Feature Extraction for the Prognosis of Electromechanical Faults in Electrical Machines through the DWT

DOI

10.2991/ijcis.2009.2.2.7How to use a DOI?

Keywords

electric machines, fault diagnosis, wavelet transform, broken bars, eccentricities.

Abstract

Recognition of characteristic patterns is proposed in this paper in order to diagnose the presence of electromechanical faults in induction electrical machines. Two common faults are considered; broken rotor bars and mixed eccentricities. The presence of these faults leads to the appearance of frequency components following a very characteristic evolution during the startup transient. The identification and extraction of these characteristic patterns through the Discrete Wavelet Transform (DWT) have been proven to be a reliable methodology for diagnosing the presence of these faults, showing certain advantages in comparison with the classical FFT analysis of the steady-state current. In the paper, a compilation of healthy and faulty cases are presented; they confirm the validity of the approach for the correct diagnosis of a wide range of electromechanical faults.

Copyright

© 2009, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - J.A. Antonino-Daviu
AU  - M. Riera-Guasp
AU  - M. Pineda-Sanchez
AU  - J. Pons-Llinares
AU  - R. Puche-Panadero
AU  - J. Perez-Cruz
PY  - 2009
DA  - 2009/06/01
TI  - Feature Extraction for the Prognosis of Electromechanical Faults in Electrical Machines through the DWT
JO  - International Journal of Computational Intelligence Systems
SP  - 158
EP  - 167
VL  - 2
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2009.2.2.7
DO  - 10.2991/ijcis.2009.2.2.7
ID  - Antonino-Daviu2009
ER  -