Volume 2, Issue 2, June 2009, Pages 158 - 167
Feature Extraction for the Prognosis of Electromechanical Faults in Electrical Machines through the DWT
- J.A. Antonino-Daviu, M. Riera-Guasp, M. Pineda-Sanchez, J. Pons-Llinares, R. Puche-Panadero, J. Perez-Cruz
- Corresponding Author
- J.A. Antonino-Daviu
Available Online 16 June 2009.
- https://doi.org/10.2991/ijcis.2009.2.2.7How to use a DOI?
- electric machines, fault diagnosis, wavelet transform, broken bars, eccentricities.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 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 - https://doi.org/10.2991/ijcis.2009.2.2.7 ID - Antonino-Daviu2009 ER -