Image-Based Approach Applied to Load Torque Estimation in Three-Phase Induction Motors
This paper presents a novel method for load torque estimation in three-phase induction motors using air gap flux measurement and the conversion of this type of time-domain signal into grayscale images for further processing as inputs for an inception-type convolutional neural network. The magnetic f...
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Published in | Sensors (Basel, Switzerland) Vol. 24; no. 8; p. 2614 |
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Main Authors | , |
Format | Journal Article |
Language | English |
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19.04.2024
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Abstract | This paper presents a novel method for load torque estimation in three-phase induction motors using air gap flux measurement and the conversion of this type of time-domain signal into grayscale images for further processing as inputs for an inception-type convolutional neural network. The magnetic flux was measured employing a Hall effect sensor installed inside the machine, near the stator slots, and above the stator windings. In this case, the sensor was able to measure a resultant magnetic flux density, having both rotor and stator magnetic flux contributions. The present methodology does not require motor parameters for torque prediction. The proposed approach successfully estimated load torque using three optimizers across almost the entire motor load operational range, spanning from 1.5% to 93.9% of the rated load. Four model configurations achieved a mean absolute percentage error (MAPE) less than or equal to 3.7%. Specifically, two models for a 40 × 50 pixel image achieved MAPE of 3.7% and 3%, one model for a 40 × 25 pixel image achieved a MAPE of 3.5%, and one model for a 50 × 80 pixel image achieved a MAPE of 3.3%. This research has been experimentally validated with a 7.5 kW squirrel cage induction machine. |
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AbstractList | This paper presents a novel method for load torque estimation in three-phase induction motors using air gap flux measurement and the conversion of this type of time-domain signal into grayscale images for further processing as inputs for an inception-type convolutional neural network. The magnetic flux was measured employing a Hall effect sensor installed inside the machine, near the stator slots, and above the stator windings. In this case, the sensor was able to measure a resultant magnetic flux density, having both rotor and stator magnetic flux contributions. The present methodology does not require motor parameters for torque prediction. The proposed approach successfully estimated load torque using three optimizers across almost the entire motor load operational range, spanning from 1.5% to 93.9% of the rated load. Four model configurations achieved a mean absolute percentage error (MAPE) less than or equal to 3.7%. Specifically, two models for a 40 × 50 pixel image achieved MAPE of 3.7% and 3%, one model for a 40 × 25 pixel image achieved a MAPE of 3.5%, and one model for a 50 × 80 pixel image achieved a MAPE of 3.3%. This research has been experimentally validated with a 7.5 kW squirrel cage induction machine. |
Audience | Academic |
Author | Dias, Cleber Gustavo Fontenele, Jhone |
AuthorAffiliation | Informatics and Knowledge Management Graduate Program, Nove de Julho University—UNINOVE, Sao Paulo 01525-000, Brazil; fontenele.jhone@uni9.edu.br |
AuthorAffiliation_xml | – name: Informatics and Knowledge Management Graduate Program, Nove de Julho University—UNINOVE, Sao Paulo 01525-000, Brazil; fontenele.jhone@uni9.edu.br |
Author_xml | – sequence: 1 givenname: Cleber Gustavo orcidid: 0000-0002-4232-2409 surname: Dias fullname: Dias, Cleber Gustavo organization: Informatics and Knowledge Management Graduate Program, Nove de Julho University-UNINOVE, Sao Paulo 01525-000, Brazil – sequence: 2 givenname: Jhone orcidid: 0009-0009-3744-6499 surname: Fontenele fullname: Fontenele, Jhone organization: Informatics and Knowledge Management Graduate Program, Nove de Julho University-UNINOVE, Sao Paulo 01525-000, Brazil |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38676233$$D View this record in MEDLINE/PubMed |
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Cites_doi | 10.1109/IEMDC.2017.8002304 10.1201/9781439808009 10.1109/LRA.2021.3052392 10.1109/MIE.2020.3016138 10.1109/TIA.2022.3214487 10.1109/ACCESS.2022.3179835 10.1109/TIM.2023.3276530 10.1109/ACCESS.2022.3154481 10.1109/TMAG.2019.2957162 10.1007/s42835-023-01470-7 10.1109/TEC.2018.2865030 10.1016/j.ins.2023.119496 10.1109/TMAG.2019.2956849 10.3390/en16083442 10.1109/TIM.2020.2998301 10.1109/TIE.2018.2866104 10.1609/aaai.v31i1.11231 10.1109/TIA.2021.3112137 10.1109/TIA.2008.2006297 10.1016/j.ymssp.2022.108907 10.1109/TMAG.2022.3179426 10.1109/TIA.2017.2691736 10.1109/TIM.2014.2371192 10.1109/TIA.2021.3110498 10.1109/TIA.2018.2835411 10.1109/JSEN.2018.2827204 10.1109/TIE.2022.3153830 10.1109/CVPR.2016.308 10.1007/s00170-018-2662-x 10.1109/TIA.2019.2963832 10.1109/TMAG.2022.3167254 10.3390/en15114129 10.1088/1361-6501/ad0e57 10.1109/TIA.2022.3174804 10.3390/app9152950 10.1155/2020/2604191 |
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Keywords | image-based condition monitoring torque estimation convolutional neural networks |
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Snippet | This paper presents a novel method for load torque estimation in three-phase induction motors using air gap flux measurement and the conversion of this type of... |
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StartPage | 2614 |
SubjectTerms | Analysis convolutional neural networks Efficiency Failure image-based condition monitoring Induction electric motors Neural networks Regression analysis Sensors torque estimation |
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Title | Image-Based Approach Applied to Load Torque Estimation in Three-Phase Induction Motors |
URI | https://www.ncbi.nlm.nih.gov/pubmed/38676233 https://www.proquest.com/docview/3047054154 https://search.proquest.com/docview/3047946380 https://pubmed.ncbi.nlm.nih.gov/PMC11053641 https://doaj.org/article/925643aa0c6643ec9b546b740f1e824b |
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