Digital Twin-Driven Virtual–Real Hybrid Framework Based on Parameter Identification for Bearing Thermal Prediction

Accurate temperature prediction is critical for ensuring mechanical stability and operational safety during complex operating conditions and long-term operation of rolling bearings. This study proposes a digital twin (DT) system for bearing thermal analysis and a digital twin-driven virtual–real hybrid framework, achieving thermal prediction from low-risk behaviors (low-speed/light-load) to high-risk behaviors (high-speed/heavy-load). To address the time-varying and ambiguous parameters, an efficient Nutcracker Optimization Algorithm (NOA)-based identification mechanism is introduced to dynamically calibrate the virtual thermal model, overcoming the limitations of static modeling and data isolation inherent in conventional thermal analysis methods. The Euclidean distance and uncertainty analysis between real temperature and predicted temperature demonstrate the highly reliable predictive ability of the proposed framework in terms of bearing thermal, especially under variable speed conditions. The proposed framework has certain guiding significance for enhancing thermal safety and fault early-warning capabilities of bearings during long-term operation.