Bearing Fault Diagnosis of a PWM Inverter Fed-Induction Motor Using an Improved Short Time Fourier Transform

  • Mohammed-El-Amine KhodjaEmail author
  • Ameur Fethi Aimer
  • Ahmed Hamida Boudinar
  • Noureddine Benouzza
  • Azeddine Bendiabdellah
Original Article


Induction motor diagnosis using the Power Spectral Density (PSD) estimation based on the Fourier Transform calculation has been widely used as an analysis method for its simplicity and low computation time. However, the use of PSD is not recommended for processing non stationary signals (case of variable speed applications) and therefore the analysis with PSD is not reliable. To overcome this handicap, the Short Time Fourier Transform (STFT) is proposed in this paper; giving additional information on changes of the frequencies over time for stator current signal analysis. Furthermore, the use of a new approach called Maxima’s Location Algorithm is also proposed. This later will be associated with the STFT analysis to show only those harmonics with useful information on existing faults. This approach will be used in the diagnosis of bearing faults of a PWM inverter-fed induction motor operating at variable speed. Several experimental results in the transient state are carried out firstly to validate the results and secondly to illustrate the merits and effectiveness of the combined STFT/MLA proposed approach.


Induction motor Fault diagnosis Time–frequency analysis STFT MLA Bearing faults 


  1. 1.
    Bonnett AH, Yung C (2008) Increased efficiency versus increased reliability. Ind Appl Mag IEEE 14:29–36CrossRefGoogle Scholar
  2. 2.
    Boudinar AH, Bendiabdellah A, Benouzza N, Ferradj M (2014) Improved stator current spectral analysis technique for bearing faults diagnosis. In: 16th International power electronics and motion control conference and exposition, Antalya, Turkey. 21–24 Sep. 2014Google Scholar
  3. 3.
    Immovilli F, Bellini A, Rubini R, Tassoni C (2010) Diagnosis of bearing faults in induction machines by vibration or current signals: a critical comparison. IEEE Trans Ind Appl 46:1350–1360CrossRefGoogle Scholar
  4. 4.
    Garcia-Perez A, Romero-Troncoso R, Cabal-Yepez E, Osornio-Rios RA (2011) The application of high-resolution spectral analysis for identifying multiple combined faults in induction motors. IEEE Trans Ind Electron 58:2002–2011CrossRefGoogle Scholar
  5. 5.
    Concari A, Franceschini G, Tassoni C (2008) Differential diagnosis based on multivariable monitoring to assess induction machine rotor conditions. IEEE Trans Ind Electron 55:4156–4167CrossRefGoogle Scholar
  6. 6.
    Aïmer AF, Boudinar AH, Bendiabdellah A (2013) Use of the short time Fourier transform for induction motor broken bars detection. Int Rev Modell Simul 6(6):1216–1223Google Scholar
  7. 7.
    Jung JH, Lee JJ, Kwon BH (2006) Online diagnosis of induction motors using MCSA. IEEE Trans Ind Electron 53:1842–1853CrossRefGoogle Scholar
  8. 8.
    Mehala N, Dahiya R (2010) Detection of bearing faults of induction motor using Park’s vector approach. Int J Eng Technol 2(4):263–266Google Scholar
  9. 9.
    El Bouchikhi E, Choqueuse V, Benbouzid MEH (2012) Current frequency spectral subtraction and its contribution to induction machines’ bearings condition monitoring. IEEE Trans Energy Convers 28(1):135–144CrossRefGoogle Scholar
  10. 10.
    Gong X, Qiao W (2011) Bearing fault detection for direct-drive wind turbines via stator current spectrum analysis. In: Proceedings of IEEE energy conversion congress and exposition (ECCE)Google Scholar
  11. 11.
    El Bouchikhi EH, Choqueuse V, Benbouzid M (2015) Induction machine faults detection using stator current parametric spectral estimation. Mech Syst Signal Process 52–53:447–464. CrossRefGoogle Scholar
  12. 12.
    Aïmer AF, Boudinar AH, Bendiabdellah A, Mokhtar C (2010) Effet du fenêtrage sur la résolution de la DSP et son apport dans le diagnostic des défauts rotoriques du moteur asynchrone. In: Proceedings of international conference on industrial engineering and manufacturing, Batna, 09–10 May 2010, AlgeriaGoogle Scholar
  13. 13.
    Ibrahim A, Badaoui ME, Guillet F, Bonnardot F (2008) A new bearing fault detection method in induction machines based on instantaneous power factor. IEEE Trans Ind Electron 55(12):4252–4259CrossRefGoogle Scholar
