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

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 frequ...

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Bibliographic Details
Main Authors: J.A. Antonino-Daviu, M. Riera-Guasp, M. Pineda-Sanchez, J. Pons-Llinares, R. Puche-Panadero, J. Perez-Cruz
Format: Article
Language:English
Published: Atlantis Press 2009-06-01
Series:International Journal of Computational Intelligence Systems
Subjects:
Online Access:https://www.atlantis-press.com/article/1871.pdf
Description
Summary: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.
ISSN:1875-6883