A Lifelong Learning Method Based on Event-Triggered Online Frozen-EWC Transformer Encoder for Equipment Digital Twin Dynamic Evolution

Digital twin (DT) has become a widely discussed emerging topic in last few years, which is expected to bring a new revolution to the whole life cycle of complex equipment, including Research and Development design, optimization control, and predictive maintenance. Using online real-time sensor data to realize model dynamic evolution, so as to map the state change of physical equipment faithfully, is the most significant feature of DT and is also one of the most important key technologies of DT. Lifelong learning is a feasible way to realize DT dynamic evolution. By using offline historical data and online real-time data, the DT model built in a data-driven modeling manner can evolve continuously, therefore, the accuracy of predictive simulation can be guaranteed and improved. Simultaneously, to make a balance between the computation cost and dynamic evolution performance, we introduce the event-triggered scheme during the online lifelong learning process. To sum up, a lifelong learning method based on event-triggered online Frozen-EWC Transformer Encoder for equipment DT dynamic evolution has been proposed in this article and the real flight data set of quadrotor aircraft is used to verify the effectiveness of the proposed method.