Key Technologies of PHM Digital Twin Modeling and Dynamic Updating for Electromechanical Equipment

As the demand for ultra-high reliability and extended lifespan of modern electromechanical equipment grows, their structures are becoming more complex, and fault coupling phenomena are intensifying, leading to significant economic losses and safety risks. Therefore, research on the reliable operation and health management of electromechanical equipment has become urgent. Digital twin (DT) technology, with its high-precision simulations and real-time updates, offers new solutions for condition monitoring and health management. Addressing the need for stable operation and high reliability in complex electromechanical systems, this paper explores key technologies in DT-based Prognostics and Health Management (PHM), focusing on high-precision modeling and dynamic updating, achieving four major outcomes: 1. A dual closed-loop DT framework is proposed to address the lack of dynamic adaptability in current PHM systems' performance degradation and updating mechanisms. The effectiveness and accuracy of this framework have been validated. 2. A hybrid DT construction and updating method based on a dual-trigger mechanism is introduced to overcome the limitations of physical model errors and data-driven model dependency. Experimental validation demonstrated the method's reliability. 3. A full lifecycle PHM DT multi-model evolution and efficient updating method, based on the Markov process, is proposed, achieving model self-evolution and validated for accuracy and flexibility. 4. A digital platform for constructing DT systems for electromechanical equipment has been developed, with successful deployment and application on a Permanent Magnet Synchronous Motor (PMSM), laying a solid foundation for the future development of digital transformation technologies.