Fault diagnosis of induction motors rotor using current signature with different signal processing techniques
More details
Hide details
(LACoSERE) University of Laghouat, 03000, ALGERIA
Guezam Abdelhak   

(LACoSERE) University of Laghouat, 03000, ALGERIA
Submission date: 2021-11-04
Final revision date: 2022-03-16
Acceptance date: 2022-03-18
Online publication date: 2022-03-21
The popularity of asynchronous machines, particularly squirrel cage machines, stems from their inexpensive production costs, resilience, and low maintenance requirements. Unfortunately, potential flaws in these devices might have a negative impact on the facility's profitability and service quality. As a result, diagnostic tools for detecting flaws in these types of devices must be developed. Asynchronous machine problems can be diagnosed using a variety of methods. Signal processing techniques based on extracting information from characteristic quantities of electrical machine operation can provide highly useful information about flaws. The purpose of this research is to develop efficient algorithms based on numerous signal processing approaches for correctly detecting asynchronous cage machine rotor defects (rotor bar ruptures)
Khodja Djalal Eddine. Élaboration d'un système intelligent de surveillance et de diagnostic automatique en temps réel des défaillances des moteurs à induction. Boumerdes, Université M'hamed Bougara. Faculté des hydrocarbures et de la chimie, 2007.
Trajin B. Analysis and processing of electrical quantities for the detection and diagnosis of mechanical faults in asynchronous drives. Application to the monitoring of ball bearings. Institut National Polytechnique de Toulouse-INPT, 2009.
Trigeassou, JC. Diagnostic des machines électriques. Lavoisier. 2011.
Didier G. Modelling and diagnosis of the asynchronous machine in the presence of failures. These doctoral studies from Henri Poincaré University, Nancy-I. 2004.
Ibrahim A. Contribution to the diagnosis of electromechanical machines: Exploitation of electrical signals and instantaneous speed. University Jean Monnet-Saint-Etienne, 2009.
Bonnett AH, Soukup GC. Cause and analysis of stator and rotor failures in three-phase squirrel-cage induction motors. IEEE Transactions on Industry Applications. 1992;28:921-937.
Bonnett AH. Root cause AC motor failure analysis with a focus on shaft failures. IEEE Transactions on Industry Applications. 2000;36:1435-1448.
Andriamalala RN, Razik, H, Baghli L, Sargos, FM. (2008). Eccentricity fault diagnosis of a dual-stator winding induction machine drive considering the slotting effects. IEEE Transactions on Industrial Electronics. 55(12):4238-4251.‏
Baghli L. Contribution to the control of the asynchronous machine, use of fuzzy logic, neural networks and genetic algorithms. University Henri Poincaré-Nancy, 1999.
Sahraoui M. Contribution to the diagnosis of a three-phase asynchronous cage machine. Magister thesis. University of Biskra, 2003.
Razik H, Didier G. On the monitoring of the defects of squirrel cage induction motors. Power Tech Conference Proceedings, 2003 IEEE Bologna, 2003;2.
Medoued A, Lebaroud A, Sayad D. Application of Hilbert transform to fault detection in electric machines. Advances in Difference Equations. 2013.
Rosero J, Ortega J, Urresty J, Cardenas J, Romeral L. Stator short circuits detection in pmsm by means of higher order spectral analysis (hosa). Applied Power Electronics Conference and Exposition, 2009:964-969.
Roy R, Kailath T. ESPRIT-estimation of signal parameters via rotational invariance techniques. IEEE Transactions on acoustics, speech, and signal processing. 1989;37:984-995.
Arezki M. Contribution to the identification of parameters and real-time states of an induction machine for diagnosis and robust control development. University Mustapha Ben Boulaid Batna. 2007.
Cherif H. Detection of stator and rotor faults in asynchronous machine using FFT and wavelet analysis. Mohamed KhiderBiskra University, 2014.
Poloujadoff M, Ivanes M. Comparison of diagrams equivalent to the polyphase asynchronous motor. Revue Générale de l'Electricité. 1967;76.
Bachir S. Contribution to the diagnosis of the asynchronous machine by parametric estimation. Poitiers, 2002.
Samia B. Wavelet and Bayesian methods for diagnosis: Application to asynchronous machines. University Ferhat Abbas-Setif UFAS Algeria, 2011.
Baghli L, Razik H, Rezzoug A, Caironi C, Durantay L, Akdim M. Broken Bars Diagnosis of 3600 Rpm 750 Kw Induction Motor Comparison Modelization and Measurement of Phase Currents. 2004.
Abed A. Contribution to the study and diagnosis of the asynchronous machine. Nancy, 2002.
Bensaoucha S. Contribution au diagnostic de défauts statoriques et rotoriques par l’utilisation des techniques de l’intelligence artificielle-Application aux machines asynchrones à cage. PhD Thesis. University of Amar Telidji Laghouat 2021‏.
Bessam B, Menacer A, Boumehraz M, Cherif M. DWT and Hilbert transform for broken rotor bar fault diagnosis in induction machine at low load. Energy Procedia. 2015;2-15;74:1248-1257.
Bensaoucha, Saddam, et al. A Comparative Study for Broken Rotor Bars Fault Detection in Induction Machine using DWT and MUSIC techniques. 1st International Conference on Communications, Control Systems and Signal Processing (CCSSP). IEEE, 2020.‏
Da Silva AM, Povinelli RJ, Demerdash NA. Induction machine broken bar and stator short-circuit fault diagnostics based on three-phase stator current envelopes. IEEE Transactions on Industrial Electronics. 2008;55:1310-1318.
Guezam A. Diagnosis of rotor faults in induction motor using signal processing. Master, Electromechanical, Amar Telidji Laghouat, Laghouat, 2018.
Ameid T, Menacer A, Talhaoui H, Azzoug Y. Discrete wavelet transform and energy eigen value for rotor bars fault detection in variable speed field-oriented control of induction motor drive. ISA Transactions. 2018;79:217-231.