Wavelet and PDD as fault detection techniques

J. Cusido; L. Romeral; J. A. Ortega; A. Garcia; J. R. Riba

doi:10.1016/j.epsr.2009.12.017

Wavelet and PDD as fault detection techniques

J. Cusido^*
, L. Romeral
, J. A. Ortega
, A. Garcia
, J. R. Riba

^*Corresponding author for this work

Polytechnic University of Catalonia

Research output: Contribution to journal › Article › peer-review

29 Citations (Scopus)

Abstract

Motor current signature analysis has been successfully used for fault diagnosis in induction machines. However, thismethoddoes not always achieve good results with variable load torque. This paper proposes a different signal processing method, which combines wavelet and power spectral density techniques giving the power detail density as a fault factor. The method shows good theoretical and experimental results.

Original language	English
Pages (from-to)	915-924
Number of pages	10
Journal	Electric Power Systems Research
Volume	80
Issue number	8
DOIs	https://doi.org/10.1016/j.epsr.2009.12.017
Publication status	Published - Aug 2010
Externally published	Yes

Keywords

Current analysis
Fault detection
Rotating machinery
Signal processing
Wavelet transform

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10.1016/j.epsr.2009.12.017

Cite this

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title = "Wavelet and PDD as fault detection techniques",

abstract = "Motor current signature analysis has been successfully used for fault diagnosis in induction machines. However, thismethoddoes not always achieve good results with variable load torque. This paper proposes a different signal processing method, which combines wavelet and power spectral density techniques giving the power detail density as a fault factor. The method shows good theoretical and experimental results.",

keywords = "Current analysis, Fault detection, Rotating machinery, Signal processing, Wavelet transform",

author = "J. Cusido and L. Romeral and Ortega, \{J. A.\} and A. Garcia and Riba, \{J. R.\}",

year = "2010",

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T1 - Wavelet and PDD as fault detection techniques

AU - Cusido, J.

AU - Romeral, L.

AU - Ortega, J. A.

AU - Garcia, A.

AU - Riba, J. R.

PY - 2010/8

Y1 - 2010/8

N2 - Motor current signature analysis has been successfully used for fault diagnosis in induction machines. However, thismethoddoes not always achieve good results with variable load torque. This paper proposes a different signal processing method, which combines wavelet and power spectral density techniques giving the power detail density as a fault factor. The method shows good theoretical and experimental results.

AB - Motor current signature analysis has been successfully used for fault diagnosis in induction machines. However, thismethoddoes not always achieve good results with variable load torque. This paper proposes a different signal processing method, which combines wavelet and power spectral density techniques giving the power detail density as a fault factor. The method shows good theoretical and experimental results.

KW - Current analysis

KW - Fault detection

KW - Rotating machinery

KW - Signal processing

KW - Wavelet transform

UR - https://www.scopus.com/pages/publications/79951800005

U2 - 10.1016/j.epsr.2009.12.017

DO - 10.1016/j.epsr.2009.12.017

M3 - Article

AN - SCOPUS:79951800005

SN - 0378-7796

VL - 80

SP - 915

EP - 924

JO - Electric Power Systems Research

JF - Electric Power Systems Research

IS - 8

ER -

Wavelet and PDD as fault detection techniques

Abstract

Keywords

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