Contrôle et diagnostic d’une machine à induction sans capteur en utilisant des techniques avancées d’analyse et de traitement

Les différentes techniques d'analyse et diagnostic des défauts dans la machine asynchrone sont soumises à plusieurs difficultés, en particulier lors du fonctionnement de la machine en boucle fermée. Pour cela, l'objectif de la thèse est l'exploitation des méthodes fiables de détection des défauts ou des anomalies affectant les signaux mesurés en régimes stationnaires et non-stationnaires de la machine asynchrone. Les défauts considérés sont les cassures des barres rotoriques, le court-circuit entre spires statoriques et le défaut mixte stator/rotor. Deux méthodes sont utilisées pour la détection des défauts: la méthode classique à base de la transformée de Fourier et la méthode avancée à base de l’ondelette. L'étude est menée en fonctionnement de la machine en boucle fermée, où deux techniques de commande sans capteur de vitesse utilisant l'estimateur de type Luenberger sont considérées à savoir le contrôle direct du couple (DTC) et la commande par mode glissant (MG). Pour cela, divers tests de robustesse de la commande de la machine en défaut sont effectués à savoir les variations paramétriques et le fonctionnement à faible vitesse. Les résultats obtenus montrent clairement l'efficacité de cette technique dans la possibilité d'extraire les signatures du courant statorique pour détecter et localiser les défauts en régime stationnaire et non stationnaire

Broken rotor bars fault detection in induction motors using FFT: simulation and experimentally study

This paper presents the fault detection of broken rotor bars based on the analysis technique, such as the fast Fourier transform (FFT), which utilize the steady-state spectral components of the stator quantities is considered. This technique has been given expected results, the accuracy of this technique depends on the loading conditions and constant speed of the motor. This method shows good theoretical and experimental results

Broken Rotor Bars Fault Detection in Induction Motors Using FFT: Simulation and Experimentally Study

This paper presents the fault detection of broken rotor bars based on the analysis technique, such as the Fast Fourier transform (FFT), which utilize the steady-state spectral components of the stator quantities is considered. This technique has been given expected results, the accuracy of this technique depends on the loading conditions and constant speed of the motor. This method shows good theoretical and experimental results. Cite as: Kechida, R., Menacer, A., Cherif, H. (2019). Broken Rotor Bars Fault Detection in Induction Motors using FFT: Simula-tion and Experimentally Study. Algerian Journal of Engineering and Technology, 1(1), 019-024.  https://doi.org/10.5281/zenodo.3595143 References Ayhan, B. Trussell, H.J. Chow, Mo-Yuen. Song, Myung-Hyun (2008). On the use of a lower sampling rate for broke rotor bar detection with DTFT and AR-based spectrum methods. IEEE Trans. Industrial Electronics, 55(3), 1421-1434. Bachir, S. Tnani, S Trigeassou. J-C. and Champenois, G. (2006). Diagnosis by parameter estimation of stator and rotor faults occurring in induction machines. IEEE Transitions Industrial Electronics, 53(3), 963–973. Bellini, A. Filippetti, F. Tassoni, C. and Capolino. G.A. (2008). Advances in diagnostic techniques for induction machines. IEEE Transactions on Industrial Electronics, 55(12), 4109-4125. Silva, A.M.D. Povinelli, R. J. and Demerdash, N. A. O. (2008). Induction machine broken bar and stator short-circuit fault diagnostics based on three-phase stator current envelopes. IEEE Transactions on Industrial Electronics, 55(3), 1310–1318. Bossio, G. R. De Angelo, C. Bossio, H. J. M. Pezzani, C. M. and García, G.O.(2009). Separating broken rotor bars and load oscillations on im fault diagnosis through the instantaneous active and reactive currents. IEEE Transactions on Industrial Electronics, 56(11), 4571–4580. Bouzida, A. Touhami, O. Ibtiouen, R. Belouchrani, A. Fadel, M. and Rezzoug, A. (2011). Fault Diagnosis in industrial induction Machines through Discrete Wavelet Transform. IEEE Transactions Industrial Electronics, 58(9), 4385–4395. Sadeghian, A. Ye, Z. and Wu, B. (2009). Online detection of broken rotor bars in induction motors by wavelet packet decomposition and artificial neural networks. IEEE Transactions Instrumentation and Measurement, 58(7), 2253-2263. Cusidó, J. Romeral, L. Ortega, J. A. Rosero, J. A. Garcia Espinosa, A. (2008). Fault detection in induction machines using power spectral density in wavelet decomposition. IEEE Trans. Industrial Electronics, 55(3), 633-643. Zhou, W. Habetler, T. G. and Harley, R.G. (2007). Stator current based bearing fault detection techniques: A general review, IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, pp. 7-10. Blodt, M. Granjon, P. Raison, B. Rostaing, G. (2008). Models for bearing damage detection in induction motors using stator current monitoring. IEEE Transactions on Industrial Electronics, 55(4), 1813-1822. Douglas, H. Pillay, P and Ziarani, A. K. (2005). Broken rotor bar detection in induction machines with transient operating speeds. IEEE Transactions on Energy Conversion, 20(1), 135-141. Antonio-Daviu, J.A. Riera-Guasp, M. Floch, J. R. Palomares, M.P.M. (2006). Validation of a new method for the diagnosis of rotor bar failures via wavelet transform in industrial induction machines. IEEE Transactions on Industry Applications, 42(4), 990–996. Ordaz-Moreno, A. Romero-Troncoso, R.J. Vite-Frias, J.A. Rivera-Gillen, J.R. Garcia-Perez, A. (2008). Automatic online diagnosis algorithm for broken-bar detection on induction motors based on discrete wavelet transform for FPGA implementation. IEEE Transactions on Industrial Electronics, 55(5), 2193-2202. Cusido, J. Rosero, J. Aldabas, E. Ortega, L. Romeral, J.A. (2006). New fault detection techniques for induction motors. Electrical Power Utilization Quality and, Magazine, 11(1), 39-45. Caruso, G. Iannuzzi, D. Maceri, F. Pagano, E. Piegari, L. (2008). Torsional eigenfrequency identification of squirrel cage rotors of induction motors. International Symposium on Power Electronics, Electrical Drives, Automation and Motion, 1271–1275. Benbouzid, M.E.H. (2000). A review of induction motors signature analysis as a medium for faults detection. IEEE Trans Indus Elect, 47(5), 984–993. Li, W. (2006). Detection of induction motor faults: A comparison of stator current, vibration and acoustic methods. Journal of Vibration and Control. 12(2). 165

Predictive torque control of induction motor for rotor bar faults diagnosis

Unlike DC motors and synchronous motors, they are maintenance-free motors due to the absence of brushes, commutators and slip rings. Induction motors can be operated in polluted and explosive environments as they do not have brushes which can cause sparks. In this paper, the performance of two control techniques, namely direct torque control (DTC) and predictive torque control (PTC), are compared in transient and static states when applied to a faulty induction machine (IM). The current and torque ripples is evaluated in a healthy machine, as well as in the presence of faults, at various speed and load values. During the transient state, the objective is to assess the method that provides the optimal dynamic response, which is achieving the desired speed without any overshooting while during the static state, the objective is to minimize torque ripple and harmonics in the stator current. The Discrete Wavelet Transform (DWT) is used to analyze stator phase current. In addition, the energy eigen value (EEV) analysis has been used to determine the fault severity. The healthy and faulty systems are simulated using Matlab/Simulink for the two control methods. The results show the superiority of the PTC method compared to the DTC. A comparison of the proposed control method with other works reported in the literature is performed to verify the superiority of the proposed strategy.</p