Digital twin-driven intelligent maintenance decision-making system and key-enabling technologies for nuclear power equipment

In the life cycle of nuclear power equipment (NPE), the long-term high-safety maintenance services play a vital role in ensuring their optimal operation. However, as the complex system equipment with high-safety requirements and high costs, there are lots of limitations of traditional time-based maintenance strategies for NPE. For example, the maintenance service process is invisible, the condition monitoring is mainly based on manual inspection, and the maintenance decision-making mainly depends on personal experience passively. Digital twins (DT) are an effective way to break the “information isolated island” in the whole life cycle, which can give full play to the value of data to realize the visualization of operation process of NPE. Nevertheless, nowadays, the application of DT in the field of nuclear industry is at the exploration stage, and there is lacking systematic and practical research. Thus, a novel DT-driven intelligent maintenance decision-making system involving three key-enabling technologies is proposed in this paper. Firstly, the DT-driven maintenance service mode is introduced, and its corresponding system framework is built. Then, the key enabling technologies such as DT modeling, condition monitoring and dynamic pre-alarm, and systematic intelligent maintenance decision-making and verification are expounded in detail. Finally, the cooling water pump is regarded as the case to verify the proposed method. The DT prototype system is developed and verified in the novel system, which demonstrates the novel system and the three key-enabling technologies are feasible and practical.