Detection of broken bars in three-phase motors by using curve fits and classification algorithms
Since they transform electrical energy into mechanical energy, three-phase induction motors are one of the main assets that companies have. Therefore, good monitoring of their conditions and diagnosing their faults is essential. In this article, we propose a curve fitting technique and classificatio...
- Autores:
-
Hoyos, Gabriel
Puertas, Edwin
Villa, Jose Luis
Martinez-Santos, Juan Carlos
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12176
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12176
- Palabra clave:
- Induction Motors;
Fault Detection;
Stators
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Detection of broken bars in three-phase motors by using curve fits and classification algorithms |
| title |
Detection of broken bars in three-phase motors by using curve fits and classification algorithms |
| spellingShingle |
Detection of broken bars in three-phase motors by using curve fits and classification algorithms Induction Motors; Fault Detection; Stators LEMB |
| title_short |
Detection of broken bars in three-phase motors by using curve fits and classification algorithms |
| title_full |
Detection of broken bars in three-phase motors by using curve fits and classification algorithms |
| title_fullStr |
Detection of broken bars in three-phase motors by using curve fits and classification algorithms |
| title_full_unstemmed |
Detection of broken bars in three-phase motors by using curve fits and classification algorithms |
| title_sort |
Detection of broken bars in three-phase motors by using curve fits and classification algorithms |
| dc.creator.fl_str_mv |
Hoyos, Gabriel Puertas, Edwin Villa, Jose Luis Martinez-Santos, Juan Carlos |
| dc.contributor.author.none.fl_str_mv |
Hoyos, Gabriel Puertas, Edwin Villa, Jose Luis Martinez-Santos, Juan Carlos |
| dc.subject.keywords.spa.fl_str_mv |
Induction Motors; Fault Detection; Stators |
| topic |
Induction Motors; Fault Detection; Stators LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
Since they transform electrical energy into mechanical energy, three-phase induction motors are one of the main assets that companies have. Therefore, good monitoring of their conditions and diagnosing their faults is essential. In this article, we propose a curve fitting technique and classification algorithms for a current motor phase to detect broken bars inside the motor. The data set is in the IEEE database, where the data was acquired, simulating the conditions of healthy and broken bars by varying the load condition. The curve fitting technique gives me essential attributes such as the signal's amplitude, frequency, and phase shift, supported by the Fourier transform, which informs how the signal power is a function of frequency. Furthermore, we extracted attributes to train the classifiers, achieving 85% accuracy in classifying the number of broken bars within the engine. © 2022 IEEE. |
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2022 |
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2022 |
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2023-07-19T21:15:01Z |
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2023-07-19T21:15:01Z |
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2023 |
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Hoyos, G., Puertas, E., Villa, J. L., & Martinez-Santos, J. C. (2022, November). Detection of broken bars in three-phase motors by using curve fits and classification algorithms. In 2022 IEEE ANDESCON (pp. 1-6). IEEE. |
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https://hdl.handle.net/20.500.12585/12176 |
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10.1109/ANDESCON56260.2022.9989583 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Hoyos, G., Puertas, E., Villa, J. L., & Martinez-Santos, J. C. (2022, November). Detection of broken bars in three-phase motors by using curve fits and classification algorithms. In 2022 IEEE ANDESCON (pp. 1-6). IEEE. 10.1109/ANDESCON56260.2022.9989583 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12176 |
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eng |
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eng |
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info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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6 páginas |
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2022 IEEE ANDESCON: Technology and Innovation for Andean Industry, ANDESCON 2022 |
