### Condition Monitoring of Induction Motors Based on Stator Currents Demodulation

DOI: https://doi.org/10.15866/iree.v10i6.7594

#### Abstract

Over the past several decades, induction machine condition monitoring have received increasing attention from researchers and engineers. Several induction machine faults detection techniques have been proposed that are based on vibration, temperature, and currents/power monitoring, etc. Motor current signature analysis is a cost-effective method, which has been widely investigated. Specifically, it has been demonstrated that mechanical and electrical induction machine faults can be effectively diagnosed using stator currents demodulation. Therefore, this paper proposes to investigate the use of demodulation techniques for bearing faults detection and diagnosis based on stator currents analysis. If stator currents are assumed to be mono-component signals, the demodulation techniques include the synchronous demodulator, the Hilbert transform, the Teager energy operator, the Concordia transform, the maximum likelihood approach and the principal component analysis. For a multi-component signal, further preprocessing techniques are required such as the Empirical Mode Decomposition(EMD) or the Ensemble EMD (EEMD).The studied demodulation techniques are demonstrated for bearing faults diagnosis using simulation data, issued from a coupled electromagnetic circuits approach-based simulation tool, and experiments on a 0.75kW induction machine test bed. *Copyright © 2015 Praise Worthy Prize - All rights reserved.*

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O. Duque-Perez, L.-A. Garcia-Escudero, D. Morinigo-Sotelo, P.-E. Gardel, and M. Perez-Alonso, “Analysis of fault signatures for the diagnosis of induction motors fed by voltage source inverters using ANOVA and additive models,” Electric Power Systems Research, vol. 121, pp. 1–13, April 2015.

http://dx.doi.org/10.1016/j.epsr.2014.11.021

M. Drif and A. Cardoso, “Stator fault diagnostics in squirrel cage three phase induction motor drives using the instantaneous active and reactive power signature analyses,” IEEE Trans.Industrial Informatics, vol. 10, n°2, pp. 1348–1360, May 2014.

http://dx.doi.org/10.1109/tii.2014.2307013

X. Dai and Z. Gao, “From model, signal to knowledge: A data-driven perspective of fault detection and diagnosis,” IEEE Trans. Industrial Informatics, vol. 9, n°4, pp. 2226–2238, November 2013.

http://dx.doi.org/10.1109/tii.2013.2243743

A. Garcia-Perez, R. de Jesus Romero-Troncoso, E. Cabal-Yepez, and R. Osornio-Rios, “The application of high-resolution spectral analysis for identifying multiple combined faults in induction motors,” IEEE Trans. Industrial Electronics, vol. 58, n°5, pp. 2002–2010, May 2011.

http://dx.doi.org/10.1109/tie.2010.2051398

S. Nandi, H. A. Toliyat, and X. Li, “Condition monitoring and fault diagnosis of electrical motors - a review,” IEEE Trans. Energy Conversion, vol. 20, n°4, pp. 719–729, December 2005.

http://dx.doi.org/10.1109/tec.2005.847955

M. Seera, C. P. Lim, D. Ishak, and H. Singh, “Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid fmm-cart model,” IEEE Trans. Neural Networks and Learning Systems, vol. 23, n°1, pp. 97 108, January 2012.

http://dx.doi.org/10.1109/tnnls.2011.2178443

V. Choqueuse, M. E. H. Benbouzid, Y. Amirat, and S. Turri, “Diagnosis of three-phase electrical machines using multidimensional demodulation techniques,” IEEE Trans Industrial Electronics, vol. 59, n°4, pp. 2014–2023, April 2011.

http://dx.doi.org/10.1109/tie.2011.2160138

R. R. Obaid and T. G. Habetler, “Current-based algorithm for mechanical fault detection in induction motors with arbitrary load conditions,” in Proceedings of the 2003 IEEE IAS Annual Meeting, Salt Lake City, UT, Oct. 2003, pp. 1347–1351.

http://dx.doi.org/10.1109/ias.2003.1257726

M. E. H. Benbouzid and G. Kliman, “What stator current processing based technique to use for induction motor rotor faults diagnosis?” IEEE Trans. Energy Conversion, vol. 18, n°2, pp. 238–244, June 2003.

