Application of ANN for Induction Motor Fault Classification Using Hilbert Transform
DOI:
https://doi.org/10.51983/ajes-2012.1.2.1681Keywords:
Hilbert Transform, Induction Motor, Current Envelope, Artificial Neural Network, Multilayer PerceptionAbstract
This paper addresses the development of new signal processing approach based on Hilbert transform to extract the fault features in number of induction motor conditions. The motor conditions considered are normal condition and motors with bearing defects like inner race, outer race, stator interturn faults and rotor bar crack. Present approach is based on extraction of envelopes of the stator currents by Hilbert Transform. Representative features like maximum and minimum value, mean, standard deviation and norm are obtained from current envelopes. These features are then used as input to Artificial Neural Network. Experimental results obtained show that diagnostic system using MLP neural network along with Hilbert Transform is capable of classifying multiple faults in induction motor with high accuracy recognition rate.
References
F. Boldea and A. Nasar, “ Induction Machine Handbook,” CRC press, 2002.
P. Tavner, L. Ram, J. Penman and H. Sedding , “Condition Monitoring of Rotating Electrical Machines,” The Institution of Engineering & Technology, 2008.
W. T. Thomson and M. Fenger, “Current Signature Analysis to Detect Induction Motor Faults,” IEEE industry Applications Magazine, Vol. 7, No. 4, pp. 26-34, July/August 2001.
A. Martelo, “Fault Detection of Ball Bearings in Electrical Motors by Means of Vibrations, Noise, and Stator Current Spectral Analysis,” MSc Thesis, Dept of Mechanical Engineering University of Los Andes, Bogota Colombia, 2000.
R. Schoem, T. Habetler , F. Kamran and R. Bartheld, “Motor Bearing Damage Detection Using Stator Current Monitoring,” IEEE Transaction Industrial Application, pp. 1274-1279, 1995.
M. Benbouzid, R. Beguenane and M. Viera, “Induction motor asymmetrical fault detection using advanced signal processing techniques,” IEEE Transaction Energy Conversion, Vol. 14, No. 2, pp. 146-152, 1999.
J. R. Cameron, WT. Thomson WT. A. Dow, “Vibration and current monitoring for detecting air gap eccentricity in large induction machine IEEE Proceedings Vol. 133B, No. 3, pp. 155-163, 1986.
J.Penman , H. G. Seding , B. A. Lioyd & W. T. Fink, “ Detection and location of interturn short circuits in stator winding of operating motor, IEEE Transaction Energy Conversion, Vol. 9 , No. 4, , pp. 652-658, December 1994.
J. Sottile & J. L Kohler , “ An online method to detect incipient failures of turn insulation in random wound motors IEEE transactions energy Conversion vol. 8, No. 4, pp. 762-768, December 1993.
M. Aderiano Da Silva , J.Richard Povinelli & Nabeel , A. O. Demerdash, “Induction Machines broken bar and stator short circuit fault diagnosis based on three phase stator current envelopes. “IEEE transaction Ind Electron, Vol. 55, No. 3, pp. 1310-1318, March 2008.
H. Douglas, P. Pillay & A. K. Ziarani, “A New algorithm for transient motor current signature analysis using wavelets, IEEE Transactions Industrial Application Vol. 40, pp. 1361-1368, September/October 2004.
M. E. H. Benbouzid, Michella Vieira & Celine Theys ,”Induction motor fault detection and localization using stator current advanced signal processing techniques,” IEEE Transaction Power Electrons,Vol. 14, pp. 14-22, January1999. [13] T. W. S. Chow & S. Hai, “Induction machine fault diagnostic analysis with wavelet technique, “IEEE Transaction Industrial Electron, Vol. 51, No. 3, pp. 558-565, January 2004.
W. Thomson & M. Fenger, “Current signature analysis to detect induction motor faults, “IEEE Industrial Application magazine Vol. 7, No. 4, pp. 26-34, July/August 2001.
P. Jover Rodrgez, A . Arkkio, “ detection of stator winding fault in induction machine using fuzzy logic,” Applied soft computing, Vol. 8, No. 2, pp. 1112-1120, 2008.
F. Felippetti, G. Franceschini and C. Tassoni, “ A survey of AI techniques approach for induction machine online diagnostic in proceeding PEMC 96, Vol. 2, pp. 314-318, 1996.
M. Awadallah and M. Marcos, “Application of AI tools in fault diagnosis of electrical machines and drives – An overview,” IEEE transaction Energy Conversion vol. 18, No. 2, pp. 245-251, June 2003.
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