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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/2/1046 |
| _version_ | 1797437161856827392 |
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| author | Michele Sintoni Elena Macrelli Alberto Bellini Claudio Bianchini |
| author_facet | Michele Sintoni Elena Macrelli Alberto Bellini Claudio Bianchini |
| author_sort | Michele Sintoni |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T11:15:50Z |
| format | Article |
| id | doaj.art-5423690f9ec642da857147c8c20bcebe |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:15:50Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5423690f9ec642da857147c8c20bcebe2023-12-01T00:31:51ZengMDPI AGSensors1424-82202023-01-01232104610.3390/s23021046Condition Monitoring of Induction Machines: Quantitative Analysis and ComparisonMichele Sintoni0Elena Macrelli1Alberto Bellini2Claudio Bianchini3Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi” (DEI), Alma Mater Studiorum, University of Bologna, 47522 Cesena, ItalyDepartment of Electrical, Electronic, and Information Engineering “Guglielmo Marconi” (DEI), Alma Mater Studiorum, University of Bologna, 47522 Cesena, ItalyDepartment of Electrical, Electronic, and Information Engineering “Guglielmo Marconi” (DEI), Alma Mater Studiorum, University of Bologna, 47522 Cesena, ItalyDepartment of Engineering “Enzo Ferrari” (DIEF), University of Modena and Reggio Emilia, 41125 Modena, ItalyIn 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.https://www.mdpi.com/1424-8220/23/2/1046electric machinesfault diagnosiswavelet transforms |
| spellingShingle | Michele Sintoni Elena Macrelli Alberto Bellini Claudio Bianchini Condition Monitoring of Induction Machines: Quantitative Analysis and Comparison Sensors electric machines fault diagnosis wavelet transforms |
| 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 | electric machines fault diagnosis wavelet transforms |
| url | https://www.mdpi.com/1424-8220/23/2/1046 |
| work_keys_str_mv | AT michelesintoni conditionmonitoringofinductionmachinesquantitativeanalysisandcomparison AT elenamacrelli conditionmonitoringofinductionmachinesquantitativeanalysisandcomparison AT albertobellini conditionmonitoringofinductionmachinesquantitativeanalysisandcomparison AT claudiobianchini conditionmonitoringofinductionmachinesquantitativeanalysisandcomparison |