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Assessment of Time Frequency Color Index for Electrical Machines Diagnosis and Fault Severity

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Improving availability and reliability of Induction Machine (IM) implies the minimization and the prediction of the maintenance operations. In this paper, we focus on detection and location of the rotor electrical faults in IM. This kind of fault generates non-stationary signatures in the stator current of the machine. Advanced signal processing techniques are required to deal with that type of signals. In this context, several recent studies suggest the use of Time-Frequency Representations (TFR) for the diagnosis of IMs' faults. This work presents a comparative study of some linear TFR. This last relies on good experimental results conducted on a test bench using a wound rotor induction machine operating in no load condition. The purpose is to showcase the TFR performances in faults detection. Static indicator is used to check the fault severity.
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Asynchronous Machine; Fault Detection; Location; Rotor Electrical Faults; Non-Stationary; Time-Frequency Representations; Diagnosis; Fault Severity

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M. Benbouzid, “A review of induction motors signature analysis as a medium for faults detection,” IEEE Transactions on Industrial Electronics, vol. 47, no. 5, pp. 984–993, 2000.

P. Flandrin, Time-frequency/time-scale analysis. Academoc Press, 1998.

André QUINQUIS. Cornel IOANA. Janvier 2002 ‘’Représentation temps fréquence et temps échelle’.

B. Yazici and G. Kliman, “An adaptive statistical time-frequency method for detection of broken bars and bearing faults in motors using stator current,” IEEE Transactions on Industry Applications, vol. 35, no. 2, pp. 442–452, 1999

M. Benbouzid and G. Kliman, “What stator current processing based technique to use for induction motor rotor faults diagnosis?” IEEE Transactions on Energy Conversion, vol. 18, no. 2, pp. 238–244, 2003

Nandi, S., Toliyat, H.A., Li, X., ―Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review, Energy Conversion, IEEE Transactions 20(4),(2005), pp. 719 – 729

Bellini, A, Filippetti, F., Franceschini, G., Tassoni, C., Kliman, G.B., “Quantitative evaluation of induction motor broken bars by means of electrical signature analysis”, Industry Applications, IEEE Transactions 37(5) (2001), pp. 1248 – 1255.

Schoen, R.R., Habetler, T.G.,”Effects of time-varying loads on rotor fault detection in induction machines”, Industry Applications, IEEE Transactions 31(4)(1995), pp. 900 – 906

G. Didier, E. Ternisien, O. Caspary, H. Razik, “A new approach to detect broken rotor bars in induction machines by current spectrum analysis”, Mechanical Systems and Signal Processing 21 (2007), pp. 1127–1142.

Kia, S.H., Henao, H., Capolino, G.-A.”A High-Resolution Frequency Estimation Method for Three-Phase Induction Machine Fault Detection” Industrial Electronics, IEEE Transactions 54(4)(2007, pp.) 2305 – 2314.

Aroquiadassou, G., Henao, H., Capolino, G.-A.”Experimental Analysis of the dq0 Stator Current Component Spectra of a 42V Fault-Tolerant Six-Phase Induction Machine Drive with Opened Stator Phases”, IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED 2007), 6-8 Sept. 2007, pp. 52 – 57.

Filippetti, F.; Franceschini, G.; Tassoni, C.; Vas, P. “AI techniques in induction machines diagnosis including the speed ripple effect” IEEE Trans. Indus. Appli, Vol 34, n° 1, pp 98-108, 1998

Hein, D. “Identification de la machine asynchrone en vue du diagnostic de panes” these CNAM de Paris,centre Associe de Metz, 1998.

W.T. Thomson, “on-line current monitoring to detect electrical and mechanical faults in three-phase induction motor drives,” in Proceedings of the International Conference on Life Management of Power Plants, Edinb urgh (UK), pp. 6673, December 1994.

M. Blödt, M. Chabert, J. Regnier, J. Faucher, and B. Dagues, “ Detection of mechanical load faults in induction motors at variable speed using stator current time-frequency analysis,” in Proceedings of the IEEE SDEMPED’05, Vienna (Austria), pp. 1-6, September 2005

S. Rajagopalan, J. Aller, J. A. Restrepo, T. Habetler, and R. Harley,“Detection of rotor faults in brushless dc motors operating undernonstationary conditions,” IEEE Transactions on Industry Applications,vol. 42, no. 6, 2006.

J.Cusido, L. Romeral, J. Ortega, J. Rosero, and A. Espinosa, “Fault detection in induction machines using power spectral density in wavelet decomposition,” IEEE Transactions on Industrial Electronics, vol. 55, no. 2, pp. 633–643, 2008.

S. Kia, H. Henao, and G. Capolino, “Torsional vibration assessment using induction machine electromagnetic torque estimation,” IEEE Transactions on Industrial Electronics, vol. 57, no. 1, pp. 209–219, 2010.

S. Rajagopalan, J. Aller, J. A. Restrepo, T. Habetler, and R. Harley, “Analytica-wavelet-ridge-based detection of dynamic eccentricity in brushless direct current (bldc) motors functioning under dynamic operating conditions,” IEEE Transactions on Industrial Electronics, vol. 54, no. 3, 2007.

J.Pons-Llinares, J. Antonino-Daviu, M. Riera-Guasp, M. PinedaSanchez, and V. Climente-Alarcon, “Induction motor diagnosis based on a transient current analytic wavelet transform via frequency b-splines,” IEEE Transactions on Industrial Electronics, vol. 58, no. 5, pp. 1530–1544, 2011.

M. Blodt, M. Chabert, J. Regnier, and J. Faucher, “Mechanical load fault detection in induction motors by stator current time-frequency analysis,”IEEE Transactions on Industry Applications, vol. 42, no. 6, pp. 1454–1463, 2006.

A. Espinosa, J. Rosero, J. Cusido, L. Romeral, and J. Ortega, “Fault detection by means of hilbert-huang transform of the stator current in apmsm with demagnetization,” IEEE Transactions on Energy Conversion, vol. 25, no. 2, pp. 312–318, 2010

Bazi, S., Nait Said, M.S., 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.

Irfan, M., Saad, N., Ibrahim, R., Asirvadam, V.S., An intelligent diagnostic condition monitoring system for AC motors via instantaneous power analysis, (2013) International Review of Electrical Engineering (IREE), 8 (2), pp. 664-672.

Elkholy, M.M., Elhameed, M.A., Braking of three phase induction motors by controlling applied voltage and frequency based on particle swarm optimization technique, (2015) International Review of Automatic Control (IREACO), 8 (2), pp. 106-112.

Ahmad, N.H.T., Abdullah, A.R., Abidullah, N.A., Jopri, M.H., Analysis of power quality disturbances using spectrogram and S-Transform, (2014) International Review of Electrical Engineering (IREE), 9 (3), pp. 611-619.


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