A Digital Twin System for Predictive Maintenance of Complex Equipment

Predictive maintenance of equipment is a very important task in the Industrial Internet and plays an important role in maintaining the safety of industrial production. Digital twins are an important tool and technical basis for predictive maintenance. However, the continuous changing in the operational status of equipment pose a challenge to predictive maintenance based on digital twins, because most methods do not consider the evolution of digital twin systems. Therefore, this paper proposes a digital twin system framework for predictive maintenance of equipment. This framework achieves high-precision and adaptive monitoring of equipment with changing working conditions through highly generalized modeling, AI-based data generation for unknown scene perception, and dynamic evolution of prediction models based on continuous learning. Finally, the future directions of predictive maintenance is outlooked.