A Modified Probabilistic Neural Network-based Algorithm for Detecting Turn Faults in Induction Machines

References

• Motor Reliability Working Group, “Report of large motor reliability survey of industrial and commercial installations Part I, and II,” IEEE Trans. Ind. Appi., vol. IA-21, no. 4, pp. 853–72, Jul./Aug. 1985.
   Google Scholar
• G J Paoletti, and A Rose, “Improving existing motor protection for medium voltage motors,” IEEE Trans. Ind. Appi., vol. 25, no. 3, pp. 456–64, May/Jun. 1989.
   Google Scholar
• A H Bonnett, and G C Soukup, “Cause and Analysis of Stator and Rotor Failures in Three-phase Squirrel-Cage Induction Motors,” IEEE Trans. Ind. Appi., vol. 28, no. 4,pp. 921–37, Jul / Aug. 1992.
   Google Scholar
• H A Toliyat, and T A Lipo, “Transient analysis of cage induction machines under stator, rotor bar and end ring faults,” IEEE Trans Energy Conv., vol. 10, no.2, pp. 241–5, June 1995.
   Google Scholar
• J A Oliver, “Electric Motor Predictive and Preventive Maintenance Guide,” EPRI Research Project 2814–35, July. 1992.
   Google Scholar
• C Y Lee, “Effects of Unbalanced Voltage on the Operation Performance of a Three-phase Induction Motor,” IEEE Trans. Energy Conv., vol. 14, no. 2, pp. 202–8, Jun. 1997.
   Google Scholar
• W H Kersting, “Causes and Effects of Unbalanced Voltages Serving an Induction Motor,” IEEE Trans. Ind. Appi., vol. 37, no. 1, pp. 165–70, Jan/Feb. 2001.
   Google Scholar
• X Luo, Y Liao, H A Toliyat, A El-Antably, and T A Lipo, “Multiple coupled circuit modeling of induction machines,” IEEE Trans. Ind. Appi., vol. 31, no. 2, pp. 311–8, Mar/Apr. 1995.
   Google Scholar
• P C Krause, O Wasynczuk, and S C Sudhoff, “Analysis of electric machinery and drive systems”, Hoboken, New Jersey: Johh Wiley & Sons; 2002.
   Google Scholar
• X Chang, V Cocquempot, and C Christophe, “A Model of Asynchronous Machines for Stator Fault Detection and Isolation,” IEEE Trans. Ind. Eiectron., vol. 50, no. 3, pp. 578–84, Jun. 2003.
   Google Scholar
• R M Tallam, T G Habetler, and R G Harley, “Transient Model for Induction Machines With Stator Winding Turn Faults,” IEEE Trans. Ind. Appi., vol. 38, no. 3, pp. 632–7, May/Jun. 2002.
   Google Scholar
• S M Cruz, and A J Cardoso, “Diagnosis of Stator Inter?Turn Short Circuits in DTC Induction Motor Drives,” IEEE Trans. Ind. Appl., vol. 40, no. 5, pp. 1349?60, Sept/Oct. 2004.
   Google Scholar
• J L Kohler, J Sottile, and F C Trutt, “Condition Monitoring of StatorWindings in Induction Motors: Part I—Experimental Investigation of the Effective Negative-Sequence Impedance Detector,” IEEE Trans. Ind. Appi., vol. 38, no. 5, pp. 1447–53, Sept/Oct. 2002.
   Google Scholar
• J Sottile, F C Trutt, and J L Kohler, “Condition Monitoring of Stator Windings in Induction Motors: Part II—Experimental Investigation of Voltage Mismatch Detectors,” IEEE Trans. Ind. Appi., vol. 38, no. 5, pp. 1454–59, Sept/Oct. 2002.
   Google Scholar
• R M Tallam, S B Lee, G C Stone, G B Kliman, J Yoo, and T G Habetler, et ai, “A Survey of Methods for Detection of Stator-Related Faults in Induction Machines,” IEEE Trans. Ind. Appi., vol. 43, no. 4, pp. 920–33, Jul/Aug. 2007.
   Google Scholar
• J C Hoskins, K M Kaliyur, and D M Himmelblau, “Insipient fault detection and diagnosis using artificial neural networks,” in Proc.IEEE Inti.Conf.Neurai Networks, vol. 1, pp. 81–6, Jun. 1990.
   Google Scholar
• F Filippetti, G Franceschini, C Tassoni, and P Vas, “Recent Developments of Induction Motor Drives Fault Diagnosis Using AI Techniques,” IEEE Trans. Ind. Eiectron., vol. 47, no. 5, pp. 994–1004, Oct. 2000.
