A Baseline Similarity Attention-Based Dual-Channel Feature Fusion Network for Machine Remaining Useful Life Prediction

Artificial intelligence (AI) methods have significantly improved the accuracy of RUL estimation. However, these methods often neglect issues such as inadequate deep feature mining and loss of critical information during prediction. This study proposes a baseline similarity attention-based dual-channel feature fusion (BSA-DCFF) network for RUL prediction. First, the input data undergo preliminary feature extraction using two independent channels to obtain hidden states at each time step. The feature extraction process for each channel operates independently, ensuring thorough data extraction. Second, the baseline similarity attention (BSA) mechanism is applied independently to both channels. Using feature fusion, it compensates for missing information during the extraction process and enhances degraded features. Finally, the features extracted from the two channels are concatenated and fed into a fully connected subnetwork for RUL prediction. To further improve the accuracy of the potential representation obtained by the BSA structure, additional model optimization is performed by reconstructing the joint loss function. The proposed method is evaluated through an experiment on a publicly available dataset to evaluate its effectiveness.