RUL Prediction Based on xLSTM–Transformer Neural Network for Rolling Element Bearings Under Different Working Conditions

Remaining useful life (RUL) prediction of rolling bearings is a crucial issue in intelligent predictive maintenance, thereby ensuring equipment safety and reducing maintenance costs. To address the challenge that traditional deep learning models struggle to simultaneously capture local temporal features and global degradation trends when processing degradation health indicators (HI), this paper proposes a hybrid RUL prediction model based on extended Long Short-Term Memory (xLSTM) and Transformer. The model employs an encoder-decoder architecture, integrating the Multi-Head Attention mechanism with the xLSTM module. This design simultaneously enhances the modeling capability of short-term dynamic features and effectively captures long-term degradation patterns. Validation was conducted on the XJTU-SY and PHM2012 datasets. The proposed model outperformed the comparative models across evaluation metrics such as Root Mean Square Error (RMSE), Coefficient of Determination (R²) and the Score, achieving a significant improvement in prediction accuracy and multi-dataset generalization capability. The proposed network provides a more accurate and generalizable solution for bearing health assessment and remaining useful life prediction and demonstrates significant potential for intelligent health management of industrial equipment.