用机器学习监测电机冷却效率，提升热管理可靠性。
Reliable Thermal Monitoring of Electric Machines through Machine Learning
- 基于实验数据训练三种机器学习模型，评估冷却效率。
- 模型在瞬态工况下仍能准确监测电机状态。
- 适合电机热管理研究者与工业界工程师参考。
电力传动系统的电气化正日益成为实现可持续未来的目标。为确保设备持续可靠运行并避免故障，必须监测电机内部温度并将其控制在安全范围内。传统建模方法复杂且通常需要专业知识。随着数据采集能力的提升，可利用数据驱动模型评估电机热行为。本文研究了人工智能技术在感应电机冷却效率监测中的应用。在特定工况下采集实验数据，开发了三种机器学习模型。通过严格的超参数搜索确定最优配置，并使用多种指标进行评估。三种方案均能在瞬态运行条件下有效监测电机状态，凸显了数据驱动方法在改进热管理方面的潜力。
原文摘要 · Abstract (English)
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.
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