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Proceedings ArticleDOI

Signature analysis of induction motor mechanical faults by wavelet packet decomposition

Z. Ye, +2 more
- Vol. 2, pp 1022-1029
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TLDR
In this article, wavelet packet decomposition of stator current was used for fault detection and diagnosis in induction motor mechanical faults. But this method can be extended to other fault detection applications.
Abstract
New feature coefficients for induction motor mechanical faults are obtained by wavelet packet decomposition of the stator current. The feature coefficients differentiate the healthy and faulty conditions with obvious difference and can be used for the purpose of stator current based fault detection and diagnosis. This method can be extended to other fault detection applications.

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Citations
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Journal ArticleDOI

Bearing damage detection via wavelet packet decomposition of the stator current

TL;DR: The proposed method enables the analysis of frequency bands that can accommodate the rotational speed dependence of the bearing defect frequencies, and provides a better treatment of nonstationary stator current than currently used Fourier techniques.
Journal ArticleDOI

Online Detection of Broken Rotor Bars in Induction Motors by Wavelet Packet Decomposition and Artificial Neural Networks

TL;DR: The system provides a feature representation of multiple frequency resolutions for faulty modes and accurately differentiates between healthy and faulty conditions, and its main applicability is to dynamic time-variant signals experienced in induction motors during run time.
Journal ArticleDOI

Fault Detection, Diagnosis and Prediction in Electrical Valves Using Self-Organizing Maps

TL;DR: A proactive maintenance scheme for fault detection, diagnosis and prediction in electrical valves, based in self-organizing maps, embedded into an FPGA-based platform, validated with a case study, considering a specific valve used for controlling the oil flow in a distribution network.
Journal ArticleDOI

DWT based bearing fault detection in induction motor using noise cancellation

TL;DR: In this article, an approach to detect the bearing faults experienced by induction machine using motor current signature analysis (MCSA) is presented, where domination of pre fault components is suppressed using noise cancellation by Wiener filter.
Proceedings ArticleDOI

Bearing damage detection via wavelet packet

TL;DR: In this paper, the stator current was analyzed via wavelet packet decomposition to detect beating defects, which enables the analysis of frequency bands that can accommodate the rotational speed dependence of the beating-defect frequencies.
References
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Book

A wavelet tour of signal processing

TL;DR: An introduction to a Transient World and an Approximation Tour of Wavelet Packet and Local Cosine Bases.
Book

Ten lectures on wavelets

TL;DR: This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
Journal ArticleDOI

Ten Lectures on Wavelets

TL;DR: In this article, the regularity of compactly supported wavelets and symmetry of wavelet bases are discussed. But the authors focus on the orthonormal bases of wavelets, rather than the continuous wavelet transform.
Proceedings ArticleDOI

Cause and analysis of stator and rotor failures in three-phase squirrel-cage induction motors

TL;DR: In this article, the authors attempt to identify the various causes of stator and rotor failures in three-phase squirrel cage induction motors, and a specific methodology is proposed to facilitate an accurate analysis of these failures.
Journal ArticleDOI

Noninvasive detection of broken rotor bars in operating induction motors

TL;DR: In this paper, a computer-based noninvasive broken bar fault detector for squirrel-cage rotors of induction motors is presented, which can be applied to existing motors without disassembly or shutdown and has the sensitivity to diagnose the presence of a single broken bar or an open end ring.
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