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Stefan cel Mare
University of Suceava
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ROMANIA

Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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Reassigned Short Time Fourier Transform and K-means Method for Diagnosis of Broken Rotor Bar Detection in VSD-fed Induction Motors

OJEDA-AGUIRRE, N. A. See more information about OJEDA-AGUIRRE, N. A. on SCOPUS See more information about OJEDA-AGUIRRE, N. A. on IEEExplore See more information about OJEDA-AGUIRRE, N. A. on Web of Science, GARCIA-PEREZ, A. See more information about  GARCIA-PEREZ, A. on SCOPUS See more information about  GARCIA-PEREZ, A. on SCOPUS See more information about GARCIA-PEREZ, A. on Web of Science, ROMERO-TRONCOSO, R. J. See more information about  ROMERO-TRONCOSO, R. J. on SCOPUS See more information about  ROMERO-TRONCOSO, R. J. on SCOPUS See more information about ROMERO-TRONCOSO, R. J. on Web of Science, MORINIGO-SOTELO, D. See more information about  MORINIGO-SOTELO, D. on SCOPUS See more information about  MORINIGO-SOTELO, D. on SCOPUS See more information about MORINIGO-SOTELO, D. on Web of Science, DUQUE-PEREZ, O. See more information about  DUQUE-PEREZ, O. on SCOPUS See more information about  DUQUE-PEREZ, O. on SCOPUS See more information about DUQUE-PEREZ, O. on Web of Science, CAMARENA-MARTINEZ, D. See more information about CAMARENA-MARTINEZ, D. on SCOPUS See more information about CAMARENA-MARTINEZ, D. on SCOPUS See more information about CAMARENA-MARTINEZ, D. on Web of Science
 
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Download PDF pdficon (1,410 KB) | Citation | Downloads: 278 | Views: 669

Author keywords
induction motors, fault diagnosis, rotors, digital signal processing, spectral analysis

References keywords
induction(27), motors(20), detection(15), fault(13), rotor(11), industrial(10), diagnosis(10), broken(10), electronics(9), systems(8)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2019-05-31
Volume 19, Issue 2, Year 2019, On page(s): 61 - 68
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.02008
Web of Science Accession Number: 000475806300008
SCOPUS ID: 85066316496

Abstract
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Over the years induction motors have established an uncanny knack for providing a plethora of utilities in the industry, where the fault monitoring and detection has become necessary. Several techniques could be applied for the monitoring and identification of broken rotor bars when the motor is fed by a variable speed drive (VSD). Nevertheless, many of these methodologies detect this fault and other failures in the steady state condition, but this monitoring grow into more complicated analysis during the startup transient condition owing to the large number of harmonics, which the VSD insert to the current signal. The novelty of the proposed methodology is the application of the reassignment during the startup transient and the steady state conditions to identify one broken rotor bar in the induction motor. The proposed methodology is experimented with both, real and synthetic signals. The problems that Short Time Fourier Transform (STFT), shows for the identification of broken rotor bars are exhibited. The proposed methodology includes an automatic diagnosis (K-means algorithm), where the signal energy is used. The results show that the Reassigned Short Time Fourier Transform (RSTFT) technique and K-means methods are appropriated for the effective monitoring and diagnosis of one broken rotor bar in the induction motor during the startup and steady state conditions of operation.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 2,547 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 71 ACR
SCOPUS® Average Citations per reference: 0

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2020-09-29 01:57 in 237 seconds.




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