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Stator Current Model Validation for Rotor Faults Diagnosis

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Early rotor faults diagnosis is essential for induction motor life. The reason for which several signal processing diagnosis methods have been proposed. In order to enhance these methods, it is necessary to use a reliable mathematical model of the physical processed signal. Unfortunately, the stator current is usually obtained from an expensive measurement set-up or a complex modelling of the electric motor. The aim of this paper is therefore to use a model of the motor stator current under rotor fault condition. In other words, the searched model must take into account the effects of the fault. Indeed, it has been verified that the effect of a fault is reflected by an amplitude modulation as well as a phase modulation of the stator current corresponding respectively, to a load torque oscillation and an eccentricity in the air-gap. The paper will also investigate the impact of the severity of the rotor fault on these two types of modulation. Finally, a comparison study between both the simulated signal and the experimental signal is carried out to illustrate the merits of the model and its effectiveness.
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Diagnosis; Induction motor; Modelling; Stator Current; Rotor Fault; Modulation

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Elbouchikhi, E., Choqueuse, V., Benbouzid, M., Condition Monitoring of Induction Motors Based on Stator Currents Demodulation, (2015) International Review of Electrical Engineering (IREE), 10 (6), pp. 704-715.

Bazi, S., Nait Said, M., Extreme Learning Machines and Particle Swarm Optimization for Induction Motor Faults Detection and Classification, (2015) International Review of Electrical Engineering (IREE), 10 (4), pp. 501-509.

Elez, A., Car, S., Tvorić, S., Air Gap Magnetic Field – Key Parameter for Synchronous and Asynchronous Machine Fault Detection, (2013) International Review of Electrical Engineering (IREE), 8 (3), pp. 981-988.

A. H. Boudinar, N. Benouzza, A. Bendiabdellah and M. E. A. Khodja, Induction Motor Bearing Fault Analysis Using a Root-MUSIC Method, in IEEE Transactions on Industry Applications, Vol. 52, (Issue 5): pp. 3851-3860, Sept.-Oct. 2016.

Rahmoune, C., Benazzouz, D., Monitoring Gear Fault by Using Motor Current Signature Analysis and Fast Kurtogram Method, (2013) International Review of Electrical Engineering (IREE), 8 (2), pp. 616-625.

E. Elbouchikhi et al. “Motor current signal analysis based on matched subspace detector,” IEEE Trans. Instrumentation and Measurement, Vol. 66, (Issue 12): 3260–3270, December 2017.

Y. Trachi et al., “Induction machines fault detection based on subspacespectral estimation,” IEEE Trans. Industrial Electronics, Vol. 63, (Issue 9): 5641–5651, September 2016.

Aimer, F., Boudinar, A., Bendiabdellah, A., Use of the Short Time Fourier Transform for Induction Motor Broken Bars Detection, (2013) International Review on Modelling and Simulations (IREMOS), 6 (6), pp. 1879-1883.

A. H. Bonnett, C. Yung, Increased efficiency versus increased reliability, IEEE Ind. Appl. Mag, Vol. 14, (Issue 1): 29-36, 2008.

M. Blodt, J. Regnier, J. Faucher, Distinguishing Load Torque oscillation and Eccentricity Faults in Induction Motor Using Stator Current Wigner Distribution, IEEE Trans on Industry Application, Vol. 45, (Issue 6), 2009.

M. Blodt, M. Chabert, J. Regnier, J. Faucher, Mechanical load fault detection in induction motors by stator Current Time-Frequency Analysis, IEEE Trans on Industry applications, Vol. 42, (Issue 6): 1454-1463, 2006.

Y. Maouche, A. Boussaid, M. Boucherma, A. Khezzar, Modeling and simulation of stator turn faults Detection based on stator circular current and neutral voltage, 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED): pp. 263-268, Valencia, Spain, Aug. 2013.

F. Duan, R Zivanovic, A Model for Induction Motor with Stator Faults, 22nd Australasian Universities Power Engineering Conference (AUPEC): 1-5, Bali, Indonesia, Sept. 2012.

