A novel dual-branch Transformer with gated cross-attention for remaining useful life prediction of bearings

Features from different domains in vibration signals offer valuable insights for remaining useful life (RUL) prediction of bearings. While fusing these features can improve prediction performance, traditional fusion methods lack effective information exchange across domains, limiting adaptive feature fusion. This limitation can lead to information redundancy and hinder the accurate identification of bearing degradation states. To address these challenges, this study introduces a dual-branch Transformer with gated cross-attention (DTGCA), designed to handle and integrate features from different domains for precise RUL prediction. Specifically, one branch processes one-dimensional time-series features from the time and frequency domains, while the other branch uses a residual convolutional GRU (res-ConvGRU) to handle two-dimensional time-frequency image features. The proposed gated cross-attention (GCA) mechanism enables adaptive information exchange between the branches, effectively fusing their information to provide a clearer representation of bearing degradation states. The proposed method is validated on two real run-to-failure datasets. Comprehensive ablation experiments confirm the method's underlying rationality, while detailed comparative experiments with other approaches clearly demonstrate its superiority.