Diagnosis and detection of short circuit in asynchronous motor using three-phase model

  • Saadi Sakhara
  • Salah SaadEmail author
  • Leila Nacib
Original Article


Several reported works have studied detection and short circuit fault diagnosis in electric machines based on different models. Regarding simplifying assumptions and model conditions of use in relation to stator fault (short circuit), the X change model is proposed because all parameters are computed online. Subsequently, the electrical parameters do not depend on relative position between the stator and the rotor. In this paper, two signal processing techniques are employed for short-circuit diagnosis. The first technique is based on spectral analysis (FFT) using stator current spectral components in healthy and short circuit states at steady state (stationary state). Whereas, the second technique is based on discrete wavelet transform (DWT) considered as an ideal tool because of its signals (non stationary state) analysis ability. Tests are conducted by numerical simulation and the obtained results have showed clearly that, the signatures can be extracted to detect and locate faults.


Diagnosis Unbalance FFT Wavelet transform Asynchronous machine Three-phase model 

List of symbols


Stator resistance matrix


Rotor resistance matrix


Matrix of stator and rotor mutual inductance


Matrix of rotor and stator mutual inductance with [M sr ] = [M rs ] T


Matrix of stator proper inductance \(\left[ {M_{s} } \right] \, = \, [L_{s\sigma } \left] { \, + \, } \right[M_{ss} ]\)


Matrix of rotor proper inductance \(\left[ {M_{r} } \right] \, = \, [L_{r\sigma } \left] { \, + \, } \right[M_{rr} ]\)

\([L_{s\sigma } ]\)

Matrix of stator cyclic inductance


Matrix of mutual inductance between three stator winding

\([L_{r\sigma } ]\)

Matrix of rotor cyclic inductance


Matrix of mutual inductance between three rotor winding

Machine parameters


Output power (1.1 Kw)


Number of pole pairs (2)


Rotor resistance (3.58 Ω)


Stator resistance (10.4 Ω)


Mutual inductance (0.44 H)


Stator leakage inductance of (0.0566 H)


Rotor leakage inductance of (0.017 H)



The authors like to thank the Algerian general direction of research (DGRSDT) for their financial support.


  1. Konan K et al (1988) A new stator model to study induction machine winding short-circuits. ICEM’ 98, Istanbul, Turkey, 3:1516–1521Google Scholar
  2. Henao H et al (1997) A circuit-oriented model of induction machine for diagnostics. IEEE SDEMPED, Carry-le-Rouet, France, pp.185–190Google Scholar
  3. Bachir S, Tnani S, Trigeassou JC, Champenois G (2001) Diagnosis by parameter estimation of stator and rotor faults occurring in induction machines. EPE’01, Graz, Autriche, AoûtGoogle Scholar
  4. Boumegoura T (2001) Recherche de signature électromagnétique des défauts dans une machine asynchrone et synthèse d’observateurs en vue de diagnostic. Thèse Doctorat Ecole centrale de Lyon. MarsGoogle Scholar
  5. Casimir R (2003) Diagnostic des défauts des machines asynchrones par reconnaissance des formes. Thèse de Doctorat, Ecole Centrale de Lyon, FranceGoogle Scholar
  6. Chang X, Cocquempot V, Christophe C (2002) Modélisation de la machine asynchrone en présence de pannes du stator. CEEI-France 23–25Google Scholar
  7. Chang X, Cocquempot V, Christophe C (2003) A model of asynchronous machines for stator fault detection and isolation. IEEE transactions on industrial electronics, 50(3)Google Scholar
  8. ChinmayaKar AR (2006) Mohanty monitoring gear vibrations through motor current signature analysis and wavelet transform. Mech Syst Signal Proc 20:158–187CrossRefGoogle Scholar
  9. Green-Uhp, Razik H (2001) Sur la détection d’un défaut au rotor des moteurs asynchrones. La revue 3EI no 27, Juin 2001Google Scholar
  10. Houdouin G, Barakat G, Dakyo B, Destobbeleer E (2002) A method for the simulation of inter-turn short circuits in squirrel cage induction machines. EPE-PEMC 2002 Dubrovnik, CavtatGoogle Scholar
  11. Kechida R, Menacer A, Talhaoui H (2013) Approach signal for rotor fault detection in induction motors. J Fail Anal Preven 13:346–352CrossRefGoogle Scholar
  12. Nacib L, Saad S, Sakhara S (2014) A comparative study of various methods of gear faults diagnosis. J Fail Anal Prevent 14(5):645–656. doi: 10.1007/s11668-014-9860-0 CrossRefGoogle Scholar
  13. Oumaamar MEK (2012) Surveillance et diagnostic des défauts rotoriques et mécaniques de la machine asynchrone avec alimentation équilibrée ou déséquilibrée. Thèse de doctorat; Université de Lorraine, FranceGoogle Scholar
  14. Razik H (2002) Green-Uhp Le contenu spectral du courant absorbe par la Machine asynchrone en cas de défaillance. Un état de l’art. La revue 3EI (29):48–52Google Scholar
  15. Schaeffer E (1999) Diagnostic des machines asynchrones: modèles et outils paramétriques dédiés à la simulation et à la détection de défauts. Ph.D. thèses, Université de Nantes 6, FranceGoogle Scholar
  16. Shakya P, Darpe AK, Kulkarni MS (2013) Vibration-based fault diagnosis in rolling element bearings: ranking of various time, frequency and time-frequency domain data-based damage identification parameters. Int J Condit Monitor 3(2):53–62CrossRefGoogle Scholar
  17. Soufi Y (2012) Modélisation et diagnostic d’une association convertisseur machine électrique. Thèse deDoctorat en Sciences; Université de Annaba 2012 AlgérieGoogle Scholar
  18. Soufi Y, Bahi T, Harkat MF, Merabet H (2009) Diagnosis and detection of short-circuit in a three-phase induction machine. doi  10.1109/ICCEE.2009.113,2009 IEEE
  19. Talhaoui H, Menacer A, Kessal A, Kechida R (2014) Fast Fourier and discrete wavelet transforms applied to sensorless vector control induction motor for rotor bar faults diagnosis. ISA Transactions, ElsevierGoogle Scholar
  20. Zouzou SE, Sahraoui M, Ghoggal A, Guedidi S (2010) Detection of inter-turn short-circuit and broken rotor bars in induction motors using the partial relative indexes: application on the MCSA. ICEM 2010, Rome, 2010 IEEEGoogle Scholar

Copyright information

© The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2016

Authors and Affiliations

  1. 1.LSELM: Laboratoire Systèmes ElectromécaniquesUniversité de Badji MokhtarAnnabaAlgeria
  2. 2.LAGIS: Laboratoire de Génie Informatique et SignalUniversité Lille 1LilleFrance
  3. 3.Université El Bachir El IbrahimiBordj Bou ArreridjAlgeria

Personalised recommendations