Digital Twins in Electric Drives: A Review and Proposed Framework

Digital Twin (DT) technology has rapidly evolved into a foundational concept within Industry 4.0, offering a dynamic virtual representation of physical systems for real time monitoring, simulation, and optimization. In recent years, it has become a key technology for smart industrial control systems and Internet of Things (IoT) applications. Electric drives particularly benefit from recent advancements in DT technology. This technology enables accurate condition monitoring, fault diagnosis, and estimation of the Remaining Useful Life (RUL) of electric drive systems. This review article explores the concepts, advantages and applications of DT across various industrial domains, with a specific focus on its implementation in electric drive systems. Furthermore, this work proposes a DT framework of the electric drives developed through an extensive analysis of the existing literature. The choice of the modeling approach for a DT in electric drive systems largely depends on its intended application. Here we focus on three directions: (i) evaluation of the current operational state, which can be done using the data generated from the combination of both physical based and data driven models by condition monitoring techniques; (ii) simulation of the future scenarios which can be done using a physics-based modeling approach, it present a significant tool that can generate a huge datasets for fault prediction and diagnosis in real-time scenarios with the support of the Machine Learning technique; and (iii) estimation of the time that the system will continue to operate properly which can be achieved by RUL using the data generated from the data-driven approaches, physicsbased models, or a combination of them. Integrating one or a combination of these models into the proposed DT framework enables targeted applications through real-time data exchange between the physical system and its virtual counterpart.