A Novel Swin-Transformer with Multi-Source Information Fusion for Online Cross-Domain Bearing RUL Prediction

Accurate remaining useful life (RUL) prediction of rolling bearings plays a critical role in predictive maintenance. However, existing methods face challenges in extracting and fusing multi-source spatiotemporal features, addressing distribution differences between intra-domain and inter-domain features, and balancing global-local feature attention. To overcome these limitations, this paper proposes an online cross-domain RUL prediction method based on a swin-transformer with multi-source information fusion. The method uses a Bidirectional Long Short-Term Memory (Bi-LSTM) network to capture temporal features, which are transformed into 2D images using Gramian Angular Fields (GAF) for spatial feature extraction by a 2D Convolutional Neural Network (CNN). A self-attention mechanism further integrates multi-source features, while an adversarial Multi-Kernel Maximum Mean Discrepancy (MK-MMD) combined with a relational network mitigates feature distribution differences across domains. Additionally, an offline-online swin-transformer with a dynamic weight updating strategy enhances cross-domain feature learning. Experimental results demonstrate that the proposed method significantly reduces Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), outperforming public methods in prediction accuracy and robustness.