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Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window
The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in the transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (S...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5795768/ https://www.ncbi.nlm.nih.gov/pubmed/29316650 http://dx.doi.org/10.3390/s18010146 |
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author | Burriel-Valencia, Jordi Puche-Panadero, Ruben Martinez-Roman, Javier Sapena-Bano, Angel Pineda-Sanchez, Manuel |
author_facet | Burriel-Valencia, Jordi Puche-Panadero, Ruben Martinez-Roman, Javier Sapena-Bano, Angel Pineda-Sanchez, Manuel |
author_sort | Burriel-Valencia, Jordi |
collection | PubMed |
description | The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in the transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration, the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as the analysis window. In this paper, the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15-MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current’s spectrogram with a significant reduction of the required computational resources. |
format | Online Article Text |
id | pubmed-5795768 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-57957682018-02-13 Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window Burriel-Valencia, Jordi Puche-Panadero, Ruben Martinez-Roman, Javier Sapena-Bano, Angel Pineda-Sanchez, Manuel Sensors (Basel) Article The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in the transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration, the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as the analysis window. In this paper, the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15-MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current’s spectrogram with a significant reduction of the required computational resources. MDPI 2018-01-06 /pmc/articles/PMC5795768/ /pubmed/29316650 http://dx.doi.org/10.3390/s18010146 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Burriel-Valencia, Jordi Puche-Panadero, Ruben Martinez-Roman, Javier Sapena-Bano, Angel Pineda-Sanchez, Manuel Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window |
title | Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window |
title_full | Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window |
title_fullStr | Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window |
title_full_unstemmed | Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window |
title_short | Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window |
title_sort | fault diagnosis of induction machines in a transient regime using current sensors with an optimized slepian window |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5795768/ https://www.ncbi.nlm.nih.gov/pubmed/29316650 http://dx.doi.org/10.3390/s18010146 |
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