Reactor System Health Management Based on Deep Digital Twins

Nuclear power, as a quintessentially complex system, is characterized by prolonged operational cycles and high operational costs. The application of intelligent health management can effectively reduce both operational and maintenance costs. This paper combines digital twin technology with generative adversarial networks, sparse denoising autoencoders, and long short-term memory networks to mitigate issues such as high sparsity in sensor data, limited adaptability of purely data-driven algorithms, and challenges faced by physics-driven models in simulating intrinsic system characteristics. Specifically, after establishing the digital twin model, twin data are generated using generative adversarial networks. The health management model was constructed by combining sparse denoising autoencoders and long short-term memory networks. This model encompasses health monitoring, fault diagnosis, and degradation prediction. In this way, the health management model enables full lifecycle health management of nuclear power systems and transforms passive periodic preventive maintenance into proactive predictive maintenance, thus saving maintenance costs.