  14. 14.
    Dzwonkowski A, Swędrowski L (2012) Uncertainty analysis of measuring system for instantaneous power research. Metrol Meas Syst 19(3):573–582CrossRefGoogle Scholar
  15. 15.
    Zagirnyak M, Mamchur D, Kalinov A (2012) Comparison of induction motor diagnostic methods based on spectra analysis of current and instantaneous power signals. Przegląd Elektrotechniczny 88(12):221–224Google Scholar
  16. 16.
    Amirat Y, Choqueuse V, Benbouzid MEH, Turri S (2011) Hilbert Transform based bearing failure detection in DFIG-based wind turbines. Int Rev Electr Eng 6(3):1249–1256Google Scholar
  17. 17.
    Espinosa AG, Rosero JA, Cusido J, Romeral L, Ortega JA (2010) Fault detection by means of Hilbert–Huang transform of the stator current in a PMSM with demagnetization. IEEE Trans Energy Convers 25(2):312–318CrossRefGoogle Scholar
  18. 18.
    Aïmer AF, Boudinar AH, Benouzza N, Bendiabdellah A (2015) Simulation and experimental study of induction motor broken rotor bars fault diagnosis using stator current spectrogram. In: Proceedings of IEEE 3rd international conference on control, engineering & information technology (CEIT), Tlemcen, Algeria. 25–27 May 2015Google Scholar
  19. 19.
    El Ahmar E, Choqueuse V, Benbouzid MEH, Amirat Y, El Assad J (2010) Advanced signal processing techniques for fault detection and diagnosis in a wind turbine induction generator drive train: a comparative study. In: Proceedings of IEEE energy conversion congress and exposition (ECCE), September 2010, Atlanta, USA, pp 3576–3581Google Scholar
  20. 20.
    Climente-Alarcon V, Antonino-Daviu JA, Riera-Guasp M, Vlcek M (2014) Induction motor diagnosis by advanced notch FIR filters and the Wigner–Ville distribution. IEEE Trans Ind Electron 61(8):4217–4227CrossRefGoogle Scholar
  21. 21.
    Henao H, Capolino GA, Cabanas MF, Fiippetti F, Bruzzese C, Strangas E, Pusca R, Estima J, Riera-Guasp M, Kia SH (2014) Trends in fault diagnosis for electric machines: a review of diagnostic methods. IEEE Ind Electron Mag 8(2):31–42. CrossRefGoogle Scholar
  22. 22.
    Yan R, Gao RX, Chen X (2014) Wavelets for fault diagnosis of rotary machines: a review with applications. Signal Process 96:1–15CrossRefGoogle Scholar
  23. 23.
    Llinares JP, Antonino-Daviu JA, Riera-Guasp M, Pineda-Sanchez M, Climente-Alarcon V (2011) Induction motor diagnosis based on a transient current analytic wavelet transform via frequency B-Splines. IEEE Trans Ind Electron 58(5):1530–1544CrossRefGoogle Scholar
  24. 24.
    Bouzida A, Touhami O, Ibtiouen R, Belouchrani A, Fadel M, Rezzoug A (2011) Fault diagnosis in industrial induction machines through discrete wavelet transform. IEEE Trans Ind Electron 89(9):4385–4395CrossRefGoogle Scholar
  25. 25.
    Gaeid KS, Ping HW (2011) Wavelet fault diagnosis and tolerant of induction motor: a review. Int J Phys Sci 6(3):358–376Google Scholar
  26. 26.
    Blödt M, Granjon P, Raison B, Rostaing G (2008) Models for bearing damage detection in induction motors using stator current monitoring. IEEE Trans Ind Electron 55:1813–1823CrossRefGoogle Scholar
  27. 27.
    Boudinar AH, Benouzza N, Bendiabdellah A, Khodja MEA (2016) Induction motor bearing fault analysis using a root-MUSIC method. IEEE Trans Ind Appl 52(5):3851–3860CrossRefGoogle Scholar
  28. 28.
    Bendiabdellah A, Boudinar AH, Benouzza N, Khodja M (2015) The enhancements of broken bar fault detection in induction motors. In: Proceedings of International Aegean conference on electrical machines & power electronics (ACEMP), international conference on optimization of electrical & electronic equipment (OPTIM) & international symposium on advanced electromechanical motion systems (ELECTROMOTION), 02–04 Sep. 2015, Side, TurkeyGoogle Scholar

Copyright information

© The Korean Institute of Electrical Engineers 2019

Authors and Affiliations

  • Mohammed-El-Amine Khodja
    • 1
    Email author
  • Ameur Fethi Aimer
    • 1
  • Ahmed Hamida Boudinar
    • 1
  • Noureddine Benouzza
    • 1
  • Azeddine Bendiabdellah
    • 1
  1. 1.Electrical Engineering Faculty, Diagnosis Group, LDEE LaboratoryUniversity of Sciences and Technology of OranBir El DjirAlgeria

Personalised recommendations