August 4, 2025

Real-Time Temperature Distribution Estimation in Permanent Magnet Synchronous Motors (PMSMs) Using Machine Learning: A Path Toward Energy Efficiency and Sustainability

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Authors

OWOmolara Williams ABA. O. Bakare TRT. M. RufaiLagos State University

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Implication

Research demonstrates real-time temperature estimation in permanent magnet synchronous motors, indicating machine learning can enhance energy efficiency and sustainability.

Key Points

The XGBoost Regressor achieved an R² score of 0.9810 in temperature estimation, underscoring its high accuracy and performance.
Advanced supervised machine learning techniques were utilized to estimate real-time temperature distribution, enhancing PMSM diagnostics.
A web application was developed to deploy the model for real-time temperature predictions based on input parameters, improving motor functionality management.
This integration of machine learning with PMSM technology supports energy efficiency and calls for future advancements in sustainable practices.

Cite This Study

Williams et al. (2025) studied this question.

synapsesocial.com/papers/68af2974cf1dd9ea359e2e62 https://doi.org/10.2118/228733-ms

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Implication

Research demonstrates real-time temperature estimation in permanent magnet synchronous motors, indicating machine learning can enhance energy efficiency and sustainability.

Real-Time Temperature Distribution Estimation in Permanent Magnet Synchronous Motors (PMSMs) Using Machine Learning: A Path Toward Energy Efficiency and Sustainability

Discussion

Member takes

Implication

Key Points

Cite This Study

Real-Time Temperature Distribution Estimation in Permanent Magnet Synchronous Motors (PMSMs) Using Machine Learning: A Path Toward Energy Efficiency and Sustainability

Discussion

Member takes

Implication

Key Points

Cite This Study

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