Aerotecnica Missili & Spazio

, Volume 93, Issue 3–4, pp 61–67 | Cite as

Brushless Dc Motors Failure Detection Using The Continuous Wavelet Transform

  • V. M. Fico
  • M. A. Martin Prats
  • A. L. Rodríguez Vazquez


Having in mind the growing interest in Electro-Mechanical actuators and the need for a diagnostic tool to make them even more reliable, this paper will be focused on the creation of a method capable of detecting different failures of a Brushless DC Motor, based on the analysis of the frequency spectrum of its stator current. The analysis has been carried out using the Continuous Wavelet Transform (CWT) and its property to preserve signal energy in the transformed domain was used to detect failures which cause some type of asymmetry in the magnetic flux between rotor and stator. Simulations were carried out using the software Matlab/Simulink®. The obtained results show that one of the cited indexes can be used for failures detection and diagnosis purposes with relevant benefits such as a low computational cost, an easy implementation scheme and a high detection power.


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  1. 1.
    A. Bellini, F. Filippetti, C. Tassoni, and G.-A. Capolino. “Advances in diagnostic techniques for induction machines”. IEEE Transactions on Industrial Electronics, 2008. cited By (since 1996) 179.Google Scholar
  2. 2.
    Martin Blodt, Pierre Granjon, Bertrand Raison, and Gilles Rostaing. “Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring”. Industrial Electronics, IEEE Transactions on, 2004.Google Scholar
  3. 3.
    J. Cusidó, L. Romeral, J.A. Ortega, J.A. Rosero, and A.G. Espinosa. “Fault detection in induction machines using power spectral density in wavelet decomposition”. IEEE Transactions on Industrial Electronics, 55(2):633–643, 2008. cited By (since 1996) 94.CrossRefGoogle Scholar
  4. 4.
    Nathalie Delprat, Bernard Escudii, Philippe Guillemain, Richard Kronland-Martinet, Philippe Tchamitchian, and Bruno Torresani. “Asymptotic Wavelet and Gabor Analysis: Extraction of Instantaneous Frequencies”. IEEE Transactions on Information Theory, 1992.Google Scholar
  5. 5.
    M. El Hachemi Benbouzid. “A review of induction motors signature analysis as a medium for faults detection”. Industrial Electronics, IEEE Transactions on, 2000.Google Scholar
  6. 6.
    L. Eren and M.J. Devaney. “Bearing damage detection via wavelet packet decomposition of the stator current”. Instrumentation and Measurement, IEEE Transactions on, 2004.Google Scholar
  7. 7.
    Vito M. Fico. “Application of Continuos Wavelet Transform to Brushless DC motors Failures Detection and Diagnosis”. Master’s thesis, 2012.Google Scholar
  8. 8.
    W. le Roux, R. G. Harley, and T. G. Habetler. “Rotor fault analysis of a permanent magnet synchronous machine”. Proc. 15th Int. Conf. Elect. Mach. (ICEM’02), 2002.Google Scholar
  9. 9.
    S.G. Mallat. “A theory for multiresolution signal decomposition: the wavelet representation”. 11, 1989.Google Scholar
  10. 10.
    P. O’Donnell. “Report on Large Motor Reliability Survey of Industrial and Commercial Installations”. IEEE Transactions on Industry Applications, 2007.Google Scholar
  11. 11.
    Z.K. Peng and F.L. Chu. “Application of the wavelet transform in machine condition monitoring and fault diagnostics: A review with bibliography”. Mechanical Systems and Signal Processing, (2):199–221. cited By (since 1996) 262.Google Scholar
  12. 12.
    Satish Rajagopalan, José M. Aller, José A. Restrepo, Thomas G. Habetler, and Ronald G. Harley. “Detection of Rotor Faults in Brushless DC Motors Operating Under Nonstationary Conditions”. IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2006.Google Scholar
  13. 13.
    M. Riera-Guasp, J.A. Antonino-Daviu, M. Pineda-Sanchez, R. Puche-Panadero, and J. Perez-Cruz. “A general approach for the transient detection of slip-dependent fault components based on the discrete wavelet transform”. IEEE Transactions on Industrial Electronics, 55(12):4167–4180, 2008. cited By (since 1996) 60.CrossRefGoogle Scholar
  14. 14.
    J.A. Rosero, J.A. Ortega, E. Aldabas, and L. Romeral. “Moving towards a more electric aircraft”. Aerospace and Electronic Systems Magazine, IEEE, 2007.Google Scholar
  15. 15.
    J.-R.R. Ruiz, J.A. Rosero, A.G. Espinosa, and L. Romeral. “Detection of Demagnetization Faults in Permanent-Magnet Synchronous Motors Under Nonstationary Conditions”. Magnetics, IEEE Transactions on, 2009.Google Scholar
  16. 16.
    R.R. Schoen, T.G. Habetler, F. Kamran, and R.G. Bartfield. “Motor bearing damage detection using stator current monitoring”. Industry Applications, IEEE Transactions on, 1995.Google Scholar
  17. 17.
    R.R. Schöen, B.K. Lin, T.G. Habetler, J.H. Schlag, and S. Farag. “An unsupervised, on-line system for induction motor fault detection using stator current monitoring”. Industry Applications, IEEE Transactions on, 1995.Google Scholar
  18. 18.
    Jae Hong Suh, Soundar R.T. Kumara, and Shreesh P. Mysore. “Machinery Fault Diagnosis and Prognosis: Application of Advanced Signal Processing Techniques”. Annals of the CIRP, 48/1/1999, 1999.Google Scholar
  19. 19.
    B. Yazici and G.B. Kliman. “An adaptive statistical time-frequency method for detection of broken bars and bearing faults in motors using stator current”. Industry Applications, IEEE Transactions on, 1999.Google Scholar

Copyright information

© AIDAA Associazione Italiana di Aeronautica e Astronautica 2014

Authors and Affiliations

  • V. M. Fico
    • 1
  • M. A. Martin Prats
    • 1
  • A. L. Rodríguez Vazquez
    • 1
  1. 1.Departamento de Ingenieria ElectronicaUniversidad de Sevilla - Escuela Técnica Superior de IngenieríaSpain

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