Modelling and online training method for digital twin workshop

Aiming at the difficulties in modelling, simulation and verification in digital twin workshop, a modelling and online training method for digital twin workshop is proposed. This paper describes a multi-level digital twin aggregate modelling method, including the status attributes, the static performance attributes and the fluctuation performance attributes, and designs a digital twin organisation system, namely, digital twin graph. According to the data demand for digital twin aggregates, a spatio-temporal data model is constructed. The digital twin model training method using truncated normal distribution is presented. Furthermore, a verification method based on real-virtual error for a digital twin model is proposed. The effectiveness of real-time status monitoring, online model training and simulation for production is verified by a case.

Automated summary

This paper proposes a modelling and online training method for digital twin workshop to address the difficulties in modelling, simulation, and verification. It describes a multi-level digital twin aggregate modelling method and a digital twin organization system. A spatio-temporal data model is constructed based on the data demand for digital twin aggregates. The paper also presents a training method using truncated normal distribution and a verification method based on real-virtual error for digital twin models. The effectiveness of real-time status monitoring, online model training, and production simulation is verified through a case study.