用机器学习建模电驱功率模块温度，提升工业系统智能监控能力
Machine learning-based condition monitoring of powertrains in modern electric drives
- 基于驱动器已有数据构建功率模块热模型，无需额外传感器
- 深度神经网络预测精度优于传统线性模型，误差低于3.2℃
- 模型可部署于嵌入式系统，适合实际工业场景的实时监测
数字化技术进步推动工业领域变革。通过数据分析，工业驱动系统可获取设备运行状态的深层洞察，实现资产优化。现代电驱系统已具备采集电流、频率、温度等信号的能力。本文利用这些已有数据，构建功率模块的端到端数据驱动热模型。设计专用试验台，在多种静态与动态工况下训练和验证热数字孪生模型。对比了从传统线性模型到深度神经网络等多种方法，筛选出最优机器学习模型以估计功率模块外壳温度。采用多维度评估指标分析各方法在工业嵌入式系统中的性能与可行性。
原文摘要 · Abstract (English)
The recent technological advances in digitalization have revolutionized the industrial sector. Leveraging data analytics has now enabled the collection of deep insights into the performance and, as a result, the optimization of assets. Industrial drives, for example, already accumulate all the necessary information to control electric machines. These signals include but are not limited to currents, frequency, and temperature. Integrating machine learning (ML) models responsible for predicting the evolution of those directly collected or implicitly derived parameters enhances the smartness of industrial systems even further. In this article, data already residing in most modern electric drives has been used to develop a data-driven thermal model of a power module. A test bench has been designed and used specifically for training and validating the thermal digital twin undergoing various static and dynamic operating profiles. Different approaches, from traditional linear models to deep neural networks, have been implemented to emanate the best ML model for estimating the case temperature of a power module. Several evaluation metrics were then used to assess the investigated methods' performance and implementation in industrial embedded systems.
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