A mixture-of-experts prior–posterior fusion framework for predicting the remaining useful life of aerospace high-speed bearings

Accurate prediction of the Remaining Useful Life (RUL) of aerospace high-speed bearings is critical for optimizing maintenance schedules and ensuring the operational reliability of aero-engines. Despite significant advances, existing methods struggle with early fault detection, the integration of multimodal data, and the interpretability of results. In this study, we propose a hybrid prior–posterior fusion framework designed to address these challenges. The prior phase employs an exponential degradation model, coupled with statistical slope significance testing, to detect early-stage faults with high interpretability. The posterior phase integrates a dual-branch deep learning architecture: the Dynamic Sparse Attention-based Temporal Fusion Transformer (DSA-TFT) and the Interactive Convolutional Block with Adaptive Spectral Branch-enhanced N-BEATS (ICB-ASB-N-BEATS), which are fused using a novel Moirai Mixture-of-Experts (Moirai-MoE) mechanism. This self-correcting framework continuously calibrates predictions based on real-time data, providing both early fault detection and robust long-term RUL predictions. Extensive validation on the IMS and XJTU datasets demonstrates a 12% improvement in prediction accuracy and a RUL error within ±8%, outperforming existing state-of-the-art methods.