A Digital Twin Framework for Nuclear Power Equipment Maintenance: Design, Prototyping, and Preliminary Validation [version 2; peer review: 3 approved, 3 approved with reservations] - Qingfeng Xu; Qian Huang; Fengtian Chang; Yifan Zhi; Min Zhang; Guanghui Zhou; Chao Zhang (Digital Twin)
Throughout the lifecycle of Nuclear Power Equipment (NPE), maintaining high-safety maintenance services is essential for optimal operation. Traditional time-based maintenance strategies are limited in NPE contexts due to stringent safety requirements and high costs of complex systems. Specifically, maintenance processes lack transparency, state monitoring relies heavily on manual inspections, and decisions depend passively on individual expertise. Digital Twin (DT) effectively breaks down "information silos," leverages data value, and enables proactive maintenance decision-making for NPE. However, DT application in the nuclear industry is still exploratory, with limited systematic and practical research, especially for critical equipment maintenance. This paper introduces a DT-based intelligent maintenance decision system featuring three key technologies: DT modeling, state monitoring with dynamic early warning, and systematic intelligent decision-making and verification. A DT-based prototype using a cooling water pump case study preliminarily validates the state monitoring model's accuracy. Results indicate that the proposed framework and methods are feasible and hold significant application potential.