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

^*Autor correspondiente de este trabajo

Polytechnic University of Catalonia

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

29 Citas (Scopus)

Resumen

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.

Idioma original	Inglés
Páginas (desde-hasta)	915-924
Número de páginas	10
Publicación	Electric Power Systems Research
Volumen	80
N.º	8
DOI	https://doi.org/10.1016/j.epsr.2009.12.017
Estado	Publicada - ago 2010
Publicado de forma externa	Sí

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@article{1811b553ea544b579d9750339f65d044,

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",

month = aug,

doi = "10.1016/j.epsr.2009.12.017",

language = "English",

volume = "80",

pages = "915--924",

journal = "Electric Power Systems Research",

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publisher = "Elsevier BV",

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TY - JOUR

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

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