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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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: Springer 2009-06-01
Series:International Journal of Computational Intelligence Systems
Subjects:
Online Access:https://www.atlantis-press.com/article/1871.pdf
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author J.A. Antonino-Daviu
M. Riera-Guasp
M. Pineda-Sanchez
J. Pons-Llinares
R. Puche-Panadero
J. Perez-Cruz
author_facet J.A. Antonino-Daviu
M. Riera-Guasp
M. Pineda-Sanchez
J. Pons-Llinares
R. Puche-Panadero
J. Perez-Cruz
author_sort J.A. Antonino-Daviu
collection DOAJ
description 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.
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spelling doaj.art-f450aff3b98d482d882d62a915b9c0d12022-12-22T01:56:35ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832009-06-012210.2991/ijcis.2009.2.2.7Feature Extraction for the Prognosis of Electromechanical Faults in Electrical Machines through the DWTJ.A. Antonino-DaviuM. Riera-GuaspM. Pineda-SanchezJ. Pons-LlinaresR. Puche-PanaderoJ. Perez-CruzRecognition 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.https://www.atlantis-press.com/article/1871.pdfelectric machinesfault diagnosiswavelet transformbroken barseccentricities.
spellingShingle J.A. Antonino-Daviu
M. Riera-Guasp
M. Pineda-Sanchez
J. Pons-Llinares
R. Puche-Panadero
J. Perez-Cruz
Feature Extraction for the Prognosis of Electromechanical Faults in Electrical Machines through the DWT
International Journal of Computational Intelligence Systems
electric machines
fault diagnosis
wavelet transform
broken bars
eccentricities.
title Feature Extraction for the Prognosis of Electromechanical Faults in Electrical Machines through the DWT
title_full Feature Extraction for the Prognosis of Electromechanical Faults in Electrical Machines through the DWT
title_fullStr Feature Extraction for the Prognosis of Electromechanical Faults in Electrical Machines through the DWT
title_full_unstemmed Feature Extraction for the Prognosis of Electromechanical Faults in Electrical Machines through the DWT
title_short Feature Extraction for the Prognosis of Electromechanical Faults in Electrical Machines through the DWT
title_sort feature extraction for the prognosis of electromechanical faults in electrical machines through the dwt
topic electric machines
fault diagnosis
wavelet transform
broken bars
eccentricities.
url https://www.atlantis-press.com/article/1871.pdf
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