Applications of wavelets in induction machine fault detection

Research output: Contribution to journalConference article

Abstract

This paper presents a new wavelet-based algorithm for three-phase induction machine fault detection. This new method uses the standard deviation of wavelet coefficients, obtained from n-level decomposition of each phase, to identify single-phasing of supply and unbalanced stator resistance faults in three-phase machines. The proposed algorithm can operate independent of the operational frequency, fault type and loading conditions. Results show that this algorithm has better detection response than the Fourier Transform-based techniques. In addition, a user-friendly graphical interface was designed.

Original languageEnglish (US)
JournalASEE Annual Conference and Exposition, Conference Proceedings
StatePublished - Jan 1 2008
Event2008 ASEE Annual Conference and Exposition - Pittsburg, PA, United States
Duration: Jun 22 2008Jun 24 2008

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Fault detection
Graphical user interfaces
Stators
Fourier transforms
Decomposition

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

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title = "Applications of wavelets in induction machine fault detection",
abstract = "This paper presents a new wavelet-based algorithm for three-phase induction machine fault detection. This new method uses the standard deviation of wavelet coefficients, obtained from n-level decomposition of each phase, to identify single-phasing of supply and unbalanced stator resistance faults in three-phase machines. The proposed algorithm can operate independent of the operational frequency, fault type and loading conditions. Results show that this algorithm has better detection response than the Fourier Transform-based techniques. In addition, a user-friendly graphical interface was designed.",
author = "Erick Schmitt and Peter Idowu and Morales, {Aldo W.}",
year = "2008",
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AU - Schmitt, Erick

AU - Idowu, Peter

AU - Morales, Aldo W.

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N2 - This paper presents a new wavelet-based algorithm for three-phase induction machine fault detection. This new method uses the standard deviation of wavelet coefficients, obtained from n-level decomposition of each phase, to identify single-phasing of supply and unbalanced stator resistance faults in three-phase machines. The proposed algorithm can operate independent of the operational frequency, fault type and loading conditions. Results show that this algorithm has better detection response than the Fourier Transform-based techniques. In addition, a user-friendly graphical interface was designed.

AB - This paper presents a new wavelet-based algorithm for three-phase induction machine fault detection. This new method uses the standard deviation of wavelet coefficients, obtained from n-level decomposition of each phase, to identify single-phasing of supply and unbalanced stator resistance faults in three-phase machines. The proposed algorithm can operate independent of the operational frequency, fault type and loading conditions. Results show that this algorithm has better detection response than the Fourier Transform-based techniques. In addition, a user-friendly graphical interface was designed.

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JO - ASEE Annual Conference and Exposition, Conference Proceedings

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