B. Xu, L. Sun, L. Xu, G. Xu, Improvement of the Hilbert Method via ESPRIT for Detecting Rotor Fault in Induction Motors at Low Slip, IEEE Trans On Energy Conversion, Vol. 28, (Issue 1): 225-233 , 2013.

B. Trajin, M. Chabert, J. Regnier, J. Faucher, Hilbert versus Concordia transform for three-phase machine stator current time-frequency monitoring, Mechanical Systems and Signal Processing, Vol. 23, (Issue 8): 2648-2657, 2009.

A.H. Boudinar, N. Benouzza, A. Bendiabdellah, Induction motor cracked rotor bars fault analysis using an improved Root-MUSIC method,3rd International Conference on Control, Engineering and Information Technology (CEIT), Tlemcen, Algeria. 25-27 May 2015.

E. H. El Bouchikhi, V. Choqueuse, M. Benbouzid and J. A. Antonino-Daviu, Stator current demodulation for induction machine rotor faults diagnosis,2014 First International Conference on Green Energy ICGE 2014, Sfax, 2014, pp. 176-181.

M. Sahraoui, A. J. M. Cardoso and A. Ghoggal, The Use of a Modified Prony Method to Track the Broken Rotor Bar Characteristic Frequencies and Amplitudes in Three-Phase Induction Motors,in IEEE Transactions on Industry Applications, vol. 51, no. 3, pp. 2136-2147, May-June 2015.

A.F. Aïmer, A.H. Boudinar, N. Benouzza, A. Bendiabdellah, Simulation and Experimental Study of Induction Motor Broken Rotor Bars Fault Diagnosis using Stator Current Spectrogram, In Proc. of IEEE 3rd International Conference on Control, Engineering & Information Technology (CEIT), 25-27 May, 2015, Tlemcen, Algeria.

I. Ouachtouk, S. El Hani, S. Guedira, L. Sadiki and K. Dahi, Modeling of squirrel cage induction motor a view to detecting broken rotor bars faults, Electrical and Information Technologies (ICEIT), 2015 International Conference on, Marrakech, pp. 347-352.

A. Bendiabdellah, A.H. Boudinar, N. Benouzza, M. Khodja, The enhancements of broken bar fault detection in induction motors. In Proc. of Intl Aegean Conference on Electrical Machines & Power Electronics (ACEMP), Intl Conference on Optimization of Electrical & Electronic Equipment (OPTIM) & Intl Symposium on Advanced Electromechanical Motion Systems (ELECTROMOTION), Side, Turkey, 02-04 Sep. 2015.

S. B. Lee et al., Identification of False Rotor Fault Indications Produced by Online MCSA for Medium-Voltage Induction Machines, IEEE Transactions on Industry Applications, 2016, Vol. 52 (Issue1): 729-739.

J. R. Stack, T. G. Habetler, R. G. Harley, fault classification and fault signature production for rolling element bearings in electric machines, IEEE Trans on industry applications, Vol. 40, (Issue 3): 375-739, 2004.

R. R. Schoen, T. G. Habetler, Effect of time varying loads on rotor faults detection in induction machines, IEEE Trans. on Industry Application, Vol. 31, (Issue 4): 900-906, 1995.

M. El Amine Khodja, A. H. Boudinar, N. Benouzza and A. Bendiabdellah, Stator current modeling of an induction motor for rotor faults diagnosis,2016 IEEE International Power Electronics and Motion Control Conference (PEMC), Varna, 2016.

Cherif, B., Bendiabdellah, A., Khelif, M., Detection of Open-Circuit Fault in a Three-Phase Voltage Inverter Fed Induction Motor, (2016) International Review of Automatic Control (IREACO), 9 (6), pp. 374-382.

Benaouda, O., Bendiabdellah, A., Cherif, B., Contribution to Reconfigured Multi-Level Inverter Fed Double Stator Induction Machine DTC-SVM Control, (2016) International Review on Modelling and Simulations (IREMOS), 9 (5), pp. 317-328.


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