Rolling bearings serve as critical components in rotating machinery, where accurate remaining useful life (RUL) prediction under variable operating conditions presents a fundamental challenge for industrial predictive maintenance. This study proposes a Residual-enhanced Convolutional Transformer that combines strided convolutions with residual connections for local fault feature extraction and a Transformer encoder for long-term degradation modeling. Through systematic ablation studies, optimal hyperparameters were identified to establish an effective training strategy. Evaluation on the XJTU-SY accelerated lifetime test dataset demonstrates consistent performance with R² scores reaching 77% on the validation dataset and 81% on the testing dataset, which outperforms conventional CNN and ResNet model. The model maintains stable prediction accuracy across diverse operating conditions, confirming its robustness and practical applicability for equipment health monitoring systems.

Rolling Bearing,Prognostics Health Management,Remaining Useful Life,Residual-enhanced Convolutional Transformer