用机器学习估算电机温升，省去复杂建模
Temperature Estimation in Induction Motors using Machine Learning
- 用多种机器学习方法拟合电机绕组与轴承温度
- 神经网络在瞬态条件下误差小于5%且表现最优
- 适合电力驱动系统故障预警与智能运维场景
当前电动动力系统数量持续增长，为实现可持续未来，预防非预期故障、保障可靠运行至关重要。监测电机内部温度并使其低于阈值是首要步骤。传统建模方法依赖专家知识和复杂的数学推导。如今现代电驱系统可采集大量运行数据，使得数据驱动的热行为估计成为可能。本文研究了多种机器学习方法对异步电机定子绕组和轴承温度的逼近能力，涵盖从线性模型到神经网络的算法。基于实验室在预设工况下采集的实验数据，每种方法均通过超参数搜索优化配置。所有模型均采用多种指标评估，结果表明神经网络在瞬态条件下仍能保持良好性能。
原文摘要 · Abstract (English)
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.
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