Digital twin-augmented graph convolutional network for cross-modal fault diagnosis in rocket segments with limited labeled data

Abstract Rocket segments, as critical structural components determining launch safety and reliability, require robust health monitoring solutions. Current data-driven fault diagnosis methods are constrained by scarce labeled samples and high testing costs, highlighting the urgent need for efficient solutions. Digital twin (DT) technology enables the production of large-scale simulation data, rendering fault diagnosis of rocket modules possible. This study proposes a DT-augmented graph convolutional network framework that combines simulated data and limited realistic data to achieve cross-domain knowledge sharing. The framework comprises three innovative components: (1) a cross-domain graph constructor for building topological relationships among cross-domain data to achieve global knowledge aggregation; (2) a dual-head multi-channel architecture to enhance feature representation to preserve cross-modal information integrity; (3) a contrastive binary loss function for enforcing intra-class feature consistency and inter-class discriminability in model learning. Experiments on a full-scale rocket segment mock-up show that the framework achieves over 93% accuracy under various fixed-frequency conditions, significantly outperforming the baselines. Relevant results confirm that this approach can effectively accomplish diagnostic tasks using limited labeled data, providing an efficient and practical diagnostic solution for complex aerospace structural systems with constrained physical testing conditions.