An Optimized Deep Learning Approach for Predicting the Electric Motor Temperature Using IOT Sensors

References

• K. Bingi, B. R. Prusty, A. Kumra, and A. Chawla, “Torque and temperature prediction for permanent magnet synchronous motor using neural networks,” presented at the 2020 3rd Int. Conf. Energy, Power Environ.: Towards Clean Energy Technol., Meghalaya, India, Mar. 2021, pp. 1–6. DOI: 10.1109/ICEPE50861.2021.9404536.
   Google Scholar
• W. Kirchgässner, O. Wallscheid, and J. Böcker, “Estimating electric motor temperatures with deep residual machine learning,” IEEE Trans. Power Electron., vol. 36, no. 7, pp. 7480–7488, 2021. DOI: 10.1109/TPEL.2020.3045596.
   Web of Science ®Google Scholar
• V. Sze, Y. Chen, T. Yang, and J. S. Emer, “Efficient processing of deep neural networks: A tutorial and survey,” Proc. IEEE, vol. 105, no. 12, pp. 2295–2329, Dec. 2017. DOI: 10.1109/JPROC.2017.2761740.
   Web of Science ®Google Scholar
• H. Zhang, M. Dou, and J. Deng, “Loss-minimization strategy of nonsinusoidal back EMF PMSM in multiple synchronous reference frames,” IEEE Trans. Power Electron., vol. 35, no. 8, pp. 8335–8346, Aug. 2020. DOI: 10.1109/TPEL.2019.2961689.
   Web of Science ®Google Scholar
• W. Kirchgässner, O. Wallscheid, and J. Böcker, “Data-driven permanent magnet temperature estimation in synchronous motors with supervised machine learning: A benchmark,” IEEE Trans. Energy Convers., vol. 36, no. 3, pp. 2059–2067, 2021. DOI: 10.1109/TEC.2021.3052546.
   Web of Science ®Google Scholar
• D. Joo, J. Cho, K. Woo, B. Kim, and D. Kim, “Electromagnetic field and thermal linked analysis of interior permanent-magnet synchronous motor for agricultural electric vehicle,” IEEE Trans. Magn., vol. 47, no. 10, pp. 4242–4245, Oct. 2011. DOI: 10.1109/TMAG.2011.2149504.
   Web of Science ®Google Scholar
• D. Fernández et al., “Permanent magnet temperature estimation in PM synchronous motors using low-cost hall effect sensors,” IEEE Trans. Ind. Appl., vol. 53, no. 5, pp. 4515–4525, Sept. 2017. DOI: 10.1109/ECCE.2016.7855349.
   Web of Science ®Google Scholar
• K. G. Suman and A. T. Mathew, “Speed control of permanent magnet synchronous motor drive system using PI, PID, SMC and SMC plus PID controller,” presented at the 2018 Int. Conf. Adv. Comput. Commun. Informatics (ICACCI), Bangalore, India, Sept. 2018, pp. 543–549. DOI: 10.1109/ICACCI.2018.8554788.
   Google Scholar
• S. Sakunthala, R. Kiranmayi, and P. N. Mandadi, “A review on speed control of permanent magnet synchronous motor drive using different control techniques,” presented at the 2018 Int. Conf. Power, Energy, Control Transmission Syst. (ICPECTS), Chennai, India, Feb. 2018, pp. 97–102. DOI: 10.1109/ICPECTS.2018.8521574.
   Google Scholar
• M. Chammas, A. Makhoul, and J. Demerjian, “An efficient data model for energy prediction using wireless sensors,” Comput. Electr. Eng., vol. 76, pp. 249–257, Jun. 2019. DOI: 10.1016/j.compeleceng.2019.04.002.
   Web of Science ®Google Scholar
• S. Sarkar, V. Lodhi, and J. Maiti, “Text-clustering based deep neural network for prediction of occupational accident risk: A case study,” presented at the 2018 Inte. Joint Symp. Artif. Intell. Nat. Language Processing (iSAI-NLP), Pattaya, Thailand, Nov. 2018, pp. 1–6. DOI: 10.1109/iSAI-NLP.2018.8692881.
   Google Scholar
• M. Hedyehzadeh, K. Maghooli, M. MomenGharibvand, and S. Pistorius, “A comparison of the efficiency of using a deep CNN approach with other common regression methods for the prediction of EGFR expression in glioblastoma patients,” J. Digit. Imag., vol. 33, no. 2, pp. 391–398, Dec. 2020. DOI: 10.1007/s10278-019-00290-4.
   PubMed Web of Science ®Google Scholar
• E. H. Houssein, D. Oliva, E. Çelik, M. M. Emam, and R. M. Ghoniem, “Boosted sooty tern optimization algorithm for global optimization and feature selection,” Expert Syst. Appl., vol. 213, pp. 119015, Mar. 2023. DOI: 10.1016/j.eswa.2022.119015.
   Web of Science ®Google Scholar
• E. Çelik, “IEGQO-AOA: Information-Exchanged Gaussian Arithmetic Optimization Algorithm with Quasi-opposition learning,” Knowl.-Based Syst., vol. 260, pp. 110169, Jan. 2023. DOI: 10.1016/j.knosys.2022.110169.
   Web of Science ®Google Scholar