References
- S. Wu and T. Chow, 'Induction machine fault detection using SOM-based RBF neural network.' IEEE Trans. Ind. Elect., vol. 51, no. 1, pp. 183-194, 2004 https://doi.org/10.1109/TIE.2003.821897
- W. T. Thomson and M. Fenger, 'Current signature analysis to detect induction motor faults.' IEEE Ind. Applicat. Magazine, pp. 26-34, July/August 2001 https://doi.org/10.1109/2943.930988
- M. Nejjari and II. Benbouzid, 'Monitoring and diagnosis of induction motors electrical faults using a current Park's vector pattern learning approach,' IEEE Trans. Ind. Applical., Vol. 36. no.3, pp. 730-735 2000 https://doi.org/10.1109/28.845047
- Zidani et al., 'Induction motor stator faults diagnosis by a current Concordia pattern-based fuzzy decision system,' IEEE Trans. Energy Conversion, vol. 18, no. 4, pp. 469-475, December 2004 https://doi.org/10.1109/TEC.2003.815832
- M. Haji and H. A. Toliyat, 'Pattern recognition-a technique for induction machines rotor broken bar detection,' IEEE Trans. on Energy Conversion, Vol. 16, Issue 4, pp. 312-317, 2001 https://doi.org/10.1109/60.969469
- A. M. Trzynadlowski and E. Ritchie, 'Comparative investigation of diagnostic media for induction motors : a case of rotor cage faults,' IEEE Trans. on Industrial Electronics, Vol. 47, Issue 5, pp. 1092-1099, 2000 https://doi.org/10.1109/41.873218
- Z. Zhang, Z. Ren, and W. Huang, 'A Novel Detection Method of Motor Broken Rotor Bars Based on Wavelet Ridge,' IEEE Trans. On Energy Conversion, Vol. 18, no. 3, September, 2003 https://doi.org/10.1109/TEC.2003.815851
- Z. Ye, R. Wu, and A. Sadeghian, 'Current Signature Analysis of Induction Motor Mechanical Faults by Wavelet Packet Decomposition,' IEEE Trans. on Industrial Electronics, vol. 50, no. 6, December, 2003 https://doi.org/10.1109/TIE.2003.819682
- S. Seker and E. Ayaz, 'Feature extraction related to bearing damage in electric motors by wavelet analysis,' Journal of the Franklin Institute, 2003 https://doi.org/10.1016/S0016-0032(03)00015-2
- R. O. Duda, P. E. Hart, and D. C. Stork, Pattern Classification, JOHN WILEY &SONS, Second Edition, 2002
- V. N. Vapnik, The Nature of Statistical Learning Theory. New York. Springer, 1999
- L. V. Ganyun, H. Cheng, H. Zhai, and L. Dong, 'Fault diagnosis of power transformer based on multi-layer SVM classifier.' Electrical Power Systems Research, Vol. 75, pp. 9-15. 2005 https://doi.org/10.1016/j.epsr.2004.07.013
- A. Widodo, B.-S. Yang, and T. Han, 'Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors,' Expert Systems with Applications. In Press, Corrected Proof, Available online 4, Jan. 2006
- Q. Hu, Z. He, Z. Zhang, and Y. Zi, 'Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble,' Mechanical Systems and Signal Processing, 2006 https://doi.org/10.1016/j.ymssp.2006.01.007
- G. R. Bossio, C. H. De Angelo, G. O. Garcia, J. A. Solsona, and M. I. Valla, 'Effects of rotor bar and end-ring faults over the signals of a position estimation strategy for induction motors,' IEEE Trans. Industry Applications, vol. 41, no. 4, pp. 1005-1011, 2005 https://doi.org/10.1109/TIA.2005.851038
- C.-Chang and C.-J. Lin, (2001), LIBSVM a library for support vector machines, Software available: http://www.csie. ntu.edu.tw/~cjlin/libsvm