http://dx.doi.org/10.1109/tec.2003.811741

N. Feki, G. Clerc, and P. Velex, “Gear and motor fault modeling anddetection based on motor current analysis,” Electric Power Systems Research, vol. 95, pp. 28–37, February 2013.

http://dx.doi.org/10.1016/j.epsr.2012.08.002

A. Bellini, F. Filippetti, C. Tassoni, and G. A. Capolino, “Advances in diagnostic techniques for induction machines,” IEEE Trans. Industrial Electronics, vol. 55, n°12, pp. 4109–4126, December 2008.

http://dx.doi.org/10.1109/tie.2008.2007527

M. E. H. Benbouzid, “A review of induction motors signature analysis as a medium for faults detection,” IEEE Trans. Industrial Electronics,vol. 47, n°5, pp. 984–993, October 2000.

http://dx.doi.org/10.1109/41.873206

E. Elbouchikhi, V. Choqueuse and M.E.H. Benbouzid, “Induction machine diagnosis using stator current advanced signal processing,” International Journal on Energy Conversion, vol. 3, n°3, pp. 76–87, May 2015.

M. Blodt, J. Regnier, and J. Faucher, “Distinguishing load torque oscillations and eccentricity faults in induction motors using stator current Wigner distributions,” IEEE Trans. Industry Applications, vol. 45, no. 6, pp. 1991–2000, November/December 2009.

http://dx.doi.org/10.1109/tia.2009.2031888

M. Blodt, P. Granjon, B. Raison, and G. Rostaing, “Models for bearing damage detection in induction motors using stator monitoring,” IEEE Trans. Industrial Electronics, vol. 55, n°4, pp. 1813–1822, April 2008.

http://dx.doi.org/10.1109/tie.2008.917108

B. Heller and V. Hamata, Harmonic Field Effects in Induction Machine (Elsevier Scientific Publishing Company, 1977).

M. Blodt, D. Bonacci, J. Regnier, M. Chabert, and J. Faucher, “Online monitoring of mechanical faults in variable-speed induction motor drives using the Wigner distribution,” IEEE Trans. Industrial Electronics, vol. 55, n°2, pp. 522–533, February 2008.

http://dx.doi.org/10.1109/tie.2007.911941

I. Jaksch, “Fault diagnosis of three-phase induction motors using envelopeanalysis,” in Proceedings of the 2003 IEEE SDEMPED, Atlanta (USA), August 2003, pp. 289–293.

http://dx.doi.org/10.1109/demped.2003.1234588

I. Jaksch and J. Bazant, “Demodulation methods for exact inductionmotor rotor fault diagnosis,” in Proceedings of the 2005 IEEE SDEMPED, Vienna (Austria), September 2005, pp.1–5.

http://dx.doi.org/10.1109/demped.2005.4662494

I. Jaksch and P. Fuchs, “Rotor cage faults detection in inductionmotors by motor current demodulation analysis,” in Proceedings of the 2007 IEEE SDEMPED, Cracow (Poland), 2007, pp. 247–252.

http://dx.doi.org/10.1109/demped.2007.4393103

I. Jaksch and P. Fuchs, “Demodulation analysis for exact rotor faults detection underchanging parameters,” in Proceedings of the 2009 IEEE SDEMPED, Cargèse (France),August-September 2009, pp. 1–7.

http://dx.doi.org/10.1109/demped.2009.5292800

M. Pineda-Sanchez and M. Riera-Guasp, “Instantaneous frequency of the left sideband harmonic during the start-up transient: A new methodfor diagnosis of broken bars,” IEEE Trans. Industrial Electronics,vol. 56, n°11, pp. 4557–4570, November 2009.

http://dx.doi.org/10.1109/tie.2009.2026211

A. Mohanty and C. Kar, “Fault detection in a multistage gearbox by demodulation of motor current waveform,” IEEE Trans.Industrial Electronics, vol. 53, n°4, pp. 1285–1297, June 2006.

http://dx.doi.org/10.1109/tie.2006.878303

M. Blodt, M. Chabert, J. Regnier, and J. Faucher, “Mechanical load fault detection in induction motors by stator current time-frequency analysis,”IEEE Trans. Industry Applications, vol. 42, n°6, pp. 1454–1463, November-December 2006.

