Digital Twins in Electric Drives: A Review and Proposed Framework

Abstract

Digital twin (DT) technology has become a core enabler of Industry 4.0 by providing a dynamic virtual representation of physical systems for real-time monitoring, simulation optimisation, supported by Internet of Things (IoT) sensing and connectivity. Electric drives (ED) can particularly benefit from DTs because they enable condition monitoring, fault diagnosis remaining useful life (RUL) estimation. The reviewed literature is analysed in terms of application, model type, integration level, strengths limitations. The proposed six-layer framework separates offline commissioning and validation from online synchronisation and decision support. Additionally, an induction motor drive use case is presented to demonstrate how the proposed framework can be applied in practice. The use case comprises a V/f-controlled induction motor supplied by an inverter, together with isolated voltage and current measurements, speed acquisition, signal preprocessing, virtual model synchronisation using an extended Kalman filter (EKF) supervisory control for improving drive operation. The proposed framework provides criteria for designing and validating DTs for electric drive monitoring, fault diagnosis, predictive maintenance supervisory control.