. H. Kwon, “Online diagnosis of induction motors using MCSA,” IEEE Trans. Ind. Electron., vol. 53, no. 6, pp. 1842– 1852, Dec. 2006.
[9] Jaideva C.Goswami and Andrew K.Chan, “Fundamentals of Wavelets: Theory, Algorithms, and Applications, John Wiley & Sons”, 1999
[10] J. A. Antonino-Daviu, M. Riera-Guasp, J. R. Folch, and M. P. M. Palomares, “Validation of a new method for the diagnosis of rotor bar failures via wavelet transform in industrial induction machines,” IEEE Trans. Ind. Appl., vol. 42, no. 4, pp. 990–996, Jul./Aug. 2006.
[11] J. Cusido, L. Romeral, J. A. Ortega, J. A. Rosero, and A. García, “Fault detection in induction machines using power spectral density in wavelet decomposition,” IEEE Trans. Ind. Electron., vol. 55, no. 2, p. 633–643, Feb. 2008
[12] P.Addison, “The illustrated wavelet transform handbook, Institute of Physics Publishing”, 2002.
[13] Jaideva C.Goswami and Andrew K.Chan, “Fundamentals of Wavelets: Theory, Algorithms, and Applications, John Wiley & Sons”, 1999.
[14] Cortés J.A, Cano H.B, Chaves J.A. “Del Análisis de Fourier a las Wavelets- Transformada Continua Wavelet (CWT). Scientia Et Technica. Vol XIII. No 037.p.p 133-138. Diciembre 2007.
Published
2013-10-24
How to Cite
Anteliz Jaimes, A. (2013). Detección de pérdidas de aislamiento en un motor de inducción basado en el análisis de la transformada Wavelet aplicada al flujo de dispersión magnético. ITECKNE, 7(2), 157-164. https://doi.org/https://doi.org/10.15332/iteckne.v7i2.2719
Issue
Section
Research and Innovation Articles