http://dx.doi.org/10.1109/tia.2006.882631

M. Begovic, P. Djuric, S. Dunlap, and A. Phadke, “Frequency tracking in power networks in the presence of harmonics,” IEEE Trans. Power Delivery, vol. 8, n°2, pp. 480–486, April 1993.

http://dx.doi.org/10.1109/61.216849

M. Akke, “Frequency estimation by demodulation of two complex signals,” IEEE Trans. Energy Conversion, vol. 12, n°1, pp. 157–163, January 1997.

http://dx.doi.org/10.1109/61.568235

R. Puche-Panadero, M. Pineda-Sanchez, M. Riera-Guasp, J. Roger-Folch, E. Hurtado-Perez, and J. Perez-Cruz, “Improved Resolution of the MCSA Method Via Hilbert Transform, Enabling the Diagnosis of Rotor Asymmetries at Very Low Slip,” IEEE Trans. EnergyConversion, vol. 24, n°1, pp. 52–59, March 2009.

http://dx.doi.org/10.1109/tec.2008.2003207

V. Climente-Alarcon, J. A. Antonino-Daviu, M. Riera-Guasp, R. Puche-Panadero, and L. Escobar, “Application of the Wigner–Ville distributionfor the detection of rotor asymmetries and eccentricity through highorderharmonics,” Electric Power Systems Research, vol. 91, pp. 28–36,October 2012.

http://dx.doi.org/10.1016/j.epsr.2012.05.001

J. Antonino-Daviu, S. Aviyente, E. G. Strangas, and M. Riera-Guasp, “Scale invariant feature extraction algorithm for the automatic diagnosis of rotor asymmetries in induction motors,”IEEE Trans. Industrial Informatics, vol. 9, n°1, pp. 100–108, February 2013.

http://dx.doi.org/10.1109/tii.2012.2198659

E. Cabal-Yepez, A. G. Garcia-Ramirez, R. J. Romero-Troncoso, A. Garcia-Perez, and R. A. Osornio-Rios, “Reconfigurable monitoringsystem for time-frequency analysis on industrial equipment throughSTFT and DWT,” IEEE Trans. Industrial Informatics, vol. 9, n°2, pp. 760–771, May 2013.

http://dx.doi.org/10.1109/tii.2012.2221131

P. Dash and S. Hasan, “A fast recursive algorithm for the estimation of frequency, amplitude, and phase of noisy sinusoid,”IEEE Trans. Industrial Electronics, vol. 58, n°10, pp. 4847 –4856, October 2011.

http://dx.doi.org/10.1109/tie.2011.2119450

B. Trajin, M. Chabert, J. Regnier, and J. Faucher, “Hilbert versus Concordia transform for three phase machine stator current time-frequency monitoring,” Mechanical Systems &Signal Processing, vol. 23, n°8, pp. 2648–2657, November 2009.

http://dx.doi.org/10.1016/j.ymssp.2009.05.015

M. Pineda-Sanchez, R. Puche-Panadero, M. Riera-Guasp, J. Perez-Cruz, J. Roger-Folch, J. Pons-Llinares, V. ClimenteAlarcon, and J. A.Antonino-Daviu, “Application of the Teager Kaiser energy operator tothe fault diagnosis of induction motors,” IEEE Trans. Energy Conversion, vol. 28, n°4, pp. 1036–1044, December 2013.

http://dx.doi.org/10.1109/tec.2013.2279917

M. Riera-Guasp, J. Antonino-Daviu, J. Rusek, and J. Roger-Folch, “Diagnosis of rotor asymmetries in induction motors based on the transient extraction of fault components using filtering techniques,”Electric Power Systems Research, vol. 79, n°8, pp. 1181–1191, August 2009.

http://dx.doi.org/10.1016/j.epsr.2009.02.009

J. Faiz, V. Ghorbanian, and B. M. Ebrahimi, “EMD-based analysis of industrial induction motors with broken rotor bars for identification of operating point at different supply modes,”IEEE Trans. Industrial Informatics, vol. 10, n°2, pp. 957–966, May 2014.

