Digital Twin Proposal for Induction Motors Using IoT and Finite Elements Method

Induction motors are susceptible to adverse conditions such as mechanical overload, voltage unbalance, mechanical vibrations, and short-circuits, which can lead to overheating and compromise their lifespan. An accurate analysis of thermal behavior is essential to prevent damage and optimize performance, especially under severe conditions. This work proposes a real-time remote thermal sensing technique using digital twins and the Internet of Things. The developed system performs thermal simulations based on the finite element method, executed in parallel with the motor in operation, capturing real-time data on current, voltage, humidity, and ambient temperature to feed the digital model. The system was experimentally validated in the laboratory, showing an average absolute error lower than 3.6% in the worst-case scenario. This technique enables continuous and remote simulation, providing a non-invasive and precise solution to enhance motor performance and prevent failures.