DETECTION OF STATIC AIR-GAP ECCENTRICITY IN THREE PHASE INDUCTION MOTOR BY USING ARTIFICIAL NEURAL NETWORK (ANN)
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Abstract
This paper presents the effect of the static air-gap eccentricity on the performance of a three phase induction motor .The Artificial Neural Network (ANN) approach has been used to detect this fault .This technique depends upon the amplitude of the positive and negative harmonics of the frequency. Two motors of (2.2 kW) have been used to achieve the actual fault and desirable data at no-load, half-load and full-load conditions. Motor Current Signature analysis (MCSA) based on stator current has been used to detect eccentricity fault. Feed forward neural network and error back propagation training algorithms are used to perform the motor fault detection. The inputs of artificial neural network are the amplitudes of the positive and negative harmonics and the speed, and the output is the type of fault. The training of neural network is achieved by data through the experiments test on healthy and faulty motor and the diagnostic system can discriminate between “healthy” and “faulty” machine.
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N. A. Al- Nuaim, H. A. Toliyat “A Novel Method For Modeling Dynamic Air-Gap Eccentricity in Synchronous Machines Based on Modified Winding Function Theory” IEEE Transaction on Energy Conversion, Vol.13, 2, June 1998.
H. A. Toliyat and M. A. Haji “Pattern Recognition- Technique for Induction Machines Rotor Fault Detection Eccentricity and Broken Bar Fault” Department of Electrical Engineering Texas A&M IEEE Transactions on Energy Conversion, Vol 2001.
R. R. Schoen, T. G. Habetler “An Unsupervised, On_Line System for Induction Motor Fault Detection Using Stator Current Monitoring” IEEE Georgia Institute of Technology 1994.
X. Huang, T. G. Habetler, R. G. Harley, 2004,"Detection of Rotor Eccentricity Faults In closed-Loop Drive-Connected Induction Motors Using an Artificial Neural ", IEEE 35th Annual Power Electronics Specialists Conference-PESC, Aachen, Germany, June 2004, 20-25, Vol.2, pp. 913-918.
F. Filippetti, G. Franceschini, C. Tassoni "Neural Networks Aided On-Line Diagnostics of Induction Motor Rotor Faults", IEEE Transaction on industry Applications, Vol.31, Issue 4, pp.892-899. 2005.
[7] H. A. Toliyat and S. Nandi “Condition Monitoring and Fault Diagnosis of Electrical Motors –A Review ” IEEE Transactions on Energy Conversion, Vol.20 NO.4, December 2005.
D. G. Dorrell, W. T. Thomson and S. Roach, “Analysis of Air-Gap Flux, Current, Vibration Signals as a Function of The Combination of Static and Dynamic Air-gap Eccentricity in 3-Phase Induction Motors”, IEEE Trans. Ind. Applns. n., Vol. 33, No.1, pp. 24-34, 1997.
Barbour and W.T. Thomson, “Finite Element Study of Rotor Slot Designs With Respect to Current Monitoring For Detecting Static Air gap Eccentricity in Squirrel-Cage Induction Motor” IEEE-IAS annual meeting conference recordings, pp. 112-119, New Orleans, Louisiana,Oct.5-8, 1997.
S. Nandi and H. A. Toliyat, “Detection of Rotor Slot and Other Eccentricity Related Harmonics In a Three Phase Induction Motor With Different Rotor Cages” IEEE Trans Energy Convers. Vol. 16 , no. 3 ,pp.253-260, Sep.2001.
S. Nandi, R. M. Bharadwaj, H. A. Toliyat, A. G. Parlos “ Performance Analysis of a Three Phase Induction Motor Under Incipient Mixed Eccentricity Condition”, IEEE Trans. Energy Converse. Vol. 17. No.3. pp 392-399. Sep. 2002.