Digital Twin Synthetic Dataset for Bearing Fault Diagnosis in Industrial Spindles

This paper proposes a hybrid digital twin framework that couples a real-time physics-based digital twin model with a data-driven diagnostic layer implemented through cloud-based data acquisition and analysis. This framework generates synthetic datasets across multiple speed levels and fault severities for bearing fault detection and classification in industrial spindle systems, where real fault recordings are costly, risky, and difficult to reproduce. Once the system is validated, a two-stage classifier is trained and used for online fault detection and fault-type identification, whereas the deep-sequence model provides offline verification. To improve robustness, training data are enhanced with multi-domain feature enrichment and targeted data augmentation techniques that simulate measurement noise and small operating variations. The resulting models achieved strong performance under previously unseen operating conditions within the validated digital twin envelope. Overall, the proposed approach reduces the dependence on real fault experiments by enabling the risk-reduced development and evaluation of data-driven bearing fault diagnosis.