# Reliable Thermal Monitoring of Electric Machines through Machine Learning

arXiv: https://arxiv.org/abs/2504.19141 · HTML: https://mlsanity.com/paper/2504.19141/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 27 Apr 2025.

## Builds on (most cited first, 2 of 2)

- [Machine learning-based condition monitoring of powertrains in modern electric drives](https://mlsanity.com/paper/2504.17305/) (2025)
- [Temperature Estimation in Induction Motors using Machine Learning](https://mlsanity.com/paper/2504.18105/) (2025)

## Abstract (arXiv)

The electrification of powertrains is rising as the objective for a more viable future is intensified. To ensure continuous and reliable operation without undesirable malfunctions, it is essential to monitor the internal temperatures of machines and keep them within safe operating limits. Conventional modeling methods can be complex and usually require expert knowledge. With the amount of data collected these days, it is possible to use information models to assess thermal behaviors. This paper investigates artificial intelligence techniques for monitoring the cooling efficiency of induction machines. Experimental data was collected under specific operating conditions, and three machine-learning models have been developed. The optimal configuration for each approach was determined through rigorous hyperparameter searches, and the models were evaluated using a variety of metrics. The three solutions performed well in monitoring the condition of the machine even under transient operation, highlighting the potential of data-driven methods in improving the thermal management.