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Condition Monitoring of Induction Machines: Quantitative Analysis and Comparison †
In this paper, a diagnostic procedure for rotor bar faults in induction motors is presented, based on the Hilbert and discrete wavelet transforms. The method is compared with other procedures with the same data, which are based on time–frequency analysis, frequency analysis and time domain. The resu...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9866328/ https://www.ncbi.nlm.nih.gov/pubmed/36679843 http://dx.doi.org/10.3390/s23021046 |
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author | Sintoni, Michele Macrelli, Elena Bellini, Alberto Bianchini, Claudio |
author_facet | Sintoni, Michele Macrelli, Elena Bellini, Alberto Bianchini, Claudio |
author_sort | Sintoni, Michele |
collection | PubMed |
description | In this paper, a diagnostic procedure for rotor bar faults in induction motors is presented, based on the Hilbert and discrete wavelet transforms. The method is compared with other procedures with the same data, which are based on time–frequency analysis, frequency analysis and time domain. The results show that this method improves the rotor fault detection in transient conditions. Variable speed drive applications are common in industry. However, traditional condition monitoring methods fail in time-varying conditions or with load oscillations. This method is based on the combined use of the Hilbert and discrete wavelet transforms, which compute the energy in a bandwidth corresponding to the maximum fault signature. Theoretical analysis, numerical simulation and experiments are presented, which confirm the enhanced performance of the proposed method with respect to prior solutions, especially in time-varying conditions. The comparison is based on quantitative analysis that helps in choosing the optimal trade-off between performance and (computational) cost. |
format | Online Article Text |
id | pubmed-9866328 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98663282023-01-22 Condition Monitoring of Induction Machines: Quantitative Analysis and Comparison † Sintoni, Michele Macrelli, Elena Bellini, Alberto Bianchini, Claudio Sensors (Basel) Article In this paper, a diagnostic procedure for rotor bar faults in induction motors is presented, based on the Hilbert and discrete wavelet transforms. The method is compared with other procedures with the same data, which are based on time–frequency analysis, frequency analysis and time domain. The results show that this method improves the rotor fault detection in transient conditions. Variable speed drive applications are common in industry. However, traditional condition monitoring methods fail in time-varying conditions or with load oscillations. This method is based on the combined use of the Hilbert and discrete wavelet transforms, which compute the energy in a bandwidth corresponding to the maximum fault signature. Theoretical analysis, numerical simulation and experiments are presented, which confirm the enhanced performance of the proposed method with respect to prior solutions, especially in time-varying conditions. The comparison is based on quantitative analysis that helps in choosing the optimal trade-off between performance and (computational) cost. MDPI 2023-01-16 /pmc/articles/PMC9866328/ /pubmed/36679843 http://dx.doi.org/10.3390/s23021046 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sintoni, Michele Macrelli, Elena Bellini, Alberto Bianchini, Claudio Condition Monitoring of Induction Machines: Quantitative Analysis and Comparison † |
title | Condition Monitoring of Induction Machines: Quantitative Analysis and Comparison † |
title_full | Condition Monitoring of Induction Machines: Quantitative Analysis and Comparison † |
title_fullStr | Condition Monitoring of Induction Machines: Quantitative Analysis and Comparison † |
title_full_unstemmed | Condition Monitoring of Induction Machines: Quantitative Analysis and Comparison † |
title_short | Condition Monitoring of Induction Machines: Quantitative Analysis and Comparison † |
title_sort | condition monitoring of induction machines: quantitative analysis and comparison † |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9866328/ https://www.ncbi.nlm.nih.gov/pubmed/36679843 http://dx.doi.org/10.3390/s23021046 |
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