Data-Driven Monitoring and Benchmarking of a Permanent Magnet Synchronous Motor Using Digital Twins

Digital twins (DTs) are a critical technology that bridges the gap between physical machinery and real-time manufacturing decisions. DTs provide a viable solution to digital and intelligent manufacturing challenges. They have proven effective in various scenarios, particularly core components in industrial machining applications, electrical machines, and drive systems. As Industry 4.0 advances, DTs are expected to enhance design optimization, fault diagnosis, and coordinated control, significantly strengthening these systems. This study proposes a DT framework for a Permanent Magnet Synchronous Machine (PMSM), a part of a machine tool spindle, to connect isolated machines to an interconnected system and monitor machine conditions in real time. The proposed approach facilitates the design of process monitoring and fault diagnosis methods for PMSM drives used in industrial applications by incorporating disturbances from multiple perspectives. It is asserted that DT modeling is highly comparable to the natural system. Subsequently, for various operating conditions, a simulated faulty condition is induced to demonstrate the real-time state of the PMSM while a bearing fault occurs.