http://dx.doi.org/10.1109/tii.2013.2289941

Y. Amirat, V. Choqueuse, and M. Benbouzid, “EEMD-based wind turbine bearing failure detection using the generator stator currenthomopolar component,” Mechanical Systems & Signal Processing,vol. 41, n°1, pp. 667–678, December 2013.

http://dx.doi.org/10.1016/j.ymssp.2013.06.012

E. Elbouchikhi, V. Choqueuse, M. E. H. Benbouzid, J. F. Charpentier, and G. Barakat, “A comparative study of time-frequency representations for fault detection in wind turbine,” in Proceedings of the 2011 IEEE IECON, Melbourne (Australia), November 2011, pp. 3584–3589.

http://dx.doi.org/10.1109/iecon.2011.6119891

E. Elbouchikhi, V. Choqueuse, M. Benbouzid, and J. A. Antonino-Daviu, “Stator current demodulation for induction machine rotor faults diagnosis,” in Proceedings of the 2014 IEEEICGE, Sfax (Tunisia), March2014, pp. 176–181.

http://dx.doi.org/10.1109/icge.2014.6835418

D. Vakman, “On the analytic signal, the Teager-Kaiser energy algorithm, and other methods for defining amplitude and frequency,” IEEE Trans. Signal Processing, vol. 44, n°4, pp. 791–797, April 1996.

http://dx.doi.org/10.1109/78.492532

A. Oppenheim and R. Schafer, Discrete-Time Signal Processing(3rd ed.Prentice Hall, 2009).

B. Picinbono, “On instantaneous amplitude and phase of signals,” IEEE Trans. Signal Processing, vol. 45, n°3, pp. 552–560, March 1997.

http://dx.doi.org/10.1109/78.558469

L. MarpleJr, “Computing the discrete-time analytic? signal via FFT,”IEEE Trans. Signal Processing, vol. 47, no. 9, pp. 2600–2603, September 1999.

http://dx.doi.org/10.1109/78.782222

P. Maragos, J. Kaiser, and T. Quartieri, “On amplitude and frequency demodulation using energy operators,” IEEE Trans. SignalProcessing, vol. 41, n°4, pp. 1532–1550, April 1993.

http://dx.doi.org/10.1109/78.212729

P. Maragos, J. Kaiser, and T. Quatieri, “Energy separation in signalmodulations with application to speech analysis,” IEEE Trans.Signal Processing, vol. 10, n°41, pp. 3024–3051, October 1993.

http://dx.doi.org/10.1109/78.277799

J. M. Aller, A. Bueno, and T. Paga, “Power system analysis using space vector transformation,” IEEE Trans. Power Systems, vol. 17, n°4, pp. 957–965, November 2002.

http://dx.doi.org/10.1109/tpwrs.2002.804995

S. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory (Prentice-Hall signal processing series, 1993, 17th Printing).

V. Choqueuse, A. Belouchrani, E. Elbouchikhi, and M. Benbouzid, “Estimationof amplitude, phase and unbalance parameters in three-phase systems: analytical solutions, efficient implementation and performanceanalysis,” IEEE Trans. Signal Processing, vol. 62, n°16, pp. 4064–4076, 15 August 2014.

http://dx.doi.org/10.1109/tsp.2014.2333565

J. Pons-Llinares, J. Roger-Folch, and M. Pineda-Sanchez, “Diagnosis of eccentricity based on the hilbert transform of the startup transient current,” in Proceedings of the 2009 SDEMPED, Cargèse (France), August-September2009, pp. 1–6.

http://dx.doi.org/10.1109/demped.2009.5292787

H. Li, L. Fu, and Y. Zhang, “Bearing fault diagnosis based on Teagerenergy operator demodulation technique,” in Proceedings of the 2009 IEEE ICMTMA, Zhangjiajie (China), April 2009, pp. 594–597.

