Machine learning-based condition monitoring of powertrains in modern electric drives
This addresses condition monitoring for industrial electric drives, but appears incremental as it applies existing ML methods to a specific domain problem.
The paper developed a data-driven thermal model for power modules in electric drives using existing sensor data, comparing traditional linear models to deep neural networks to estimate case temperature under various operating conditions.
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.