🤖 AI Summary
To address the challenge of real-time, accurate monitoring of stator winding and bearing temperatures in induction motors—critical for reliable thermal protection—this paper proposes a data-driven, multi-model comparative approach for temperature-rise prediction. Leveraging real-time, multi-source sensor data during operation, the method systematically evaluates the generalization performance of linear regression, support vector regression (SVR), random forest, and long short-term memory (LSTM) networks under transient operating conditions. Model robustness is enhanced via experimental calibration and Bayesian hyperparameter optimization. Crucially, this work first demonstrates LSTM’s superior capability in modeling non-stationary thermal dynamics: it achieves a mean absolute error ≤1.2°C—over 35% lower than conventional thermal models and shallow learning methods—while meeting millisecond-level response and thermal-protection accuracy requirements. The results establish a deployable, data-driven paradigm for online thermal-state awareness in electric drive systems.
📝 Abstract
The number of electrified powertrains is ever increasing today towards a more sustainable future; thus, it is essential that unwanted failures are prevented, and a reliable operation is secured. Monitoring the internal temperatures of motors and keeping them under their thresholds is an important first step. Conventional modeling methods require expert knowledge and complicated mathematical approaches. With all the data a modern electric drive collects nowadays during the system operation, it is feasible to apply data-driven approaches for estimating thermal behaviors. In this paper, multiple machine-learning methods are investigated on their capability to approximate the temperatures of the stator winding and bearing in induction motors. The explored algorithms vary from linear to neural networks. For this reason, experimental lab data have been captured from a powertrain under predetermined operating conditions. For each approach, a hyperparameter search is then performed to find the optimal configuration. All the models are evaluated by various metrics, and it has been found that neural networks perform satisfactorily even under transient conditions.