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Hoyos, Gabriel448e8cd3-150f-4443-9281-7efff4caa82fPuertas, Edwin5a1b1566-e112-43dc-8ac7-310ea9af8f05Villa, Jose Luis46e8f721-4d8f-4e19-b0db-2bb2ebedb33dMartinez-Santos, Juan Carlos5c958644-c78d-401d-8ba9-bbd39fe773182023-07-19T21:15:01Z2023-07-19T21:15:01Z20222023Hoyos, G., Puertas, E., Villa, J. L., & Martinez-Santos, J. C. (2022, November). Detection of broken bars in three-phase motors by using curve fits and classification algorithms. In 2022 IEEE ANDESCON (pp. 1-6). IEEE.https://hdl.handle.net/20.500.12585/1217610.1109/ANDESCON56260.2022.9989583Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarSince they transform electrical energy into mechanical energy, three-phase induction motors are one of the main assets that companies have. Therefore, good monitoring of their conditions and diagnosing their faults is essential. In this article, we propose a curve fitting technique and classification algorithms for a current motor phase to detect broken bars inside the motor. The data set is in the IEEE database, where the data was acquired, simulating the conditions of healthy and broken bars by varying the load condition. The curve fitting technique gives me essential attributes such as the signal's amplitude, frequency, and phase shift, supported by the Fourier transform, which informs how the signal power is a function of frequency. Furthermore, we extracted attributes to train the classifiers, achieving 85% accuracy in classifying the number of broken bars within the engine. © 2022 IEEE.6 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf22022 IEEE ANDESCON: Technology and Innovation for Andean Industry, ANDESCON 2022Detection of broken bars in three-phase motors by using curve fits and classification algorithmsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Induction Motors;Fault Detection;StatorsLEMBCartagena de IndiasChicco, D., Tötsch, N., Jurman, G. The matthews correlation coefficient (Mcc) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation (2021) BioData Mining, 14, art. no. 13, pp. 1-22. Cited 245 times. http://www.biodatamining.org/ doi: 10.1186/s13040-021-00244-zMartínez, D.G. Mantenimiento Prescriptivo a Partir de la Predicción de Eventos AnómalosGrandhi, R.T., Prakash, N.K. Machine-Learning Based Fault Diagnosis of Electrical Motors Using Acoustic Signals (2021) Data Intelligence and Cognitive Informatics., pp. 663-671. Cited 6 times. SpringerGundewar, S., Kane, P., Andhare, A. Detection of broken rotor bar fault in an induction motor using convolution neural network (2022) Journal of Advanced Mechanical Design, Systems and Manufacturing, 16 (2), art. no. A2. Cited 3 times. https://www.jstage.jst.go.jp/article/jamdsm/16/2/16_2022jamdsm0020/_article/-char/en doi: 10.1299/jamdsm.2022jamdsm0020Halder, S., Bhat, S., Dora, B.K. Inverse thresholding to spectrogram for the detection of broken rotor bar in induction motor (2022) Measurement: Journal of the International Measurement Confederation, 198, art. no. 111400. Cited 5 times. https://www.journals.elsevier.com/measurement doi: 10.1016/j.measurement.2022.111400Kennedy, S. (2017) RxM: What Is Prescriptive Maintenance, and How Soon Will You Need It?. Cited 3 times.Lee, C.-Y., Zhuo, G.-L. Effective rotor fault diagnosis model using multilayer signal analysis and hybrid genetic binary chicken swarm optimization (2021) Symmetry, 13 (3), art. no. 487. Cited 6 times. https://www.mdpi.com/2073-8994/13/3/487/pdf doi: 10.3390/sym13030487Liu, X., Yan, Y., Hu, K., Zhang, S., Li, H., Zhang, Z., Shi, T. Fault Diagnosis of Rotor Broken Bar in Induction Motor Based on Successive Variational Mode Decomposition (2022) Energies, 15 (3), art. no. 1196. Cited 9 times. https://www.mdpi.com/1996-1073/15/3/1196/pdf doi: 10.3390/en15031196Maciejewski, N.A.R. Deteccão e Diagn Óstico de Defeitos No Regime Transitório de Motores de Inducão Baseado em Sistemas Inteligentes PhD thesis. Universidade de São PauloMoreno-Sandoval, L.G. Assembly of Polarity, Emotion and User Statistics for Detection of Fake Profiles (2020) CLEF (Working Notes).. Cited 3 times.Puertas, E., Martinez-Santos, J.C. (2021) Phonetic Detection for Hate Speech Spreaders on Twitter. Cited 3 times.Puertas, E., Moreno-Sandoval, L.G., Redondo, J., Alvarado-Valencia, J.A., Pomares-Quimbaya, A. Detection of Sociolinguistic Features in Digital Social Networks for the Detection of Communities (Open Access) (2021) Cognitive Computation, 13 (2), pp. 518-537. Cited 4 times. http://www.springer.com/biomed/neuroscience/journal/12559 doi: 10.1007/s12559-021-09818-9Rácz, A., Bajusz, D., Héberger, K. Multi-Level Comparison of Machine Learning Classifiers and Their Performance Metrics (Open Access) (2019) Molecules (Basel, Switzerland), 24 (15). Cited 45 times. doi: 10.3390/molecules24152811Refaeilzadeh, P., Tang, L., Liu, H. Crossvalidation (2009) Encyclopedia of Database Systems, 5, pp. 532-538. Cited 1602 times.Treml, A.E. Experimental database for detecting and diagnosing rotor broken bar in a threephase induction motor (2020) IEEE DataPort. Cited 11 times.Yepez, E.C. (2021) Detección de Barras Rotas en Motores de Inducción Utilizando Análisis de Entropía de la Información en Señales de CorrienteYin, M., Vaughan, J.W., Wallach, H. Understanding the effect of accuracy on trust in machine learning models (2019) Conference on Human Factors in Computing Systems - Proceedings. Cited 183 times. ISBN: 978-145035970-2 doi: 10.1145/3290605.3300509Zhang, P., Du, Y., Habetler, T.G., Lu, B. A survey of condition monitoring and protection methods for medium-voltage induction motors (2011) IEEE Transactions on Industry Applications, 47 (1), art. no. 5620974, pp. 34-46. Cited 550 times. doi: 10.1109/TIA.2010.2090839Zhang, X., Zhang, T., Young, A.A., Li, X. Applications and comparisons of four time series models in epidemiological surveillance data (Open Access) (2014) PLoS ONE, 9 (2), art. no. e88075. 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