Thermal Behavior of a Three-Phase Motor with Speed Reducer Under Different Load Levels
DOI:
https://doi.org/10.61236/p02p1f28Keywords:
Three-phase induction motor, thermal behavior, casing temperature, mechanical load, speed reducerAbstract
This study analyzed the thermal behavior of a three-phase induction motor under two operating configurations, one without a speed reducer and the other with a speed reducer, considering a no-load condition and four successive loaded operation stages defined according to reference current levels. Housing temperature was used as the main variable, while line current and rotational speed were used as support variables to interpret the operating condition of the system. The tests lasted 120 min, with measurements recorded every 10 min. The results showed that, in both configurations, temperature increased progressively with operating demand and service time. However, under the adopted experimental criterion, the configuration with the speed reducer exhibited greater thermal severity under all evaluated conditions, including no-load operation. Likewise, the comparison between final current and final temperature indicated that current alone did not explain the thermal difference observed between the two configurations. Overall, the findings show that the thermal assessment of three-phase motors in service should consider not only electrical variables but also the mechanical configuration under which the system operates
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References
[1] Chapman, “MÁQUINAS ELÉCTRICAS,” 2006.
[2] Austin. Hughes and Bill. Drury, Electric motors and drives: fundamentals, types and applications. Elsevier, 2013.
[3] A. E. Fitzgerald, Charles. Kingsley, and S. D. Umans, Electric machinery. McGraw-Hill, 2003.
[4] Gilberto Enríquez Harper, El ABC de las instalaciones eléctricas industriales, Predicción. Mexico: Editorial Limusa, S.A. de C.V., 2001.
[5] C. Kim and K.-S. Lee, Induction Motors - Recent Advances, New Perspectives and Applications. IntechOpen, 2023. doi: 10.5772/intechopen.104031.
[6] S. Madhavan et al., “Thermal management analyses of induction motor through the combination of air-cooling and an integrated water-cooling system,” Sci. Rep., vol. 13, no. 1, Dec. 2023, doi: 10.1038/s41598-023-36989-2.
[7] B. Ge, J. Zhang, and D. Tao, “Thermal analysis of axial-radial hybrid ventilation motor and stator ventilation channel improvement,” IET Electr. Power Appl., vol. 16, no. 11, pp. 1330–1338, Nov. 2022, doi: 10.1049/elp2.12230.
[8] A. Glowacz and Z. Glowacz, “Diagnosis of the three-phase induction motor using thermal imaging,” Infrared Phys. Technol., vol. 81, pp. 7–16, Mar. 2017, doi: 10.1016/j.infrared.2016.12.003.
[9] Anees Mohammed and Sinisa Djurović, “Stator Winding Internal Thermal Monitoring and Analysis Using in-situ FBG Sensing Technology,” IEEE Transactions on Energy Conversion, 2018.
[10] M. O. Sonnaillon, G. Bisheimer, C. De Angelo, and G. O. García, “Online sensorless induction motor temperature monitoring,” IEEE Transactions on Energy Conversion, vol. 25, no. 2, pp. 273–280, Jun. 2010, doi: 10.1109/TEC.2010.2042220.
[11] H. Ashfaq, M. Saood, and M. S. J. Asghar, “A new formulation for minimum input volt-ampere (VA)-slip relationship of three-phase induction motors,” Journal of King Saud University - Engineering Sciences, vol. 29, no. 3, pp. 253–256, Jul. 2017, doi: 10.1016/j.jksues.2015.10.002.
[12] H. Zhang, Q. Wang, L. Chen, J. Zhou, and H. Shao, “Fault Diagnosis of Industrial Motors with Extremely Similar Thermal Images Based on Deep Learning-Related Classification Approaches,” Energy Engineering: Journal of the Association of Energy Engineering, vol. 120, no. 8, pp. 1867–1883, 2023, doi: 10.32604/ee.2023.028453.
[13] J. H. Lee, J. H. Pack, and I. S. Lee, “Fault diagnosis of induction motor using convolutional neural network,” Applied Sciences (Switzerland), vol. 9, no. 15, 2019, doi: 10.3390/app9152950.
[14] H. S. Huey Wee and N. S. Ahmad, “Review and Comparative Analysis of System Identification Methods for Perturbed Motorized Systems,” 2025, Tech Science Press. doi: 10.32604/cmes.2025.063611.
[15] J. Lee and J. I. Ha, “Temperature Estimation of PMSM Using a Difference-Estimating Feedforward Neural Network,” IEEE Access, vol. 8, pp. 130855–130865, 2020, doi: 10.1109/ACCESS.2020.3009503.
[16] Sudarsono, Yuli Purwanto, Anak A. P. Susastriawan, and La Astamu, “An Effect of Zeolite Size on Performance of Dry Scrubber in Tar Removal of Biomass Derived Syngas,” International Journal of Heat and Technology, 2022.
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