Baza Publikacji Pracowników Politechniki Lubelskiej

Status:
Autorzy:	Czerwiński Dariusz, Gęca Jakub, Kolano Krzysztof
Dyscypliny:
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Rok wydania:	2021
Wersja dokumentu:	Drukowana \| Elektroniczna
Język:	angielski
Numer czasopisma:	14
Wolumen/Tom:	21
Numer artykułu:	4655
Strony:	1 - 17
Impact Factor:	3,847
Web of Science® Times Cited:	21
Scopus® Cytowania:	31
Bazy:	Web of Science \| Scopus
Efekt badań statutowych	NIE
Finansowanie:	This work was supported by Lublin University of Technology grant no. FD-20/EE-2/605 and FD-20/IT-3/999.
Materiał konferencyjny:	NIE
Publikacja OA:	TAK
Licencja:
Sposób udostępnienia:	Witryna wydawcy
Wersja tekstu:	Ostateczna wersja opublikowana
Czas opublikowania:	W momencie opublikowania
Data opublikowania w OA:	7 lipca 2021
Abstrakty:	angielski
	In this article, the authors propose two models for BLDC motor winding temperature estimation using machine learning methods. For the purposes of the research, measurements were made for over 160 h of motor operation, and then, they were preprocessed. The algorithms of linear regression, ElasticNet, stochastic gradient descent regressor, support vector machines, decision trees, and AdaBoost were used for predictive modeling. The ability of the models to generalize was achieved by hyperparameter tuning with the use of cross-validation. The conducted research led to promising results of the winding temperature estimation accuracy. In the case of sensorless temperature prediction (model 1), the mean absolute percentage error MAPE was below 4.5% and the coefficient of determination R2 was above 0.909. In addition, the extension of the model with the temperature measurement on the casing (model 2) allowed reducing the error value to about 1% and increasing R2 to 0.990. The results obtained for the first proposed model show that the overheating protection of the motor can be ensured without direct temperature measurement. In addition, the introduction of a simple casing temperature measurement system allows for an estimation with accuracy suitable for compensating the motor output torque changes related to temperature.

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Machine Learning for Sensorless Temperature Estimation of a BLDC Motor

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