Broken Bar Fault Detection Using Taylor-Fourier Filters and Statistical Analysis
Broken rotor bars in induction motors make up one of the typical fault types that are challenging to detect. This type of damage can provoke adverse effects on the motors, such as mechanical and electrical stresses, together with an increase in electricity consumption, causing higher operative costs...
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Published in | Entropy (Basel, Switzerland) Vol. 25; no. 1; p. 44 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
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27.12.2022
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Abstract | Broken rotor bars in induction motors make up one of the typical fault types that are challenging to detect. This type of damage can provoke adverse effects on the motors, such as mechanical and electrical stresses, together with an increase in electricity consumption, causing higher operative costs and losses related to the maintenance times or even the motor replacement if the damage has led to a complete failure. To prevent such situations, diverse signal processing algorithms have been applied to incipient fault detection, using different variables to analyze, such as vibrations, current, or flux. To counteract the broken rotor bar damage, this paper focuses on a motor current signal analysis for early broken bar detection and classification by using the digital Taylor-Fourier transform (DTFT), whose implementation allows fine filtering and amplitude estimation with the final purpose of achieving an incipient fault detection. The detection is based on an analysis of variance followed by a Tukey test of the estimated amplitude. The proposed methodology is implemented in Matlab using the O-splines of the DTFT to reduce the computational load compared with other methods. The analysis is focused on groups of 50-test of current signals corresponding to different damage levels for a motor operating at 50% and 75% of its full load. |
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AbstractList | Broken rotor bars in induction motors make up one of the typical fault types that are challenging to detect. This type of damage can provoke adverse effects on the motors, such as mechanical and electrical stresses, together with an increase in electricity consumption, causing higher operative costs and losses related to the maintenance times or even the motor replacement if the damage has led to a complete failure. To prevent such situations, diverse signal processing algorithms have been applied to incipient fault detection, using different variables to analyze, such as vibrations, current, or flux. To counteract the broken rotor bar damage, this paper focuses on a motor current signal analysis for early broken bar detection and classification by using the digital Taylor–Fourier transform (DTFT), whose implementation allows fine filtering and amplitude estimation with the final purpose of achieving an incipient fault detection. The detection is based on an analysis of variance followed by a Tukey test of the estimated amplitude. The proposed methodology is implemented in Matlab using the O-splines of the DTFT to reduce the computational load compared with other methods. The analysis is focused on groups of 50-test of current signals corresponding to different damage levels for a motor operating at 50% and 75% of its full load. |
Author | Paternina, Mario R A Aguayo-Tapia, Sarahi Rangel-Magdaleno, Jose de Jesus Avalos-Almazan, Gerardo |
AuthorAffiliation | 1 Digital Systems Group, National Institute for Astrophysics, Optics and Electronics, Puebla 72840, Mexico 2 Department of Electrical Engineering, National Autonomous University of Mexico, Mexico City 04510, Mexico |
AuthorAffiliation_xml | – name: 2 Department of Electrical Engineering, National Autonomous University of Mexico, Mexico City 04510, Mexico – name: 1 Digital Systems Group, National Institute for Astrophysics, Optics and Electronics, Puebla 72840, Mexico |
Author_xml | – sequence: 1 givenname: Sarahi surname: Aguayo-Tapia fullname: Aguayo-Tapia, Sarahi organization: Digital Systems Group, National Institute for Astrophysics, Optics and Electronics, Puebla 72840, Mexico – sequence: 2 givenname: Gerardo surname: Avalos-Almazan fullname: Avalos-Almazan, Gerardo organization: Digital Systems Group, National Institute for Astrophysics, Optics and Electronics, Puebla 72840, Mexico – sequence: 3 givenname: Jose de Jesus orcidid: 0000-0003-2785-5060 surname: Rangel-Magdaleno fullname: Rangel-Magdaleno, Jose de Jesus organization: Digital Systems Group, National Institute for Astrophysics, Optics and Electronics, Puebla 72840, Mexico – sequence: 4 givenname: Mario R A surname: Paternina fullname: Paternina, Mario R A organization: Department of Electrical Engineering, National Autonomous University of Mexico, Mexico City 04510, Mexico |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36673185$$D View this record in MEDLINE/PubMed |
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Cites_doi | 10.3390/en14051469 10.1016/j.eswa.2021.116290 10.1109/RPIC53795.2021.9648507 10.1109/TIM.2010.2064690 10.1109/TIA.2019.2905803 10.1109/TIE.2010.2051398 10.1109/TIM.2018.2813820 10.1007/s00180-015-0555-0 10.1109/TIM.2009.2012932 10.3390/s20071884 10.3390/s20133721 10.1109/NAPS.2018.8705105 10.1109/TPWRD.2020.3033755 10.1109/TIM.2014.2373513 10.3390/e24111589 10.1109/ICEEE52452.2021.9415920 10.1007/s11831-020-09446-w 10.1109/TPWRS.2018.2832615 10.1109/TIM.2018.2795895 10.1161/CIRCULATIONAHA.107.654335 10.1109/TCSI.2020.2996976 10.1155/2019/8325218 10.1109/JSEN.2020.2976519 10.1109/MIM.2021.9549228 10.1109/TIA.2019.2958908 10.1109/EUROCON.2019.8861767 |
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Keywords | broken bar stator current statistical analysis induction motor fault detection digital Taylor–Fourier transform |
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Snippet | Broken rotor bars in induction motors make up one of the typical fault types that are challenging to detect. This type of damage can provoke adverse effects on... |
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SubjectTerms | Algorithms Amplitudes broken bar Damage detection digital Taylor–Fourier transform Electricity consumption Fault detection Fourier transforms Full load induction motor Induction motors Neural networks Rotors Signal analysis Signal processing Statistical analysis stator current Variance analysis |
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Title | Broken Bar Fault Detection Using Taylor-Fourier Filters and Statistical Analysis |
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