B. Xu, L. Sun, L. Xu, and G. Xu, “Improvement of the Hilbert method via ESPRIT for detecting rotor fault in induction motors at low slip,” IEEETrans. Energy Conversion, vol. 28, n°1, pp. 225–233, March 2013.

http://dx.doi.org/10.1109/tec.2012.2236557

V. Pires, J. Martin, and A. Pires, “Eigenvector/eignevalue analysis ofa 3D current referential fault detection and diagnosis of an induction motor,” Energy Conversion & Management, vol. 51, n°5, pp. 901–907, May 2010.

http://dx.doi.org/10.1016/j.enconman.2009.11.028

J. Rosero, L. Romeral, J. Ortega, and E. Rosero, “Short-circuit detection by means of empirical mode decomposition and Wigner-Ville distribution for PMSM running under dynamic condition,” IEEE Trans. Industrial Electronics, vol. 56, n°11, pp. 4534–4547, November 2009.

http://dx.doi.org/10.1109/tie.2008.2011580

S. Das, P. Purkait, and S. Chakravorti, “Space-vector characterization of induction motor operating conditions,” in Proceedings of the 2008 NPSC, Bombay (India), December 2008, pp. 512–517.

D. Diallo, M. Benbouzid, D. Hamad, and X. Pierre, “Fault detection and diagnosis in an induction machine drive: A pattern recognition approach based on concordia stator mean current vector,” IEEE Trans.Energy Conversion, vol. 20, n°3, pp. 512–519, September 2005.

http://dx.doi.org/10.1109/tec.2005.847961

H. Toliyat, M. Arefeen, and A. Parlos, “A method for dynamic simulation of air-gap eccentricity in induction machines,” IEEE Trans. IndustryApplications, vol. 32, n°4, pp. 910–918, July-August 1996.

http://dx.doi.org/10.1109/28.511649

E. Elbouchikhi, V. Choqueuse, and M. E. H. Benbouzid, “Current frequency spectral subtraction and its contribution to induction machines’ bearings condition monitoring,” IEEE Trans. Energy Conversion, vol. 28, n°1, pp. 135–144, March 2013.

http://dx.doi.org/10.1109/tec.2012.2227746

G. Joksimovic, M. Durovic, J. Penman, and N. Arthur, “Dynamic simulation of dynamic eccentricity in induction machines-winding function approach,” IEEE Trans. Energy Conversion, vol. 15, n°2, pp. 143–148, June 2000.

http://dx.doi.org/10.1109/60.866991

G. Houdouin, G. Barakat, B. Dakyo, E. Destobbeleer, and C. Nichita,“A coupled magnetic circuit based global method for the simulationof squirrel cage induction machines under rotor and stator faults,” inProceedings of the 2002 ELECTRIMACS, Montreal (Canada), August 2002, pp. 18–21.

A. Knight and S. Bertani, “Mechanical fault detection in a medium-sizedinduction motor using stator current monitoring,” IEEE Trans. EnergyConversion, vol. 29, n°4, pp. 753–760, December 2005.

http://dx.doi.org/10.1109/tec.2005.853731

R. Schoen, T. Habetler, F. Kamran, and R. Bartheld, “Motor bearing damage detection using stator current monitoring,” IEEE Trans. IndustryApplications, vol. 31, n°6, pp. 1274–1279, November-December 1995.

http://dx.doi.org/10.1109/28.475697

Z. Obeid, S. Poignant, J. Regnier, and P. Maussion, “Stator currentbased indicators for bearing fault detection in synchronous machine by statistical frequency selection,” in Proceedings of the 2011 IEEE IECON, Melbourne (Australia), November 2011, pp. 2036–2041.

http://dx.doi.org/10.1109/iecon.2011.6119621

G. Rilling, P. Flandrin, and P. Gonalves, “On empirical mode decompositionand its algorithms,” in Proceedings of the 2003 IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, Grado (Italia), 2003.

Y. Yu, Y. Dejie, and C. Junsheng, “A roller bearing fault diagnosis method based on EMD energy entropy and ANNS,”Journal of Sound andVibration, vol. 294, n°1-2, pp. 269–277, June 2006.

http://dx.doi.org/10.1016/j.jsv.2005.11.002

D. Yu, J. Cheng, and Y. Yang, “Application of EMD method and Hilbertspectrum to the fault diagnosis of roller bearings,”Mechanical Systems & SignalProcessing, vol. 19, n°2, pp. 259–270, March 2005.

http://dx.doi.org/10.1016/s0888-3270(03)00099-2

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