   Google Scholar
• A Graps, “An Introduction to wavelets,” IEEE Computational science and Engineering., Summer, pp.50–5, 1995.
   Google Scholar
• T K Sarkar, M Salazar-Palma, and M C Wicks, “Wavelet Applications in Engineering Electromagnetics”, Norwood, MA: Artech House, 2002.
   Google Scholar
• R M Tallam, T G Habetler, and R G Harley, “Stator Winding Turn-Fault Detection for Closed-Loop Induction Motor Drives,” IEEE Trans. Ind. Appi., vol. 39, no. 3, pp. 720–4, May/Jun. 2003.
   Google Scholar
• S Blanco, A Figliola, R Q Quiroga, O A Rosso, and E Serrano, “Time-frequency analysis of electroencephalogram series, III, Wavelet packets and information cost function,” Physica E., Vol. 57 , no. 1, pp. 932–40, Jan. 1998.
   Google Scholar
• O A Rosso, S Blanco, J Yordanova, V Kolev, A Figliola, and M Schurmann, et ai, “Wavelet entropy: a new tool for analysis of short duration brain electrical signals,” J. Neurosci. Method, vol. 105, no. 1, pp. 65–75, Jan. 2001.
   Google Scholar
• S Sello, “Wavelet entropy as a measure of solar cycle complexity,” J.Astron. Astrophys., vol. 363, pp. 311–5, 2000.
   Google Scholar
• C M Gonza’lez, H A Larrondo, and O A Rosso “Statistical complexity measure of pseudorandom bit generators,” Physica A: Statistical Mechanics and its Applications, vol. 354, pp. 281–300, Aug. 2005
   Google Scholar
• A M Kowalski, M T Marti’n, A Plastino, A N Proto, and O A Rosso, “Wavelet statistical complexity analysis of the classical limit,” Phys. Lett. A. vol. 311, no. 2–3, pp. 180–91, May. 2003.
   Google Scholar
• L Zunino, D G Pe’rez, M Garavaglia, and O A Rosso, “Characterization of laser propagation through turbulent media by quantifiers based on the wavelet transform: Dynamic study,” Physica A: Statistical Mechanics and its Applications., vol. 364, pp. 79–86, May. 2006.
   Google Scholar
• D G Pe’rez, L Zunino, M Garavaglia, and O A Rosso, “Wavelet entropy and fractional Brownian motion time series,” Physica A: Statistical Mechanics and its Applications, vol. 356, no. 2, pp. 282–8, May. 2006.
   Google Scholar
• L Zunino, D G Pere, M Garavaglia, and O A Rosso, “Wavelet entropy of stochastic processes,” Physica A: Statistical Mechanics and its Applications, vol. 379, no. 2, pp. 503–12, Jun. 2007.
   Google Scholar
• A M Korol, R J Rasia, and O A Rosso, “Alterations of thalassemic erythrocytes detected by wavelet entropy,” Physica A: Statistical Mechanics and its Applications., vol. 375, no. 1,pp. 257–64, Feb. 2007.
   Google Scholar
• E Parzen, “On estimation of a probability density function and mode,” Ann. Math. Stat., vol. 33, pp. 1065–76, Sept. 1962.
   Google Scholar
• D F Specht, “Probabilistic Neural Networks for Classification, Mapping, or Associative Memory,” IEEE International Conference on Neurai Networks, vol. 1, pp. 525–32, July 1988.
   Google Scholar
• A Zaknich, “Neural networks for Intelligent Signal Processing”, Singapore: World Scientific publishing co. Pte.Ltd; 2003.
   Google Scholar
• R L Streit, and T E Luginbuhl, “Maximum likelihood training of probabilistic neural networks,” IEEE Trans. Neurai Networks. vol. 5, no. 5, pp. 764–83, 1994.
   Google Scholar
• D F Specht, “Enhancements to probabilistic neural networks,” in Proc. of the IEEE International Joint Conference on Neural Networks, pp. 761–8, Jun. 1992,
   Google Scholar
• R B Michael, and J Diamond, “Constructive training of probabilistic neural networks,” vol. 19, no. 2, pp. 167–83, 1998.
   Google Scholar
• M Plutowski, and H White, “Selecting Concise Training Sets from Clean Data,” IEEE Trans. Neurai Net., vol. 4, no. 2, pp. 305–18, Mar. 1993.
   Google Scholar
• S Tong, and D Koller, “Active learning for parameter estimation in bayesian networks,” in Proc. Advances in Neurai Inf. Processing Sys. Denver. USA. 2000.
   Google Scholar
• B Bolat, and T Yildirim, “Active learning for Probabilistic Neural Networks,” in Proc Springer ICNC2005, pp. 110–8, 2005.
   Google Scholar